From a92b070b8ae58a28a8cb519dd378941e3a1f26d5 Mon Sep 17 00:00:00 2001
From: am <am>
Date: Thu, 1 Aug 2024 16:46:28 -0700
Subject: [PATCH 1/4] vista2d

Signed-off-by: am <am>
---
 vista2d/README.md                             |   178 +
 vista2d/configs/hyper_parameters.yaml         |   135 +
 vista2d/configs/inference.json                |   133 +
 vista2d/configs/metadata.json                 |    98 +
 vista2d/datalists/cellpose_datalist.json      |  2978 +++
 vista2d/datalists/deepbacs_datalist.json      |   921 +
 vista2d/datalists/group1_datalist.yaml        |    30 +
 vista2d/datalists/kg_nuclei_datalist.json     |  3287 +++
 vista2d/datalists/livecell_A172_datalist.json |  2894 +++
 .../datalists/livecell_BT474_datalist.json    |  3198 +++
 vista2d/datalists/livecell_BV2_datalist.json  |  2884 +++
 vista2d/datalists/livecell_Huh7_datalist.json |  2806 +++
 vista2d/datalists/livecell_MCF7_datalist.json |  3497 +++
 .../datalists/livecell_SHSY5Y_datalist.json   |  3350 +++
 .../datalists/livecell_SKOV3_datalist.json    |  2742 ++
 .../datalists/livecell_SkBr3_datalist.json    |  3350 +++
 vista2d/datalists/nips_data_list.json         |  5206 ++++
 .../datalists/omnipose_bact_f_dataset.json    |  1164 +
 .../datalists/omnipose_bact_p_dataset.json    |  2092 ++
 vista2d/datalists/omnipose_worm_dataset.json  |   606 +
 .../datalists/tissuenet_breast_datalist.json  | 10537 ++++++++
 .../tissuenet_breast_imc_datalist.json        |   900 +
 .../tissuenet_breast_mibi_datalist.json       |  2174 ++
 .../tissuenet_breast_vectra_datalist.json     |  7475 ++++++
 .../tissuenet_gi_codex_datalist.json          |  6964 +++++
 vista2d/datalists/tissuenet_gi_datalist.json  | 20960 ++++++++++++++++
 .../datalists/tissuenet_gi_mibi_datalist.json |  8804 +++++++
 .../datalists/tissuenet_gi_mxif_datalist.json |  5204 ++++
 .../tissuenet_immune_cycif_datalist.json      | 10411 ++++++++
 .../datalists/tissuenet_immune_datalist.json  | 13220 ++++++++++
 .../tissuenet_immune_mibi_datalist.json       |   891 +
 .../tissuenet_immune_vectra_datalist.json     |  1930 ++
 .../tissuenet_lung_cycif_datalist.json        |  8329 ++++++
 .../tissuenet_pancreas_codex_datalist.json    | 16646 ++++++++++++
 .../tissuenet_pancreas_datalist.json          | 17174 +++++++++++++
 .../tissuenet_pancreas_vectra_datalist.json   |   534 +
 .../tissuenet_skin_mibi_datalist.json         |  2466 ++
 vista2d/datalists/yeaz_bf_dataset_list.json   |  1476 ++
 vista2d/datalists/yeaz_dataset_list.json      |  1749 ++
 vista2d/datalists/yeaz_phc_dataset_list.json  |   213 +
 vista2d/docs/data_license.txt                 |   361 +
 .../all_file_downloader.py                    |    73 +
 .../cellpose_agreement.png                    |   Bin 0 -> 239450 bytes
 .../download_preprocessor/cellpose_links.png  |   Bin 0 -> 1014357 bytes
 vista2d/download_preprocessor/data_tree.png   |   Bin 0 -> 26307 bytes
 .../download_preprocessor/generate_json.py    |   988 +
 .../download_preprocessor/kaggle_download.png |   Bin 0 -> 129951 bytes
 .../omnipose_download.png                     |   Bin 0 -> 216499 bytes
 vista2d/download_preprocessor/process_data.py |   398 +
 vista2d/download_preprocessor/readme.md       |    73 +
 .../tissuenet_download.png                    |   Bin 0 -> 569771 bytes
 .../download_preprocessor/tissuenet_login.png |   Bin 0 -> 37886 bytes
 vista2d/download_preprocessor/urls.txt        |    39 +
 vista2d/large_files.yml                       |     3 +
 vista2d/license.code                          |   201 +
 vista2d/license.weights                       |   438 +
 vista2d/scripts/__init__.py                   |     0
 .../cell_distributed_weighted_sampler.py      |   119 +
 vista2d/scripts/cell_sam_wrapper.py           |    70 +
 vista2d/scripts/components.py                 |   309 +
 vista2d/scripts/utils.py                      |    88 +
 vista2d/scripts/workflow.py                   |  1214 +
 vista2d/setup.cfg                             |    17 +
 vista2d/unit_tests/test_vista2d.py            |   123 +
 vista2d/unit_tests/test_vista2d_mgpu.py       |    80 +
 vista2d/unit_tests/utils.py                   |    86 +
 66 files changed, 184286 insertions(+)
 create mode 100644 vista2d/README.md
 create mode 100644 vista2d/configs/hyper_parameters.yaml
 create mode 100644 vista2d/configs/inference.json
 create mode 100644 vista2d/configs/metadata.json
 create mode 100644 vista2d/datalists/cellpose_datalist.json
 create mode 100644 vista2d/datalists/deepbacs_datalist.json
 create mode 100644 vista2d/datalists/group1_datalist.yaml
 create mode 100644 vista2d/datalists/kg_nuclei_datalist.json
 create mode 100644 vista2d/datalists/livecell_A172_datalist.json
 create mode 100644 vista2d/datalists/livecell_BT474_datalist.json
 create mode 100644 vista2d/datalists/livecell_BV2_datalist.json
 create mode 100644 vista2d/datalists/livecell_Huh7_datalist.json
 create mode 100644 vista2d/datalists/livecell_MCF7_datalist.json
 create mode 100644 vista2d/datalists/livecell_SHSY5Y_datalist.json
 create mode 100644 vista2d/datalists/livecell_SKOV3_datalist.json
 create mode 100644 vista2d/datalists/livecell_SkBr3_datalist.json
 create mode 100644 vista2d/datalists/nips_data_list.json
 create mode 100644 vista2d/datalists/omnipose_bact_f_dataset.json
 create mode 100644 vista2d/datalists/omnipose_bact_p_dataset.json
 create mode 100644 vista2d/datalists/omnipose_worm_dataset.json
 create mode 100644 vista2d/datalists/tissuenet_breast_datalist.json
 create mode 100644 vista2d/datalists/tissuenet_breast_imc_datalist.json
 create mode 100644 vista2d/datalists/tissuenet_breast_mibi_datalist.json
 create mode 100644 vista2d/datalists/tissuenet_breast_vectra_datalist.json
 create mode 100644 vista2d/datalists/tissuenet_gi_codex_datalist.json
 create mode 100644 vista2d/datalists/tissuenet_gi_datalist.json
 create mode 100644 vista2d/datalists/tissuenet_gi_mibi_datalist.json
 create mode 100644 vista2d/datalists/tissuenet_gi_mxif_datalist.json
 create mode 100644 vista2d/datalists/tissuenet_immune_cycif_datalist.json
 create mode 100644 vista2d/datalists/tissuenet_immune_datalist.json
 create mode 100644 vista2d/datalists/tissuenet_immune_mibi_datalist.json
 create mode 100644 vista2d/datalists/tissuenet_immune_vectra_datalist.json
 create mode 100644 vista2d/datalists/tissuenet_lung_cycif_datalist.json
 create mode 100644 vista2d/datalists/tissuenet_pancreas_codex_datalist.json
 create mode 100644 vista2d/datalists/tissuenet_pancreas_datalist.json
 create mode 100644 vista2d/datalists/tissuenet_pancreas_vectra_datalist.json
 create mode 100644 vista2d/datalists/tissuenet_skin_mibi_datalist.json
 create mode 100644 vista2d/datalists/yeaz_bf_dataset_list.json
 create mode 100644 vista2d/datalists/yeaz_dataset_list.json
 create mode 100644 vista2d/datalists/yeaz_phc_dataset_list.json
 create mode 100644 vista2d/docs/data_license.txt
 create mode 100644 vista2d/download_preprocessor/all_file_downloader.py
 create mode 100644 vista2d/download_preprocessor/cellpose_agreement.png
 create mode 100644 vista2d/download_preprocessor/cellpose_links.png
 create mode 100644 vista2d/download_preprocessor/data_tree.png
 create mode 100644 vista2d/download_preprocessor/generate_json.py
 create mode 100644 vista2d/download_preprocessor/kaggle_download.png
 create mode 100644 vista2d/download_preprocessor/omnipose_download.png
 create mode 100644 vista2d/download_preprocessor/process_data.py
 create mode 100644 vista2d/download_preprocessor/readme.md
 create mode 100644 vista2d/download_preprocessor/tissuenet_download.png
 create mode 100644 vista2d/download_preprocessor/tissuenet_login.png
 create mode 100644 vista2d/download_preprocessor/urls.txt
 create mode 100644 vista2d/large_files.yml
 create mode 100644 vista2d/license.code
 create mode 100644 vista2d/license.weights
 create mode 100644 vista2d/scripts/__init__.py
 create mode 100644 vista2d/scripts/cell_distributed_weighted_sampler.py
 create mode 100644 vista2d/scripts/cell_sam_wrapper.py
 create mode 100644 vista2d/scripts/components.py
 create mode 100644 vista2d/scripts/utils.py
 create mode 100644 vista2d/scripts/workflow.py
 create mode 100644 vista2d/setup.cfg
 create mode 100644 vista2d/unit_tests/test_vista2d.py
 create mode 100644 vista2d/unit_tests/test_vista2d_mgpu.py
 create mode 100644 vista2d/unit_tests/utils.py

diff --git a/vista2d/README.md b/vista2d/README.md
new file mode 100644
index 0000000..7db2679
--- /dev/null
+++ b/vista2d/README.md
@@ -0,0 +1,178 @@
+<!--
+Copyright (c) MONAI Consortium
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+    http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+## Overview
+
+The **VISTA2D** is a cell segmentation training and inference pipeline for cell imaging [[`Blog`](https://developer.nvidia.com/blog/advancing-cell-segmentation-and-morphology-analysis-with-nvidia-ai-foundation-model-vista-2d/)].
+
+A pretrained model was trained on collection of 15K public microscopy images. The data collection and training can be reproduced following the [tutorial](./download_preprocessor/). Alternatively, the model can be retrained on your own dataset. The pretrained vista2d model achieves good performance on diverse set of cell types, microscopy image modalities, and can be further finetuned if necessary.  The codebase utilizes several components from other great works including [SegmentAnything](https://github.com/facebookresearch/segment-anything)  and [Cellpose](https://www.cellpose.org/), which must be pip installed as dependencies.  Vista2D codebase follows MONAI bundle format and its [specifications](https://docs.monai.io/en/stable/mb_specification.html).
+
+<div align="center"> <img src="https://developer-blogs.nvidia.com/wp-content/uploads/2024/04/magnified-cells-1.png" width="800"/> </div>
+
+
+### Model highlights
+
+- Robust deep learning algorithm based on transformers
+- Generalist model as compared to specialist models
+- Multiple dataset sources and file formats supported
+- Multiple modalities of imaging data collectively supported 
+- Multi-GPU and multinode training support
+
+
+### Generalization performance
+
+Evaluation was performed for the VISTA2D model with multiple public datasets, such as TissueNet, LIVECell, Omnipose, DeepBacs, Cellpose, and [more](./docs/data_license.txt). A total of ~15K annotated cell images were collected to train the generalist VISTA2D model. This ensured broad coverage of many different types of cells, which were acquired by various imaging acquisition types. The benchmark results of the experiment were performed on held-out test sets for each public dataset that were already defined by the dataset contributors. Average precision at an IoU threshold of 0.5 was used for evaluating performance. The benchmark results are reported in comparison with the best numbers found in the literature, in addition to a specialist VISTA2D model trained only on a particular dataset or a subset of data. 
+
+<div align="center"> <img src="https://developer-blogs.nvidia.com/wp-content/uploads/2024/04/vista-2d-model-precision-versus-specialist-model-baseline-performance.png" width="800"/> </div>
+
+
+
+### Install dependencies
+
+```
+pip install monai fire tifffile imagecodecs pillow fastremap
+pip install --no-deps cellpose natsort roifile
+pip install git+https://github.com/facebookresearch/segment-anything.git
+pip install mlflow psutil pynvml #optional for MLFlow support
+```
+
+### Execute training
+```bash
+python -m monai.bundle run_workflow "scripts.workflow.VistaCell" --config_file configs/hyper_parameters.yaml
+```
+
+#### Quick run with a few data points
+```bash
+python -m monai.bundle run_workflow "scripts.workflow.VistaCell" --config_file configs/hyper_parameters.yaml --quick True --train#trainer#max_epochs 3
+```
+
+### Execute multi-GPU training
+```bash
+torchrun --nproc_per_node=gpu -m monai.bundle run_workflow "scripts.workflow.VistaCell" --config_file configs/hyper_parameters.yaml
+```
+
+### Execute validation
+```bash
+python -m monai.bundle run_workflow "scripts.workflow.VistaCell" --config_file configs/hyper_parameters.yaml --pretrained_ckpt_name model.pt --mode eval
+```
+(can append `--quick True` for quick demoing)
+
+### Execute multi-GPU validation
+```bash
+torchrun --nproc_per_node=gpu -m monai.bundle run_workflow "scripts.workflow.VistaCell" --config_file configs/hyper_parameters.yaml --mode eval
+```
+
+### Execute inference
+```bash
+python -m monai.bundle run --config_file configs/inference.json
+```
+
+### Execute multi-GPU inference
+```bash
+torchrun --nproc_per_node=gpu -m monai.bundle run_workflow "scripts.workflow.VistaCell" --config_file configs/hyper_parameters.yaml --mode infer --pretrained_ckpt_name model.pt
+```
+(can append `--quick True` for quick demoing)
+
+
+
+#### Finetune starting from a trained checkpoint
+(we use a smaller learning rate, small number of epochs, and initialize from a checkpoint)
+```bash
+python -m monai.bundle run_workflow "scripts.workflow.VistaCell" --config_file configs/hyper_parameters.yaml --learning_rate=0.001 --train#trainer#max_epochs 20 --pretrained_ckpt_path /path/to/saved/model.pt
+```
+
+
+#### Configuration options
+
+To disable the segmentation writing:
+```
+--postprocessing []
+```
+
+Load a checkpoint for validation or inference (relative path within results directory):
+```
+--pretrained_ckpt_name "model.pt"
+```
+
+Load a checkpoint for validation or inference (absolute path):
+```
+--pretrained_ckpt_path "/path/to/another/location/model.pt"
+```
+
+`--mode eval` or `--mode infer`will use the corresponding configurations from the `validate` or `infer`
+of the `configs/hyper_parameters.yaml`.
+
+By default the generated `model.pt` corresponds to the checkpoint at the best validation score,
+`model_final.pt` is the checkpoint after the latest training epoch.
+
+
+### Development
+
+For development purposes it's possible to run the script directly (without monai bundle calls)
+
+```bash
+python scripts/workflow.py --config_file configs/hyper_parameters.yaml ...
+torchrun --nproc_per_node=gpu -m  scripts/workflow.py --config_file configs/hyper_parameters.yaml  ..
+```
+
+### MLFlow support
+
+Enable MLFlow logging by specifying "mlflow_tracking_uri" (can be local or remote URL).
+
+```bash
+python -m monai.bundle run_workflow "scripts.workflow.VistaCell" --config_file configs/hyper_parameters.yaml --mlflow_tracking_uri=http://127.0.0.1:8080
+```
+
+Optionally use "--mlflow_run_name=.." to specify MLFlow experiment name, and "--mlflow_log_system_metrics=True/False" to enable logging of CPU/GPU resources (requires pip install psutil pynvml)
+
+
+
+### Unit tests
+
+Test single GPU training:
+```
+python unit_tests/test_vista2d.py
+```
+
+Test multi-GPU training (may need to uncomment the `"--standalone"` in the `unit_tests/utils.py` file):
+```
+python unit_tests/test_vista2d_mgpu.py
+```
+
+## Compute Requirements
+Min GPU memory requirements 16Gb.
+
+
+## Contributing
+Vista2D codebase follows MONAI bundle format and its [specifications](https://docs.monai.io/en/stable/mb_specification.html).
+Make sure to run pre-commit before committing code changes to git
+```bash
+pip install pre-commit
+python3 -m pre_commit run --all-files
+```
+
+
+## Community
+
+Join the conversation on Twitter [@ProjectMONAI](https://twitter.com/ProjectMONAI) or join
+our [Slack channel](https://projectmonai.slack.com/archives/C031QRE0M1C).
+
+Ask and answer questions on [MONAI VISTA's GitHub discussions tab](https://github.com/Project-MONAI/VISTA/discussions).
+
+## License
+
+The codebase is under Apache 2.0 Licence. The model weight is released under CC-BY-NC-SA-4.0. For various public data licenses please see [data_license.txt](./docs/data_license.txt).
+
+## Acknowledgement
+- [segment-anything](https://github.com/facebookresearch/segment-anything)
+- [Cellpose](https://www.cellpose.org/)
\ No newline at end of file
diff --git a/vista2d/configs/hyper_parameters.yaml b/vista2d/configs/hyper_parameters.yaml
new file mode 100644
index 0000000..9f6e8b7
--- /dev/null
+++ b/vista2d/configs/hyper_parameters.yaml
@@ -0,0 +1,135 @@
+imports:
+  - $import os
+
+# seed: 28022024 # uncommend for deterministic results (but slower)
+seed: null
+
+bundle_root: "."
+ckpt_path: $os.path.join(@bundle_root, "models")  # location to save checkpoints
+output_dir: $os.path.join(@bundle_root, "eval")  # location to save events and logs
+log_output_file: $os.path.join(@output_dir, "vista_cell.log")
+
+mlflow_tracking_uri: null                     # enable mlflow logging, e.g. $@ckpt_path + '/mlruns/ or "http://127.0.0.1:8080" or a remote url
+mlflow_log_system_metrics: true               # log system metrics to mlflow (requires: pip install psutil pynvml)
+mlflow_run_name: null                         # optional name of the current run
+
+ckpt_save: true                               # save checkpoints periodically
+amp: true
+amp_dtype: "float16" #float16 or bfloat16 (Ampere or newer)
+channels_last: true
+compile: false # complie the model for faster processing
+
+start_epoch: 0
+run_final_testing: true
+use_weighted_sampler: false                   # only applicable when using several dataset jsons for data_list_files
+
+pretrained_ckpt_name: null
+pretrained_ckpt_path: null
+
+# for commandline setting of a single dataset
+datalist: datalists/tissuenet_skin_mibi_datalist.json
+basedir: /data/tissuenet
+data_list_files:
+  - {datalist: "@datalist", basedir: "@basedir"}
+
+
+fold: 0
+learning_rate: 0.01  # try 1.0e-4 if using AdamW
+quick: false  # whether to use a small subset of data for quick testing
+roi_size: [256, 256]
+
+train:
+  skip: false
+  handlers: []
+  trainer:
+    num_warmup_epochs: 3
+    max_epochs: 200
+    num_epochs_per_saving: 1
+    num_epochs_per_validation: null
+  num_workers: 4
+  batch_size: 1
+  dataset:
+    preprocessing:
+      roi_size: "@roi_size"
+    data:
+      key: null # set to 'testing' to use this subset in periodic validations, instead of the the validation set
+      data_list_files: "@data_list_files"
+
+dataset:
+  data:
+    key: "testing"
+    data_list_files: "@data_list_files"
+
+validate:
+  grouping: true
+  evaluator:
+    postprocessing: "@postprocessing"
+  dataset:
+    data: "@dataset#data"
+  batch_size: 1
+  num_workers: 4
+  preprocessing: null
+  postprocessing: null
+  inferer: null
+  handlers: null
+  key_metric: null
+
+infer:
+  evaluator:
+    postprocessing: "@postprocessing"
+  dataset:
+    data: "@dataset#data"
+
+
+device: "$torch.device(('cuda:' + os.environ.get('LOCAL_RANK', '0')) if torch.cuda.is_available() else 'cpu')"
+network_def:
+  _target_: scripts.cell_sam_wrapper.CellSamWrapper
+  checkpoint: $os.path.join(@ckpt_path, "sam_vit_b_01ec64.pth")
+network: $@network_def.to(@device)
+
+loss_function:
+  _target_: scripts.components.CellLoss
+
+key_metric:
+  _target_: scripts.components.CellAcc
+
+# optimizer:
+#   _target_: torch.optim.AdamW
+#   params: $@network.parameters()
+#   lr: "@learning_rate"
+#   weight_decay: 1.0e-5
+
+optimizer:
+  _target_: torch.optim.SGD
+  params: $@network.parameters()
+  momentum: 0.9
+  lr: "@learning_rate"
+  weight_decay: 1.0e-5
+
+lr_scheduler:
+  _target_: monai.optimizers.lr_scheduler.WarmupCosineSchedule
+  optimizer: "@optimizer"
+  warmup_steps: "@train#trainer#num_warmup_epochs"
+  warmup_multiplier: 0.1
+  t_total: "@train#trainer#max_epochs"
+
+inferer:
+  sliding_inferer:
+    _target_: monai.inferers.SlidingWindowInfererAdapt
+    roi_size: "@roi_size"
+    sw_batch_size: 1
+    overlap: 0.625
+    mode: "gaussian"
+    cache_roi_weight_map: true
+    progress: false
+
+image_saver:
+  _target_: scripts.components.SaveTiffd
+  keys: "seg"
+  output_dir: "@output_dir"
+  nested_folder: false
+
+postprocessing:
+  _target_: monai.transforms.Compose
+  transforms:
+    - "@image_saver"
diff --git a/vista2d/configs/inference.json b/vista2d/configs/inference.json
new file mode 100644
index 0000000..cf5bfa8
--- /dev/null
+++ b/vista2d/configs/inference.json
@@ -0,0 +1,133 @@
+{
+    "imports": [
+        "$import numpy as np"
+    ],
+    "bundle_root": ".",
+    "ckpt_dir": "$@bundle_root + '/models'",
+    "output_dir": "$@bundle_root + '/eval'",
+    "output_ext": ".tif",
+    "output_postfix": "trans",
+    "roi_size": [
+        256,
+        256
+    ],
+    "input_dict": "${'image': '/cellpose_dataset/test/001_img.png'}",
+    "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+    "sam_ckpt_path": "$@ckpt_dir + '/sam_vit_b_01ec64.pth'",
+    "pretrained_ckpt_path": "$@ckpt_dir + '/model.pt'",
+    "image_key": "image",
+    "channels_last": true,
+    "use_amp": true,
+    "amp_dtype": "$torch.float",
+    "network_def": {
+        "_target_": "scripts.cell_sam_wrapper.CellSamWrapper",
+        "checkpoint": "@sam_ckpt_path"
+    },
+    "network": "$@network_def.to(@device)",
+    "preprocessing_transforms": [
+        {
+            "_target_": "scripts.components.LoadTiffd",
+            "keys": "@image_key"
+        },
+        {
+            "_target_": "EnsureTyped",
+            "keys": "@image_key",
+            "data_type": "tensor",
+            "dtype": "$torch.float"
+        },
+        {
+            "_target_": "ScaleIntensityd",
+            "keys": "@image_key",
+            "minv": 0,
+            "maxv": 1,
+            "channel_wise": true
+        },
+        {
+            "_target_": "ScaleIntensityRangePercentilesd",
+            "keys": "image",
+            "lower": 1,
+            "upper": 99,
+            "b_min": 0.0,
+            "b_max": 1.0,
+            "channel_wise": true,
+            "clip": true
+        }
+    ],
+    "preprocessing": {
+        "_target_": "Compose",
+        "transforms": "$@preprocessing_transforms "
+    },
+    "dataset": {
+        "_target_": "Dataset",
+        "data": "$[@input_dict]",
+        "transform": "@preprocessing"
+    },
+    "dataloader": {
+        "_target_": "ThreadDataLoader",
+        "dataset": "@dataset",
+        "batch_size": 1,
+        "shuffle": false,
+        "num_workers": 0
+    },
+    "inferer": {
+        "_target_": "SlidingWindowInfererAdapt",
+        "roi_size": "@roi_size",
+        "sw_batch_size": 1,
+        "overlap": 0.625,
+        "mode": "gaussian",
+        "cache_roi_weight_map": true,
+        "progress": false
+    },
+    "postprocessing": {
+        "_target_": "Compose",
+        "transforms": [
+            {
+                "_target_": "ToDeviced",
+                "keys": "pred",
+                "device": "cpu"
+            },
+            {
+                "_target_": "scripts.components.LogitsToLabelsd",
+                "keys": "pred"
+            },
+            {
+                "_target_": "scripts.components.SaveTiffExd",
+                "keys": "pred",
+                "output_dir": "@output_dir",
+                "output_ext": "@output_ext",
+                "output_postfix": "@output_postfix"
+            }
+        ]
+    },
+    "handlers": [
+        {
+            "_target_": "StatsHandler",
+            "iteration_log": false
+        }
+    ],
+    "checkpointloader": {
+        "_target_": "CheckpointLoader",
+        "load_path": "@pretrained_ckpt_path",
+        "map_location": "cpu",
+        "load_dict": {
+            "state_dict": "@network"
+        }
+    },
+    "evaluator": {
+        "_target_": "SupervisedEvaluator",
+        "device": "@device",
+        "val_data_loader": "@dataloader",
+        "network": "@network",
+        "inferer": "@inferer",
+        "postprocessing": "@postprocessing",
+        "val_handlers": "@handlers",
+        "amp": true
+    },
+    "initialize": [
+        "$monai.utils.set_determinism(seed=123)",
+        "$@checkpointloader(@evaluator)"
+    ],
+    "run": [
+        "$@evaluator.run()"
+    ]
+}
diff --git a/vista2d/configs/metadata.json b/vista2d/configs/metadata.json
new file mode 100644
index 0000000..6729358
--- /dev/null
+++ b/vista2d/configs/metadata.json
@@ -0,0 +1,98 @@
+{
+    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
+    "version": "0.2.0",
+    "changelog": {
+        "0.2.0": "support bundle inference json",
+        "0.1.9": "finetuning checkpoint loading update",
+        "0.1.8": "added manfests",
+        "0.1.7": "prescaling to 0..1",
+        "0.1.6": "data processing script instead of json generation, data licenses",
+        "0.1.5": "improvements and mlflow support",
+        "0.1.4": "add unit tests and use monai 1.3.1",
+        "0.1.3": "update default optimizer",
+        "0.1.2": "use_weighted_sampler defaults to false",
+        "0.1.1": "remove unused code",
+        "0.1.0": "update evaluator",
+        "0.0.9": "do workflow_type and dataset data changes",
+        "0.0.8": "let pretrained_ckpt_name refer to ckpt_path",
+        "0.0.7": "update workflow input arg",
+        "0.0.6": "update the transforms and model, partly verified the reimplementation",
+        "0.0.5": "add inference logic and rename the entry point class from train.py to workflow.py",
+        "0.0.4": "update loadimage and weighted sampler",
+        "0.0.3": "fix final val error if override datalist",
+        "0.0.2": "fix attr error",
+        "0.0.1": "initialize the model package structure"
+    },
+    "monai_version": "1.3.1",
+    "pytorch_version": "2.2.2",
+    "numpy_version": "1.24.4",
+    "einops": "0.7.0",
+    "optional_packages_version": {
+        "scikit-image": "0.22.0",
+        "fastremap": "1.14.1",
+        "cucim-cu12": "24.2.0",
+        "gdown": "5.1.0",
+        "fire": "0.6.0",
+        "pyyaml": "6.0.1",
+        "tensorboard": "2.16.2",
+        "opencv-python": "4.7.0.68",
+        "numba": "0.59.0",
+        "torchvision": "0.17.2",
+        "cellpose": "3.0.8",
+        "segment_anything": "1.0",
+        "mlflow": "2.13.1",
+        "pynvml": "11.5.0"
+    },
+    "name": "VISTA-Cell",
+    "task": "cell image segmentation",
+    "description": "VISTA2D bundle for cell image analysis",
+    "authors": "MONAI team",
+    "copyright": "Copyright (c) MONAI Consortium",
+    "data_type": "tiff",
+    "image_classes": "1 channel data, intensity scaled to [0, 1]",
+    "label_classes": "3-channel data",
+    "pred_classes": "3 channels",
+    "eval_metrics": {
+        "mean_dice": 0.0
+    },
+    "intended_use": "This is an example, not to be used for diagnostic purposes",
+    "references": [],
+    "network_data_format": {
+        "inputs": {
+            "image": {
+                "type": "image",
+                "num_channels": 3,
+                "spatial_shape": [
+                    256,
+                    256
+                ],
+                "format": "RGB",
+                "value_range": [
+                    0,
+                    255
+                ],
+                "dtype": "float32",
+                "is_patch_data": true,
+                "channel_def": {
+                    "0": "image"
+                }
+            }
+        },
+        "outputs": {
+            "pred": {
+                "type": "image",
+                "format": "segmentation",
+                "num_channels": 3,
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "spatial_shape": [
+                    256,
+                    256
+                ]
+            }
+        }
+    }
+}
diff --git a/vista2d/datalists/cellpose_datalist.json b/vista2d/datalists/cellpose_datalist.json
new file mode 100644
index 0000000..ce0707f
--- /dev/null
+++ b/vista2d/datalists/cellpose_datalist.json
@@ -0,0 +1,2978 @@
+{
+    "training": [
+        {
+            "image": "train/128_img.png",
+            "label": "train/128_masks.png",
+            "fold": 0
+        },
+        {
+            "image": "train/136_img.png",
+            "label": "train/136_masks.png",
+            "fold": 0
+        },
+        {
+            "image": "train/461_img.png",
+            "label": "train/461_masks.png",
+            "fold": 0
+        },
+        {
+            "image": "train/116_img.png",
+            "label": "train/116_masks.png",
+            "fold": 0
+        },
+        {
+            "image": "train/534_img.png",
+            "label": "train/534_masks.png",
+            "fold": 0
+        },
+        {
+            "image": "train/247_img.png",
+            "label": "train/247_masks.png",
+            "fold": 0
+        },
+        {
+            "image": "train/287_img.png",
+            "label": "train/287_masks.png",
+            "fold": 0
+        },
+        {
+            "image": "train/202_img.png",
+            "label": "train/202_masks.png",
+            "fold": 0
+        },
+        {
+            "image": "train/004_img.png",
+            "label": "train/004_masks.png",
+            "fold": 0
+        },
+        {
+            "image": "train/199_img.png",
+            "label": "train/199_masks.png",
+            "fold": 0
+        },
+        {
+            "image": "train/326_img.png",
+            "label": "train/326_masks.png",
+            "fold": 0
+        },
+        {
+            "image": "train/094_img.png",
+            "label": "train/094_masks.png",
+            "fold": 0
+        },
+        {
+            "image": "train/512_img.png",
+            "label": "train/512_masks.png",
+            "fold": 0
+        },
+        {
+            "image": "train/039_img.png",
+            "label": "train/039_masks.png",
+            "fold": 0
+        },
+        {
+            "image": "train/015_img.png",
+            "label": "train/015_masks.png",
+            "fold": 0
+        },
+        {
+            "image": "train/095_img.png",
+            "label": "train/095_masks.png",
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+        },
+        {
+            "image": "train/214_img.png",
+            "label": "train/214_masks.png",
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+            "label": "train/100_masks.png",
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+            "label": "train/473_masks.png",
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+        {
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+            "label": "train/080_masks.png",
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+        {
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+            "label": "train/187_masks.png",
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+        {
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+            "label": "train/350_masks.png",
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+            "label": "train/111_masks.png",
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+            "label": "train/102_masks.png",
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+            "label": "train/243_masks.png",
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+            "label": "train/487_masks.png",
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+            "label": "train/300_masks.png",
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+            "label": "train/266_masks.png",
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+        {
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+        {
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diff --git a/vista2d/datalists/deepbacs_datalist.json b/vista2d/datalists/deepbacs_datalist.json
new file mode 100644
index 0000000..3a5dfd1
--- /dev/null
+++ b/vista2d/datalists/deepbacs_datalist.json
@@ -0,0 +1,921 @@
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+        {
+            "image": "training/source/JE2NileRed_oilp22_PMP_101220_004_1.tif",
+            "label": "training/target/JE2NileRed_oilp22_PMP_101220_004_1.tif",
+            "fold": 3
+        },
+        {
+            "image": "training/source/JE2NileRed_oilp22_PMP_101220_001_1.tif",
+            "label": "training/target/JE2NileRed_oilp22_PMP_101220_001_1.tif",
+            "fold": 3
+        },
+        {
+            "image": "training/source/train_68.tif",
+            "label": "training/target/train_68.tif",
+            "fold": 3
+        },
+        {
+            "image": "training/source/pos1_frame_68.tif",
+            "label": "training/target/pos1_frame_68.tif",
+            "fold": 3
+        },
+        {
+            "image": "training/source/train_8.tif",
+            "label": "training/target/train_8.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/train_26.tif",
+            "label": "training/target/train_26.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/JE2NileRed_oilp22_PMP_101220_004_3_NR.tif",
+            "label": "training/target/JE2NileRed_oilp22_PMP_101220_004_3_NR.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/train_31.tif",
+            "label": "training/target/train_31.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/train_62.tif",
+            "label": "training/target/train_62.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/train_36.tif",
+            "label": "training/target/train_36.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/train_70.tif",
+            "label": "training/target/train_70.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/train_5.tif",
+            "label": "training/target/train_5.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/JE2NileRed_oilp22_PMP_101220_003_4.tif",
+            "label": "training/target/JE2NileRed_oilp22_PMP_101220_003_4.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/train_46.tif",
+            "label": "training/target/train_46.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/train_47.tif",
+            "label": "training/target/train_47.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/JE2NileRed_oilp22_PMP_101220_002_3_NR.tif",
+            "label": "training/target/JE2NileRed_oilp22_PMP_101220_002_3_NR.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/train_20.tif",
+            "label": "training/target/train_20.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/pos0_frame_6.tif",
+            "label": "training/target/pos0_frame_6.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/JE2NileRed_oilp22_PMP_101220_006_3_NR.tif",
+            "label": "training/target/JE2NileRed_oilp22_PMP_101220_006_3_NR.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/train_13.tif",
+            "label": "training/target/train_13.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/JE2NileRed_oilp22_PMP_101220_002_1.tif",
+            "label": "training/target/JE2NileRed_oilp22_PMP_101220_002_1.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/JE2NileRed_oilp22_PMP_101220_005_2.tif",
+            "label": "training/target/JE2NileRed_oilp22_PMP_101220_005_2.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/pos1_frame_15.tif",
+            "label": "training/target/pos1_frame_15.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/train_14.tif",
+            "label": "training/target/train_14.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/JE2NileRed_oilp22_PMP_101220__1.tif",
+            "label": "training/target/JE2NileRed_oilp22_PMP_101220__1.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/train_22.tif",
+            "label": "training/target/train_22.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/JE2NileRed_oilp22_PMP_101220_001_2.tif",
+            "label": "training/target/JE2NileRed_oilp22_PMP_101220_001_2.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/JE2NileRed_oilp22_PMP_101220_004_4.tif",
+            "label": "training/target/JE2NileRed_oilp22_PMP_101220_004_4.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/train_76.tif",
+            "label": "training/target/train_76.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/pos4_frame_64.tif",
+            "label": "training/target/pos4_frame_64.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/JE2NileRed_oilp22_PMP_101220_001_4.tif",
+            "label": "training/target/JE2NileRed_oilp22_PMP_101220_001_4.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/JE2NileRed_oilp22_PMP_101220_003_2_NR.tif",
+            "label": "training/target/JE2NileRed_oilp22_PMP_101220_003_2_NR.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/train_32.tif",
+            "label": "training/target/train_32.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/JE2NileRed_oilp22_PMP_101220__3.tif",
+            "label": "training/target/JE2NileRed_oilp22_PMP_101220__3.tif",
+            "fold": 4
+        },
+        {
+            "image": "training/source/train_51.tif",
+            "label": "training/target/train_51.tif",
+            "fold": 4
+        }
+    ],
+    "testing": [
+        {
+            "image": "test/source/test_4.tif",
+            "label": "test/target/test_4.tif"
+        },
+        {
+            "image": "test/source/test_7.tif",
+            "label": "test/target/test_7.tif"
+        },
+        {
+            "image": "test/source/pos5_fr_20.tif",
+            "label": "test/target/pos5_fr_20.tif"
+        },
+        {
+            "image": "test/source/pos7_fr_80.tif",
+            "label": "test/target/pos7_fr_80.tif"
+        },
+        {
+            "image": "test/source/test_10.tif",
+            "label": "test/target/test_10.tif"
+        },
+        {
+            "image": "test/source/test_8.tif",
+            "label": "test/target/test_8.tif"
+        },
+        {
+            "image": "test/source/pos7_fr_60.tif",
+            "label": "test/target/pos7_fr_60.tif"
+        },
+        {
+            "image": "test/source/pos5_fr_80.tif",
+            "label": "test/target/pos5_fr_80.tif"
+        },
+        {
+            "image": "test/source/pos5_fr_60.tif",
+            "label": "test/target/pos5_fr_60.tif"
+        },
+        {
+            "image": "test/source/pos7_fr_1.tif",
+            "label": "test/target/pos7_fr_1.tif"
+        },
+        {
+            "image": "test/source/JE2NileRed_oilp22_PMP_101220_011_NR.tif",
+            "label": "test/target/JE2NileRed_oilp22_PMP_101220_011_NR.tif"
+        },
+        {
+            "image": "test/source/JE2NileRed_oilp22_PMP_101220_007.tif",
+            "label": "test/target/JE2NileRed_oilp22_PMP_101220_007.tif"
+        },
+        {
+            "image": "test/source/test_2.tif",
+            "label": "test/target/test_2.tif"
+        },
+        {
+            "image": "test/source/pos5_fr_40.tif",
+            "label": "test/target/pos5_fr_40.tif"
+        },
+        {
+            "image": "test/source/JE2NileRed_oilp22_PMP_101220_009_NR.tif",
+            "label": "test/target/JE2NileRed_oilp22_PMP_101220_009_NR.tif"
+        },
+        {
+            "image": "test/source/JE2NileRed_oilp22_PMP_101220_009.tif",
+            "label": "test/target/JE2NileRed_oilp22_PMP_101220_009.tif"
+        },
+        {
+            "image": "test/source/JE2NileRed_oilp22_PMP_101220_010.tif",
+            "label": "test/target/JE2NileRed_oilp22_PMP_101220_010.tif"
+        },
+        {
+            "image": "test/source/pos7_fr_20.tif",
+            "label": "test/target/pos7_fr_20.tif"
+        },
+        {
+            "image": "test/source/JE2NileRed_oilp22_PMP_101220_011.tif",
+            "label": "test/target/JE2NileRed_oilp22_PMP_101220_011.tif"
+        },
+        {
+            "image": "test/source/pos7_fr_40.tif",
+            "label": "test/target/pos7_fr_40.tif"
+        },
+        {
+            "image": "test/source/test_1.tif",
+            "label": "test/target/test_1.tif"
+        },
+        {
+            "image": "test/source/pos2_fr_60.tif",
+            "label": "test/target/pos2_fr_60.tif"
+        },
+        {
+            "image": "test/source/JE2NileRed_oilp22_PMP_101220_007_NR.tif",
+            "label": "test/target/JE2NileRed_oilp22_PMP_101220_007_NR.tif"
+        },
+        {
+            "image": "test/source/test_3.tif",
+            "label": "test/target/test_3.tif"
+        },
+        {
+            "image": "test/source/pos2_fr_80.tif",
+            "label": "test/target/pos2_fr_80.tif"
+        },
+        {
+            "image": "test/source/pos2_fr_40.tif",
+            "label": "test/target/pos2_fr_40.tif"
+        },
+        {
+            "image": "test/source/JE2NileRed_oilp22_PMP_101220_008.tif",
+            "label": "test/target/JE2NileRed_oilp22_PMP_101220_008.tif"
+        },
+        {
+            "image": "test/source/JE2NileRed_oilp22_PMP_101220_008_NR.tif",
+            "label": "test/target/JE2NileRed_oilp22_PMP_101220_008_NR.tif"
+        },
+        {
+            "image": "test/source/JE2NileRed_oilp22_PMP_101220_010_NR.tif",
+            "label": "test/target/JE2NileRed_oilp22_PMP_101220_010_NR.tif"
+        },
+        {
+            "image": "test/source/test_5.tif",
+            "label": "test/target/test_5.tif"
+        },
+        {
+            "image": "test/source/test_6.tif",
+            "label": "test/target/test_6.tif"
+        },
+        {
+            "image": "test/source/test_9.tif",
+            "label": "test/target/test_9.tif"
+        },
+        {
+            "image": "test/source/pos5_fr_1.tif",
+            "label": "test/target/pos5_fr_1.tif"
+        },
+        {
+            "image": "test/source/pos2_fr_1.tif",
+            "label": "test/target/pos2_fr_1.tif"
+        },
+        {
+            "image": "test/source/pos2_fr_20.tif",
+            "label": "test/target/pos2_fr_20.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/group1_datalist.yaml b/vista2d/datalists/group1_datalist.yaml
new file mode 100644
index 0000000..dbb453c
--- /dev/null
+++ b/vista2d/datalists/group1_datalist.yaml
@@ -0,0 +1,30 @@
+  - {datalist: datalists2/tissuenet_skin_mibi_datalist.json, basedir: /data/vista2d_combined/tissuenet_dataset/tissuenet_1.0}
+  - {datalist: datalists2/tissuenet_breast_mibi_datalist.json, basedir: /data/vista2d_combined/tissuenet_dataset/tissuenet_1.0}
+  - {datalist: datalists2/tissuenet_breast_vectra_datalist.json, basedir: /data/vista2d_combined/tissuenet_dataset/tissuenet_1.0}
+  - {datalist: datalists2/tissuenet_breast_imc_datalist.json, basedir: /data/vista2d_combined/tissuenet_dataset/tissuenet_1.0}
+  - {datalist: datalists2/tissuenet_gi_codex_datalist.json, basedir: /data/vista2d_combined/tissuenet_dataset/tissuenet_1.0}
+  - {datalist: datalists2/tissuenet_gi_mibi_datalist.json, basedir: /data/vista2d_combined/tissuenet_dataset/tissuenet_1.0}
+  - {datalist: datalists2/tissuenet_gi_mxif_datalist.json, basedir: /data/vista2d_combined/tissuenet_dataset/tissuenet_1.0}
+  - {datalist: datalists2/tissuenet_immune_cycif_datalist.json, basedir: /data/vista2d_combined/tissuenet_dataset/tissuenet_1.0}
+  - {datalist: datalists2/tissuenet_immune_mibi_datalist.json, basedir: /data/vista2d_combined/tissuenet_dataset/tissuenet_1.0}
+  - {datalist: datalists2/tissuenet_immune_vectra_datalist.json, basedir: /data/vista2d_combined/tissuenet_dataset/tissuenet_1.0}
+  - {datalist: datalists2/tissuenet_lung_cycif_datalist.json, basedir: /data/vista2d_combined/tissuenet_dataset/tissuenet_1.0}
+  - {datalist: datalists2/tissuenet_pancreas_codex_datalist.json, basedir: /data/vista2d_combined/tissuenet_dataset/tissuenet_1.0}
+  - {datalist: datalists2/tissuenet_pancreas_vectra_datalist.json, basedir: /data/vista2d_combined/tissuenet_dataset/tissuenet_1.0}
+
+  - {datalist: datalists2/cellpose_datalist.json, basedir: /data/vista2d_combined/cellpose_dataset}
+
+  - {datalist: datalists2/omnipose_bact_f_dataset.json, basedir: /data/vista2d_combined/omnipose_dataset}
+  - {datalist: datalists2/omnipose_bact_p_dataset.json, basedir: /data/vista2d_combined/omnipose_dataset}
+
+  - {datalist: datalists2/livecell_A172_datalist.json, basedir: /data/vista2d_combined/livecell_dataset/images}
+  - {datalist: datalists2/livecell_BT474_datalist.json, basedir: /data/vista2d_combined/livecell_dataset/images}
+  - {datalist: datalists2/livecell_Huh7_datalist.json, basedir: /data/vista2d_combined/livecell_dataset/images}
+  - {datalist: datalists2/livecell_MCF7_datalist.json, basedir: /data/vista2d_combined/livecell_dataset/images}
+  - {datalist: datalists2/livecell_SHSY5Y_datalist.json, basedir: /data/vista2d_combined/livecell_dataset/images}
+  - {datalist: datalists2/livecell_SkBr3_datalist.json, basedir: /data/vista2d_combined/livecell_dataset/images}
+  - {datalist: datalists2/livecell_SKOV3_datalist.json, basedir: /data/vista2d_combined/livecell_dataset/images}
+
+  - {datalist: datalists2/deepbacs_datalist.json, basedir: /data/vista2d_combined/deepbacs_dataset}
+  - {datalist: datalists2/nips_data_list.json, basedir: /data/vista2d_combined/nips_dataset}
+  - {datalist: datalists2/kg_nuclei_datalist.json, basedir: /data/vista2d_combined/kaggle_dataset}
diff --git a/vista2d/datalists/kg_nuclei_datalist.json b/vista2d/datalists/kg_nuclei_datalist.json
new file mode 100644
index 0000000..00f9f52
--- /dev/null
+++ b/vista2d/datalists/kg_nuclei_datalist.json
@@ -0,0 +1,3287 @@
+{
+    "training": [
+        {
+            "image": "instance_processed_data/kg_bowl_193_img.png",
+            "label": "instance_processed_data/kg_bowl_193_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_426_img.png",
+            "label": "instance_processed_data/kg_bowl_426_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_362_img.png",
+            "label": "instance_processed_data/kg_bowl_362_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_266_img.png",
+            "label": "instance_processed_data/kg_bowl_266_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_397_img.png",
+            "label": "instance_processed_data/kg_bowl_397_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_328_img.png",
+            "label": "instance_processed_data/kg_bowl_328_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_364_img.png",
+            "label": "instance_processed_data/kg_bowl_364_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_42_img.png",
+            "label": "instance_processed_data/kg_bowl_42_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_392_img.png",
+            "label": "instance_processed_data/kg_bowl_392_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_296_img.png",
+            "label": "instance_processed_data/kg_bowl_296_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_419_img.png",
+            "label": "instance_processed_data/kg_bowl_419_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_436_img.png",
+            "label": "instance_processed_data/kg_bowl_436_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_213_img.png",
+            "label": "instance_processed_data/kg_bowl_213_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_271_img.png",
+            "label": "instance_processed_data/kg_bowl_271_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_384_img.png",
+            "label": "instance_processed_data/kg_bowl_384_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_493_img.png",
+            "label": "instance_processed_data/kg_bowl_493_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_492_img.png",
+            "label": "instance_processed_data/kg_bowl_492_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_489_img.png",
+            "label": "instance_processed_data/kg_bowl_489_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_474_img.png",
+            "label": "instance_processed_data/kg_bowl_474_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_215_img.png",
+            "label": "instance_processed_data/kg_bowl_215_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_595_img.png",
+            "label": "instance_processed_data/kg_bowl_595_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_518_img.png",
+            "label": "instance_processed_data/kg_bowl_518_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_560_img.png",
+            "label": "instance_processed_data/kg_bowl_560_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_409_img.png",
+            "label": "instance_processed_data/kg_bowl_409_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_396_img.png",
+            "label": "instance_processed_data/kg_bowl_396_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_1_img.png",
+            "label": "instance_processed_data/kg_bowl_1_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_164_img.png",
+            "label": "instance_processed_data/kg_bowl_164_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_148_img.png",
+            "label": "instance_processed_data/kg_bowl_148_img_masks.tiff",
+            "fold": 0
+        },
+        {
+            "image": "instance_processed_data/kg_bowl_119_img.png",
+            "label": "instance_processed_data/kg_bowl_119_img_masks.tiff",
+            "fold": 0
+        },
+        {
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+}
diff --git a/vista2d/datalists/livecell_A172_datalist.json b/vista2d/datalists/livecell_A172_datalist.json
new file mode 100644
index 0000000..4bcb995
--- /dev/null
+++ b/vista2d/datalists/livecell_A172_datalist.json
@@ -0,0 +1,2894 @@
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+}
diff --git a/vista2d/datalists/livecell_BT474_datalist.json b/vista2d/datalists/livecell_BT474_datalist.json
new file mode 100644
index 0000000..1011043
--- /dev/null
+++ b/vista2d/datalists/livecell_BT474_datalist.json
@@ -0,0 +1,3198 @@
+{
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+}
diff --git a/vista2d/datalists/livecell_BV2_datalist.json b/vista2d/datalists/livecell_BV2_datalist.json
new file mode 100644
index 0000000..95d5e0c
--- /dev/null
+++ b/vista2d/datalists/livecell_BV2_datalist.json
@@ -0,0 +1,2884 @@
+{
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diff --git a/vista2d/datalists/livecell_Huh7_datalist.json b/vista2d/datalists/livecell_Huh7_datalist.json
new file mode 100644
index 0000000..5bb4089
--- /dev/null
+++ b/vista2d/datalists/livecell_Huh7_datalist.json
@@ -0,0 +1,2806 @@
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+}
diff --git a/vista2d/datalists/livecell_MCF7_datalist.json b/vista2d/datalists/livecell_MCF7_datalist.json
new file mode 100644
index 0000000..a75e4b0
--- /dev/null
+++ b/vista2d/datalists/livecell_MCF7_datalist.json
@@ -0,0 +1,3497 @@
+{
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+}
diff --git a/vista2d/datalists/livecell_SHSY5Y_datalist.json b/vista2d/datalists/livecell_SHSY5Y_datalist.json
new file mode 100644
index 0000000..22c92fe
--- /dev/null
+++ b/vista2d/datalists/livecell_SHSY5Y_datalist.json
@@ -0,0 +1,3350 @@
+{
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diff --git a/vista2d/datalists/livecell_SKOV3_datalist.json b/vista2d/datalists/livecell_SKOV3_datalist.json
new file mode 100644
index 0000000..e9d2909
--- /dev/null
+++ b/vista2d/datalists/livecell_SKOV3_datalist.json
@@ -0,0 +1,2742 @@
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+}
diff --git a/vista2d/datalists/livecell_SkBr3_datalist.json b/vista2d/datalists/livecell_SkBr3_datalist.json
new file mode 100644
index 0000000..f0be1ae
--- /dev/null
+++ b/vista2d/datalists/livecell_SkBr3_datalist.json
@@ -0,0 +1,3350 @@
+{
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diff --git a/vista2d/datalists/nips_data_list.json b/vista2d/datalists/nips_data_list.json
new file mode 100644
index 0000000..647d62b
--- /dev/null
+++ b/vista2d/datalists/nips_data_list.json
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+            "label": "Testing/Public/labels/OpenTest_010_label.tiff"
+        }
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+}
diff --git a/vista2d/datalists/omnipose_bact_f_dataset.json b/vista2d/datalists/omnipose_bact_f_dataset.json
new file mode 100644
index 0000000..9495bed
--- /dev/null
+++ b/vista2d/datalists/omnipose_bact_f_dataset.json
@@ -0,0 +1,1164 @@
+{
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+            "image": "bact_fluor/test_sorted/cex/s_flex_cex_xy4_tile1_cyto.tif",
+            "label": "bact_fluor/test_sorted/cex/s_flex_cex_xy4_tile1_cyto_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/vibrio/vibrio_ensemble_fluor_3.tif",
+            "label": "bact_fluor/test_sorted/vibrio/vibrio_ensemble_fluor_3_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/vibrio/vibrio_ensemble_fluor_13.tif",
+            "label": "bact_fluor/test_sorted/vibrio/vibrio_ensemble_fluor_13_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/vibrio/vibrio_ensemble_fluor_11.tif",
+            "label": "bact_fluor/test_sorted/vibrio/vibrio_ensemble_fluor_11_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/vibrio/vibrio_ensemble_fluor_7.tif",
+            "label": "bact_fluor/test_sorted/vibrio/vibrio_ensemble_fluor_7_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/vibrio/vibrio_ensemble_fluor_5.tif",
+            "label": "bact_fluor/test_sorted/vibrio/vibrio_ensemble_fluor_5_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/vibrio/vibrio_ensemble_fluor_1.tif",
+            "label": "bact_fluor/test_sorted/vibrio/vibrio_ensemble_fluor_1_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/vibrio/vibrio_ensemble_fluor_9.tif",
+            "label": "bact_fluor/test_sorted/vibrio/vibrio_ensemble_fluor_9_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/bthai/bthai_f_t010xy2_tile4_cyto.tif",
+            "label": "bact_fluor/test_sorted/bthai/bthai_f_t010xy2_tile4_cyto_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/bthai/bthai_f_t001xy2_tile1_cyto.tif",
+            "label": "bact_fluor/test_sorted/bthai/bthai_f_t001xy2_tile1_cyto_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/bthai/bthai_f_t010xy2_tile4_membrane.tif",
+            "label": "bact_fluor/test_sorted/bthai/bthai_f_t010xy2_tile4_membrane_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/bthai/bthai_f_t010xy3_tile4_membrane.tif",
+            "label": "bact_fluor/test_sorted/bthai/bthai_f_t010xy3_tile4_membrane_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/bthai/bthai_f_t001xy2_tile1_membrane.tif",
+            "label": "bact_fluor/test_sorted/bthai/bthai_f_t001xy2_tile1_membrane_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/bthai/bthai_f_t010xy3_tile4_cyto.tif",
+            "label": "bact_fluor/test_sorted/bthai/bthai_f_t010xy3_tile4_cyto_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_57.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_57_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_41.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_41_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_115.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_115_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_81.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_81_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_91.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_91_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_87.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_87_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_31.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_31_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_45.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_45_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_83.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_83_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_65.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_65_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_15.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_15_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_69.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_69_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_107.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_107_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_89.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_89_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_51.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_51_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_85.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_85_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_95.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_95_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_105.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_105_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_101.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_101_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_21.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_21_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_29.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_29_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_23.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_23_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_59.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_59_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_27.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_27_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_35.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_35_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_33.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_33_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_1.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_1_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_111.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_111_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_75.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_75_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_39.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_39_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_25.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_25_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_5.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_5_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_53.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_53_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_77.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_77_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_73.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_73_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_97.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_97_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_99.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_99_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_49.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_49_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_79.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_79_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_13.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_13_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_9.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_9_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_61.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_61_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_17.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_17_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_93.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_93_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_63.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_63_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_55.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_55_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_3.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_3_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_19.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_19_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_7.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_7_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_67.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_67_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_71.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_71_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_47.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_47_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_109.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_109_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_113.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_113_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_11.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_11_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_43.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_43_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_37.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_37_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_103.tif",
+            "label": "bact_fluor/test_sorted/wiggins/wiggins_ensemble_fluor_103_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/A22/s_flex_a22_t12xy5c1_tile1_cyto.tif",
+            "label": "bact_fluor/test_sorted/A22/s_flex_a22_t12xy5c1_tile1_cyto_masks.tif"
+        },
+        {
+            "image": "bact_fluor/test_sorted/A22/s_flex_a22_t12xy5c1_tile14_cyto.tif",
+            "label": "bact_fluor/test_sorted/A22/s_flex_a22_t12xy5c1_tile14_cyto_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/omnipose_bact_p_dataset.json b/vista2d/datalists/omnipose_bact_p_dataset.json
new file mode 100644
index 0000000..702014b
--- /dev/null
+++ b/vista2d/datalists/omnipose_bact_p_dataset.json
@@ -0,0 +1,2092 @@
+{
+    "training": [
+        {
+            "image": "bact_phase/train_sorted/ftsN/ftsN_ensemble_31.tif",
+            "label": "bact_phase/train_sorted/ftsN/ftsN_ensemble_31_masks.tif",
+            "flows": "bact_phase/train_sorted/ftsN/ftsN_ensemble_31_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "bact_phase/train_sorted/ftsN/ftsN_ensemble_33.tif",
+            "label": "bact_phase/train_sorted/ftsN/ftsN_ensemble_33_masks.tif",
+            "flows": "bact_phase/train_sorted/ftsN/ftsN_ensemble_33_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "bact_phase/train_sorted/ftsN/ftsN_ensemble_3.tif",
+            "label": "bact_phase/train_sorted/ftsN/ftsN_ensemble_3_masks.tif",
+            "flows": "bact_phase/train_sorted/ftsN/ftsN_ensemble_3_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "bact_phase/train_sorted/ftsN/ftsN_ensemble_15.tif",
+            "label": "bact_phase/train_sorted/ftsN/ftsN_ensemble_15_masks.tif",
+            "flows": "bact_phase/train_sorted/ftsN/ftsN_ensemble_15_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "bact_phase/train_sorted/ftsN/ftsN_ensemble_47.tif",
+            "label": "bact_phase/train_sorted/ftsN/ftsN_ensemble_47_masks.tif",
+            "flows": "bact_phase/train_sorted/ftsN/ftsN_ensemble_47_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "bact_phase/train_sorted/ftsN/ftsN_ensemble_23.tif",
+            "label": "bact_phase/train_sorted/ftsN/ftsN_ensemble_23_masks.tif",
+            "flows": "bact_phase/train_sorted/ftsN/ftsN_ensemble_23_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "bact_phase/train_sorted/ftsN/ftsN_ensemble_29.tif",
+            "label": "bact_phase/train_sorted/ftsN/ftsN_ensemble_29_masks.tif",
+            "flows": "bact_phase/train_sorted/ftsN/ftsN_ensemble_29_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "bact_phase/train_sorted/ftsN/ftsN_ensemble_17.tif",
+            "label": "bact_phase/train_sorted/ftsN/ftsN_ensemble_17_masks.tif",
+            "flows": "bact_phase/train_sorted/ftsN/ftsN_ensemble_17_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "bact_phase/train_sorted/dnaA/dnaA_xy1_ensemble_0.tif",
+            "label": "bact_phase/train_sorted/dnaA/dnaA_xy1_ensemble_0_masks.tif",
+            "flows": "bact_phase/train_sorted/dnaA/dnaA_xy1_ensemble_0_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "bact_phase/train_sorted/dnaA/dnaA_xy1_ensemble_10.tif",
+            "label": "bact_phase/train_sorted/dnaA/dnaA_xy1_ensemble_10_masks.tif",
+            "flows": "bact_phase/train_sorted/dnaA/dnaA_xy1_ensemble_10_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "bact_phase/train_sorted/5I_crop/5I_crop_ensemble_0.tif",
+            "label": "bact_phase/train_sorted/5I_crop/5I_crop_ensemble_0_masks.tif",
+            "flows": "bact_phase/train_sorted/5I_crop/5I_crop_ensemble_0_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "bact_phase/train_sorted/cex/cex_xy3c1_tile_1.tif",
+            "label": "bact_phase/train_sorted/cex/cex_xy3c1_tile_1_masks.tif",
+            "flows": "bact_phase/train_sorted/cex/cex_xy3c1_tile_1_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "bact_phase/train_sorted/cex/cex_xy3c1_tile_6.tif",
+            "label": "bact_phase/train_sorted/cex/cex_xy3c1_tile_6_masks.tif",
+            "flows": "bact_phase/train_sorted/cex/cex_xy3c1_tile_6_flows.tif",
+            "fold": 0
+        },
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+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY4_3_masks.tif"
+        },
+        {
+            "image": "bact_phase/test_sorted/streptomyces/streptomyces_XY9_1.tif",
+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY9_1_masks.tif"
+        },
+        {
+            "image": "bact_phase/test_sorted/streptomyces/streptomyces_XY6_4.tif",
+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY6_4_masks.tif"
+        },
+        {
+            "image": "bact_phase/test_sorted/streptomyces/streptomyces_XY12_3.tif",
+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY12_3_masks.tif"
+        },
+        {
+            "image": "bact_phase/test_sorted/streptomyces/streptomyces_XY8_1.tif",
+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY8_1_masks.tif"
+        },
+        {
+            "image": "bact_phase/test_sorted/streptomyces/streptomyces_XY11_2.tif",
+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY11_2_masks.tif"
+        },
+        {
+            "image": "bact_phase/test_sorted/streptomyces/streptomyces_XY5_1.tif",
+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY5_1_masks.tif"
+        },
+        {
+            "image": "bact_phase/test_sorted/streptomyces/streptomyces_XY3_1.tif",
+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY3_1_masks.tif"
+        },
+        {
+            "image": "bact_phase/test_sorted/streptomyces/streptomyces_XY11_3.tif",
+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY11_3_masks.tif"
+        },
+        {
+            "image": "bact_phase/test_sorted/streptomyces/streptomyces_XY7_1.tif",
+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY7_1_masks.tif"
+        },
+        {
+            "image": "bact_phase/test_sorted/streptomyces/streptomyces_XY6_2.tif",
+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY6_2_masks.tif"
+        },
+        {
+            "image": "bact_phase/test_sorted/streptomyces/streptomyces_XY12_2.tif",
+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY12_2_masks.tif"
+        },
+        {
+            "image": "bact_phase/test_sorted/streptomyces/streptomyces_XY15_1.tif",
+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY15_1_masks.tif"
+        },
+        {
+            "image": "bact_phase/test_sorted/streptomyces/streptomyces_XY7_2.tif",
+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY7_2_masks.tif"
+        },
+        {
+            "image": "bact_phase/test_sorted/streptomyces/streptomyces_XY6_3.tif",
+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY6_3_masks.tif"
+        },
+        {
+            "image": "bact_phase/test_sorted/streptomyces/streptomyces_XY12_4.tif",
+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY12_4_masks.tif"
+        },
+        {
+            "image": "bact_phase/test_sorted/streptomyces/streptomyces_XY17_1.tif",
+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY17_1_masks.tif"
+        },
+        {
+            "image": "bact_phase/test_sorted/streptomyces/streptomyces_XY12_1.tif",
+            "label": "bact_phase/test_sorted/streptomyces/streptomyces_XY12_1_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/omnipose_worm_dataset.json b/vista2d/datalists/omnipose_worm_dataset.json
new file mode 100644
index 0000000..ffcc652
--- /dev/null
+++ b/vista2d/datalists/omnipose_worm_dataset.json
@@ -0,0 +1,606 @@
+{
+    "training": [
+        {
+            "image": "worm/train/c_elegans_wormpose_38.tif",
+            "label": "worm/train/c_elegans_wormpose_38_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_38_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "worm/train/celegans_bbc010_45.tif",
+            "label": "worm/train/celegans_bbc010_45_masks.tif",
+            "flows": "worm/train/celegans_bbc010_45_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "worm/train/celegans_bbc010_34.tif",
+            "label": "worm/train/celegans_bbc010_34_masks.tif",
+            "flows": "worm/train/celegans_bbc010_34_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "worm/train/celegans_bbc010_61.tif",
+            "label": "worm/train/celegans_bbc010_61_masks.tif",
+            "flows": "worm/train/celegans_bbc010_61_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "worm/train/celegans_bbc010_27.tif",
+            "label": "worm/train/celegans_bbc010_27_masks.tif",
+            "flows": "worm/train/celegans_bbc010_27_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "worm/train/celegans_bbc010_49.tif",
+            "label": "worm/train/celegans_bbc010_49_masks.tif",
+            "flows": "worm/train/celegans_bbc010_49_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "worm/train/celegans_bbc010_56.tif",
+            "label": "worm/train/celegans_bbc010_56_masks.tif",
+            "flows": "worm/train/celegans_bbc010_56_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_22.tif",
+            "label": "worm/train/c_elegans_wormpose_22_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_22_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_44.tif",
+            "label": "worm/train/c_elegans_wormpose_44_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_44_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "worm/train/celegans_bbc010_63.tif",
+            "label": "worm/train/celegans_bbc010_63_masks.tif",
+            "flows": "worm/train/celegans_bbc010_63_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "worm/train/celegans_bbc010_30.tif",
+            "label": "worm/train/celegans_bbc010_30_masks.tif",
+            "flows": "worm/train/celegans_bbc010_30_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "worm/train/celegans_bbc010_72.tif",
+            "label": "worm/train/celegans_bbc010_72_masks.tif",
+            "flows": "worm/train/celegans_bbc010_72_flows.tif",
+            "fold": 0
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_26.tif",
+            "label": "worm/train/c_elegans_wormpose_26_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_26_flows.tif",
+            "fold": 1
+        },
+        {
+            "image": "worm/train/celegans_bbc010_10.tif",
+            "label": "worm/train/celegans_bbc010_10_masks.tif",
+            "flows": "worm/train/celegans_bbc010_10_flows.tif",
+            "fold": 1
+        },
+        {
+            "image": "worm/train/celegans_bbc010_25.tif",
+            "label": "worm/train/celegans_bbc010_25_masks.tif",
+            "flows": "worm/train/celegans_bbc010_25_flows.tif",
+            "fold": 1
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_4.tif",
+            "label": "worm/train/c_elegans_wormpose_4_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_4_flows.tif",
+            "fold": 1
+        },
+        {
+            "image": "worm/train/celegans_bbc010_67.tif",
+            "label": "worm/train/celegans_bbc010_67_masks.tif",
+            "flows": "worm/train/celegans_bbc010_67_flows.tif",
+            "fold": 1
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_30.tif",
+            "label": "worm/train/c_elegans_wormpose_30_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_30_flows.tif",
+            "fold": 1
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_6.tif",
+            "label": "worm/train/c_elegans_wormpose_6_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_6_flows.tif",
+            "fold": 1
+        },
+        {
+            "image": "worm/train/celegans_bbc010_16.tif",
+            "label": "worm/train/celegans_bbc010_16_masks.tif",
+            "flows": "worm/train/celegans_bbc010_16_flows.tif",
+            "fold": 1
+        },
+        {
+            "image": "worm/train/celegans_bbc010_2.tif",
+            "label": "worm/train/celegans_bbc010_2_masks.tif",
+            "flows": "worm/train/celegans_bbc010_2_flows.tif",
+            "fold": 1
+        },
+        {
+            "image": "worm/train/celegans_bbc010_0.tif",
+            "label": "worm/train/celegans_bbc010_0_masks.tif",
+            "flows": "worm/train/celegans_bbc010_0_flows.tif",
+            "fold": 1
+        },
+        {
+            "image": "worm/train/celegans_bbc010_4.tif",
+            "label": "worm/train/celegans_bbc010_4_masks.tif",
+            "flows": "worm/train/celegans_bbc010_4_flows.tif",
+            "fold": 1
+        },
+        {
+            "image": "worm/train/celegans_bbc010_18.tif",
+            "label": "worm/train/celegans_bbc010_18_masks.tif",
+            "flows": "worm/train/celegans_bbc010_18_flows.tif",
+            "fold": 1
+        },
+        {
+            "image": "worm/train/celegans_bbc010_69.tif",
+            "label": "worm/train/celegans_bbc010_69_masks.tif",
+            "flows": "worm/train/celegans_bbc010_69_flows.tif",
+            "fold": 2
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_42.tif",
+            "label": "worm/train/c_elegans_wormpose_42_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_42_flows.tif",
+            "fold": 2
+        },
+        {
+            "image": "worm/train/celegans_bbc010_58.tif",
+            "label": "worm/train/celegans_bbc010_58_masks.tif",
+            "flows": "worm/train/celegans_bbc010_58_flows.tif",
+            "fold": 2
+        },
+        {
+            "image": "worm/train/celegans_bbc010_70.tif",
+            "label": "worm/train/celegans_bbc010_70_masks.tif",
+            "flows": "worm/train/celegans_bbc010_70_flows.tif",
+            "fold": 2
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_20.tif",
+            "label": "worm/train/c_elegans_wormpose_20_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_20_flows.tif",
+            "fold": 2
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_16.tif",
+            "label": "worm/train/c_elegans_wormpose_16_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_16_flows.tif",
+            "fold": 2
+        },
+        {
+            "image": "worm/train/celegans_bbc010_54.tif",
+            "label": "worm/train/celegans_bbc010_54_masks.tif",
+            "flows": "worm/train/celegans_bbc010_54_flows.tif",
+            "fold": 2
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_14.tif",
+            "label": "worm/train/c_elegans_wormpose_14_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_14_flows.tif",
+            "fold": 2
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_2.tif",
+            "label": "worm/train/c_elegans_wormpose_2_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_2_flows.tif",
+            "fold": 2
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_0.tif",
+            "label": "worm/train/c_elegans_wormpose_0_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_0_flows.tif",
+            "fold": 2
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_10.tif",
+            "label": "worm/train/c_elegans_wormpose_10_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_10_flows.tif",
+            "fold": 2
+        },
+        {
+            "image": "worm/train/celegans_bbc010_23.tif",
+            "label": "worm/train/celegans_bbc010_23_masks.tif",
+            "flows": "worm/train/celegans_bbc010_23_flows.tif",
+            "fold": 2
+        },
+        {
+            "image": "worm/train/celegans_bbc010_52.tif",
+            "label": "worm/train/celegans_bbc010_52_masks.tif",
+            "flows": "worm/train/celegans_bbc010_52_flows.tif",
+            "fold": 3
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_18.tif",
+            "label": "worm/train/c_elegans_wormpose_18_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_18_flows.tif",
+            "fold": 3
+        },
+        {
+            "image": "worm/train/celegans_bbc010_47.tif",
+            "label": "worm/train/celegans_bbc010_47_masks.tif",
+            "flows": "worm/train/celegans_bbc010_47_flows.tif",
+            "fold": 3
+        },
+        {
+            "image": "worm/train/celegans_bbc010_50.tif",
+            "label": "worm/train/celegans_bbc010_50_masks.tif",
+            "flows": "worm/train/celegans_bbc010_50_flows.tif",
+            "fold": 3
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_32.tif",
+            "label": "worm/train/c_elegans_wormpose_32_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_32_flows.tif",
+            "fold": 3
+        },
+        {
+            "image": "worm/train/celegans_bbc010_29.tif",
+            "label": "worm/train/celegans_bbc010_29_masks.tif",
+            "flows": "worm/train/celegans_bbc010_29_flows.tif",
+            "fold": 3
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_8.tif",
+            "label": "worm/train/c_elegans_wormpose_8_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_8_flows.tif",
+            "fold": 3
+        },
+        {
+            "image": "worm/train/celegans_bbc010_6.tif",
+            "label": "worm/train/celegans_bbc010_6_masks.tif",
+            "flows": "worm/train/celegans_bbc010_6_flows.tif",
+            "fold": 3
+        },
+        {
+            "image": "worm/train/celegans_bbc010_8.tif",
+            "label": "worm/train/celegans_bbc010_8_masks.tif",
+            "flows": "worm/train/celegans_bbc010_8_flows.tif",
+            "fold": 3
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_40.tif",
+            "label": "worm/train/c_elegans_wormpose_40_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_40_flows.tif",
+            "fold": 3
+        },
+        {
+            "image": "worm/train/celegans_bbc010_36.tif",
+            "label": "worm/train/celegans_bbc010_36_masks.tif",
+            "flows": "worm/train/celegans_bbc010_36_flows.tif",
+            "fold": 3
+        },
+        {
+            "image": "worm/train/celegans_bbc010_65.tif",
+            "label": "worm/train/celegans_bbc010_65_masks.tif",
+            "flows": "worm/train/celegans_bbc010_65_flows.tif",
+            "fold": 3
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_34.tif",
+            "label": "worm/train/c_elegans_wormpose_34_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_34_flows.tif",
+            "fold": 4
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_12.tif",
+            "label": "worm/train/c_elegans_wormpose_12_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_12_flows.tif",
+            "fold": 4
+        },
+        {
+            "image": "worm/train/celegans_bbc010_32.tif",
+            "label": "worm/train/celegans_bbc010_32_masks.tif",
+            "flows": "worm/train/celegans_bbc010_32_flows.tif",
+            "fold": 4
+        },
+        {
+            "image": "worm/train/celegans_bbc010_38.tif",
+            "label": "worm/train/celegans_bbc010_38_masks.tif",
+            "flows": "worm/train/celegans_bbc010_38_flows.tif",
+            "fold": 4
+        },
+        {
+            "image": "worm/train/celegans_bbc010_12.tif",
+            "label": "worm/train/celegans_bbc010_12_masks.tif",
+            "flows": "worm/train/celegans_bbc010_12_flows.tif",
+            "fold": 4
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_28.tif",
+            "label": "worm/train/c_elegans_wormpose_28_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_28_flows.tif",
+            "fold": 4
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_24.tif",
+            "label": "worm/train/c_elegans_wormpose_24_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_24_flows.tif",
+            "fold": 4
+        },
+        {
+            "image": "worm/train/celegans_bbc010_21.tif",
+            "label": "worm/train/celegans_bbc010_21_masks.tif",
+            "flows": "worm/train/celegans_bbc010_21_flows.tif",
+            "fold": 4
+        },
+        {
+            "image": "worm/train/celegans_bbc010_14.tif",
+            "label": "worm/train/celegans_bbc010_14_masks.tif",
+            "flows": "worm/train/celegans_bbc010_14_flows.tif",
+            "fold": 4
+        },
+        {
+            "image": "worm/train/celegans_bbc010_43.tif",
+            "label": "worm/train/celegans_bbc010_43_masks.tif",
+            "flows": "worm/train/celegans_bbc010_43_flows.tif",
+            "fold": 4
+        },
+        {
+            "image": "worm/train/celegans_bbc010_41.tif",
+            "label": "worm/train/celegans_bbc010_41_masks.tif",
+            "flows": "worm/train/celegans_bbc010_41_flows.tif",
+            "fold": 4
+        },
+        {
+            "image": "worm/train/c_elegans_wormpose_36.tif",
+            "label": "worm/train/c_elegans_wormpose_36_masks.tif",
+            "flows": "worm/train/c_elegans_wormpose_36_flows.tif",
+            "fold": 4
+        }
+    ],
+    "testing": [
+        {
+            "image": "worm/test/c_elegans_wormpose_19.tif",
+            "label": "worm/test/c_elegans_wormpose_19_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_5.tif",
+            "label": "worm/test/celegans_bbc010_5_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_51.tif",
+            "label": "worm/test/celegans_bbc010_51_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_55.tif",
+            "label": "worm/test/celegans_bbc010_55_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_28.tif",
+            "label": "worm/test/celegans_bbc010_28_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_37.tif",
+            "label": "worm/test/celegans_bbc010_37_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_39.tif",
+            "label": "worm/test/c_elegans_wormpose_39_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_9.tif",
+            "label": "worm/test/c_elegans_wormpose_9_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_15.tif",
+            "label": "worm/test/celegans_bbc010_15_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_41.tif",
+            "label": "worm/test/c_elegans_wormpose_41_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_31.tif",
+            "label": "worm/test/celegans_bbc010_31_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_17.tif",
+            "label": "worm/test/celegans_bbc010_17_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_35.tif",
+            "label": "worm/test/celegans_bbc010_35_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_24.tif",
+            "label": "worm/test/celegans_bbc010_24_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_46.tif",
+            "label": "worm/test/celegans_bbc010_46_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_37.tif",
+            "label": "worm/test/c_elegans_wormpose_37_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_23.tif",
+            "label": "worm/test/c_elegans_wormpose_23_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_25.tif",
+            "label": "worm/test/c_elegans_wormpose_25_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_60.tif",
+            "label": "worm/test/celegans_bbc010_60_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_1.tif",
+            "label": "worm/test/celegans_bbc010_1_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_5.tif",
+            "label": "worm/test/c_elegans_wormpose_5_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_3.tif",
+            "label": "worm/test/c_elegans_wormpose_3_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_17.tif",
+            "label": "worm/test/c_elegans_wormpose_17_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_1.tif",
+            "label": "worm/test/c_elegans_wormpose_1_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_73.tif",
+            "label": "worm/test/celegans_bbc010_73_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_29.tif",
+            "label": "worm/test/c_elegans_wormpose_29_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_27.tif",
+            "label": "worm/test/c_elegans_wormpose_27_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_57.tif",
+            "label": "worm/test/celegans_bbc010_57_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_40.tif",
+            "label": "worm/test/celegans_bbc010_40_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_26.tif",
+            "label": "worm/test/celegans_bbc010_26_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_21.tif",
+            "label": "worm/test/c_elegans_wormpose_21_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_33.tif",
+            "label": "worm/test/celegans_bbc010_33_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_35.tif",
+            "label": "worm/test/c_elegans_wormpose_35_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_64.tif",
+            "label": "worm/test/celegans_bbc010_64_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_62.tif",
+            "label": "worm/test/celegans_bbc010_62_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_45.tif",
+            "label": "worm/test/c_elegans_wormpose_45_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_71.tif",
+            "label": "worm/test/celegans_bbc010_71_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_13.tif",
+            "label": "worm/test/celegans_bbc010_13_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_15.tif",
+            "label": "worm/test/c_elegans_wormpose_15_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_68.tif",
+            "label": "worm/test/celegans_bbc010_68_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_19.tif",
+            "label": "worm/test/celegans_bbc010_19_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_9.tif",
+            "label": "worm/test/celegans_bbc010_9_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_33.tif",
+            "label": "worm/test/c_elegans_wormpose_33_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_59.tif",
+            "label": "worm/test/celegans_bbc010_59_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_43.tif",
+            "label": "worm/test/c_elegans_wormpose_43_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_13.tif",
+            "label": "worm/test/c_elegans_wormpose_13_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_11.tif",
+            "label": "worm/test/c_elegans_wormpose_11_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_7.tif",
+            "label": "worm/test/c_elegans_wormpose_7_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_53.tif",
+            "label": "worm/test/celegans_bbc010_53_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_20.tif",
+            "label": "worm/test/celegans_bbc010_20_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_7.tif",
+            "label": "worm/test/celegans_bbc010_7_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_3.tif",
+            "label": "worm/test/celegans_bbc010_3_masks.tif"
+        },
+        {
+            "image": "worm/test/c_elegans_wormpose_31.tif",
+            "label": "worm/test/c_elegans_wormpose_31_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_66.tif",
+            "label": "worm/test/celegans_bbc010_66_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_39.tif",
+            "label": "worm/test/celegans_bbc010_39_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_48.tif",
+            "label": "worm/test/celegans_bbc010_48_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_22.tif",
+            "label": "worm/test/celegans_bbc010_22_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_42.tif",
+            "label": "worm/test/celegans_bbc010_42_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_11.tif",
+            "label": "worm/test/celegans_bbc010_11_masks.tif"
+        },
+        {
+            "image": "worm/test/celegans_bbc010_44.tif",
+            "label": "worm/test/celegans_bbc010_44_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/tissuenet_breast_datalist.json b/vista2d/datalists/tissuenet_breast_datalist.json
new file mode 100644
index 0000000..f6cebca
--- /dev/null
+++ b/vista2d/datalists/tissuenet_breast_datalist.json
@@ -0,0 +1,10537 @@
+{
+    "training": [
+        {
+            "image": "val/breast_imc_4.tif",
+            "label": "val/breast_imc_4_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_5.tif",
+            "label": "val/breast_imc_5_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_6.tif",
+            "label": "val/breast_imc_6_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_11.tif",
+            "label": "val/breast_imc_11_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_17.tif",
+            "label": "val/breast_imc_17_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_29.tif",
+            "label": "val/breast_imc_29_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_31.tif",
+            "label": "val/breast_imc_31_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_34.tif",
+            "label": "val/breast_imc_34_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_9.tif",
+            "label": "val/breast_mibi_9_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_20.tif",
+            "label": "val/breast_mibi_20_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_24.tif",
+            "label": "val/breast_mibi_24_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_27.tif",
+            "label": "val/breast_mibi_27_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_28.tif",
+            "label": "val/breast_mibi_28_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_29.tif",
+            "label": "val/breast_mibi_29_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_52.tif",
+            "label": "val/breast_mibi_52_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_54.tif",
+            "label": "val/breast_mibi_54_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_63.tif",
+            "label": "val/breast_mibi_63_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_64.tif",
+            "label": "val/breast_mibi_64_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_69.tif",
+            "label": "val/breast_mibi_69_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_72.tif",
+            "label": "val/breast_mibi_72_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_2.tif",
+            "label": "val/breast_vectra_2_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_6.tif",
+            "label": "val/breast_vectra_6_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_16.tif",
+            "label": "val/breast_vectra_16_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_20.tif",
+            "label": "val/breast_vectra_20_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_33.tif",
+            "label": "val/breast_vectra_33_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_42.tif",
+            "label": "val/breast_vectra_42_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_46.tif",
+            "label": "val/breast_vectra_46_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_53.tif",
+            "label": "val/breast_vectra_53_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_54.tif",
+            "label": "val/breast_vectra_54_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_60.tif",
+            "label": "val/breast_vectra_60_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_61.tif",
+            "label": "val/breast_vectra_61_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_63.tif",
+            "label": "val/breast_vectra_63_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_75.tif",
+            "label": "val/breast_vectra_75_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_78.tif",
+            "label": "val/breast_vectra_78_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_81.tif",
+            "label": "val/breast_vectra_81_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_82.tif",
+            "label": "val/breast_vectra_82_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_98.tif",
+            "label": "val/breast_vectra_98_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_99.tif",
+            "label": "val/breast_vectra_99_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_104.tif",
+            "label": "val/breast_vectra_104_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_110.tif",
+            "label": "val/breast_vectra_110_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_118.tif",
+            "label": "val/breast_vectra_118_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_121.tif",
+            "label": "val/breast_vectra_121_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_124.tif",
+            "label": "val/breast_vectra_124_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_130.tif",
+            "label": "val/breast_vectra_130_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_144.tif",
+            "label": "val/breast_vectra_144_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_147.tif",
+            "label": "val/breast_vectra_147_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_149.tif",
+            "label": "val/breast_vectra_149_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_157.tif",
+            "label": "val/breast_vectra_157_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_163.tif",
+            "label": "val/breast_vectra_163_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_166.tif",
+            "label": "val/breast_vectra_166_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_167.tif",
+            "label": "val/breast_vectra_167_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_168.tif",
+            "label": "val/breast_vectra_168_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_172.tif",
+            "label": "val/breast_vectra_172_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_177.tif",
+            "label": "val/breast_vectra_177_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_181.tif",
+            "label": "val/breast_vectra_181_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_183.tif",
+            "label": "val/breast_vectra_183_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_186.tif",
+            "label": "val/breast_vectra_186_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_194.tif",
+            "label": "val/breast_vectra_194_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_198.tif",
+            "label": "val/breast_vectra_198_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_199.tif",
+            "label": "val/breast_vectra_199_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_200.tif",
+            "label": "val/breast_vectra_200_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_203.tif",
+            "label": "val/breast_vectra_203_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_204.tif",
+            "label": "val/breast_vectra_204_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_213.tif",
+            "label": "val/breast_vectra_213_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_214.tif",
+            "label": "val/breast_vectra_214_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_218.tif",
+            "label": "val/breast_vectra_218_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_229.tif",
+            "label": "val/breast_vectra_229_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_236.tif",
+            "label": "val/breast_vectra_236_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_237.tif",
+            "label": "val/breast_vectra_237_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_240.tif",
+            "label": "val/breast_vectra_240_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_254.tif",
+            "label": "val/breast_vectra_254_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_256.tif",
+            "label": "val/breast_vectra_256_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_257.tif",
+            "label": "val/breast_vectra_257_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_274.tif",
+            "label": "val/breast_vectra_274_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_277.tif",
+            "label": "val/breast_vectra_277_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_278.tif",
+            "label": "val/breast_vectra_278_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_280.tif",
+            "label": "val/breast_vectra_280_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_285.tif",
+            "label": "val/breast_vectra_285_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_289.tif",
+            "label": "val/breast_vectra_289_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_291.tif",
+            "label": "val/breast_vectra_291_masks.tif",
+            "fold": 0
+        },
+        {
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+            "label": "test/breast_vectra_73_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_78.tif",
+            "label": "test/breast_vectra_78_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_89.tif",
+            "label": "test/breast_vectra_89_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_91.tif",
+            "label": "test/breast_vectra_91_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_96.tif",
+            "label": "test/breast_vectra_96_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_102.tif",
+            "label": "test/breast_vectra_102_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_103.tif",
+            "label": "test/breast_vectra_103_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_113.tif",
+            "label": "test/breast_vectra_113_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_115.tif",
+            "label": "test/breast_vectra_115_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_120.tif",
+            "label": "test/breast_vectra_120_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_122.tif",
+            "label": "test/breast_vectra_122_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_130.tif",
+            "label": "test/breast_vectra_130_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_1.tif",
+            "label": "test/breast_imc_1_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_7.tif",
+            "label": "test/breast_imc_7_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_8.tif",
+            "label": "test/breast_imc_8_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_4.tif",
+            "label": "test/breast_mibi_4_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_6.tif",
+            "label": "test/breast_mibi_6_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_9.tif",
+            "label": "test/breast_mibi_9_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_14.tif",
+            "label": "test/breast_mibi_14_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_21.tif",
+            "label": "test/breast_mibi_21_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_34.tif",
+            "label": "test/breast_mibi_34_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_41.tif",
+            "label": "test/breast_mibi_41_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_2.tif",
+            "label": "test/breast_vectra_2_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_3.tif",
+            "label": "test/breast_vectra_3_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_5.tif",
+            "label": "test/breast_vectra_5_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_10.tif",
+            "label": "test/breast_vectra_10_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_13.tif",
+            "label": "test/breast_vectra_13_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_14.tif",
+            "label": "test/breast_vectra_14_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_21.tif",
+            "label": "test/breast_vectra_21_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_28.tif",
+            "label": "test/breast_vectra_28_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_38.tif",
+            "label": "test/breast_vectra_38_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_43.tif",
+            "label": "test/breast_vectra_43_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_57.tif",
+            "label": "test/breast_vectra_57_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_63.tif",
+            "label": "test/breast_vectra_63_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_70.tif",
+            "label": "test/breast_vectra_70_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_71.tif",
+            "label": "test/breast_vectra_71_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_75.tif",
+            "label": "test/breast_vectra_75_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_76.tif",
+            "label": "test/breast_vectra_76_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_79.tif",
+            "label": "test/breast_vectra_79_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_81.tif",
+            "label": "test/breast_vectra_81_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_82.tif",
+            "label": "test/breast_vectra_82_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_83.tif",
+            "label": "test/breast_vectra_83_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_84.tif",
+            "label": "test/breast_vectra_84_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_86.tif",
+            "label": "test/breast_vectra_86_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_88.tif",
+            "label": "test/breast_vectra_88_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_98.tif",
+            "label": "test/breast_vectra_98_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_99.tif",
+            "label": "test/breast_vectra_99_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_108.tif",
+            "label": "test/breast_vectra_108_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_127.tif",
+            "label": "test/breast_vectra_127_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/tissuenet_breast_imc_datalist.json b/vista2d/datalists/tissuenet_breast_imc_datalist.json
new file mode 100644
index 0000000..76820c9
--- /dev/null
+++ b/vista2d/datalists/tissuenet_breast_imc_datalist.json
@@ -0,0 +1,900 @@
+{
+    "training": [
+        {
+            "image": "val/breast_imc_2.tif",
+            "label": "val/breast_imc_2_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_3.tif",
+            "label": "val/breast_imc_3_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_21.tif",
+            "label": "val/breast_imc_21_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_22.tif",
+            "label": "val/breast_imc_22_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_25.tif",
+            "label": "val/breast_imc_25_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_27.tif",
+            "label": "val/breast_imc_27_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_29.tif",
+            "label": "val/breast_imc_29_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_35.tif",
+            "label": "val/breast_imc_35_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_17.tif",
+            "label": "val/breast_imc_17_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_19.tif",
+            "label": "val/breast_imc_19_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_24.tif",
+            "label": "val/breast_imc_24_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_26.tif",
+            "label": "val/breast_imc_26_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_28.tif",
+            "label": "val/breast_imc_28_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_30.tif",
+            "label": "val/breast_imc_30_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_31.tif",
+            "label": "val/breast_imc_31_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_34.tif",
+            "label": "val/breast_imc_34_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_4.tif",
+            "label": "val/breast_imc_4_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_10.tif",
+            "label": "val/breast_imc_10_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_14.tif",
+            "label": "val/breast_imc_14_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_18.tif",
+            "label": "val/breast_imc_18_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_20.tif",
+            "label": "val/breast_imc_20_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_23.tif",
+            "label": "val/breast_imc_23_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_32.tif",
+            "label": "val/breast_imc_32_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_33.tif",
+            "label": "val/breast_imc_33_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_0.tif",
+            "label": "val/breast_imc_0_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_1.tif",
+            "label": "val/breast_imc_1_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_6.tif",
+            "label": "val/breast_imc_6_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_7.tif",
+            "label": "val/breast_imc_7_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_13.tif",
+            "label": "val/breast_imc_13_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_16.tif",
+            "label": "val/breast_imc_16_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_36.tif",
+            "label": "val/breast_imc_36_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_5.tif",
+            "label": "val/breast_imc_5_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_8.tif",
+            "label": "val/breast_imc_8_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_9.tif",
+            "label": "val/breast_imc_9_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_11.tif",
+            "label": "val/breast_imc_11_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_12.tif",
+            "label": "val/breast_imc_12_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_15.tif",
+            "label": "val/breast_imc_15_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_imc_37.tif",
+            "label": "val/breast_imc_37_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "train/breast_imc_0_2.tif",
+            "label": "train/breast_imc_0_2_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_1_0.tif",
+            "label": "train/breast_imc_1_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_7_3.tif",
+            "label": "train/breast_imc_7_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_8_1.tif",
+            "label": "train/breast_imc_8_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_8_3.tif",
+            "label": "train/breast_imc_8_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_11_2.tif",
+            "label": "train/breast_imc_11_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_12_0.tif",
+            "label": "train/breast_imc_12_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_12_3.tif",
+            "label": "train/breast_imc_12_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_13_1.tif",
+            "label": "train/breast_imc_13_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_13_3.tif",
+            "label": "train/breast_imc_13_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_14_0.tif",
+            "label": "train/breast_imc_14_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_14_3.tif",
+            "label": "train/breast_imc_14_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_16_3.tif",
+            "label": "train/breast_imc_16_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_23_0.tif",
+            "label": "train/breast_imc_23_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_25_2.tif",
+            "label": "train/breast_imc_25_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_25_3.tif",
+            "label": "train/breast_imc_25_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_26_0.tif",
+            "label": "train/breast_imc_26_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_26_2.tif",
+            "label": "train/breast_imc_26_2_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_27_0.tif",
+            "label": "train/breast_imc_27_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_27_2.tif",
+            "label": "train/breast_imc_27_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_28_1.tif",
+            "label": "train/breast_imc_28_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_29_0.tif",
+            "label": "train/breast_imc_29_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_29_2.tif",
+            "label": "train/breast_imc_29_2_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_30_1.tif",
+            "label": "train/breast_imc_30_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_30_2.tif",
+            "label": "train/breast_imc_30_2_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_31_1.tif",
+            "label": "train/breast_imc_31_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_2_2.tif",
+            "label": "train/breast_imc_2_2_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_4_1.tif",
+            "label": "train/breast_imc_4_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_4_3.tif",
+            "label": "train/breast_imc_4_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_6_3.tif",
+            "label": "train/breast_imc_6_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_8_0.tif",
+            "label": "train/breast_imc_8_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_9_0.tif",
+            "label": "train/breast_imc_9_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_9_2.tif",
+            "label": "train/breast_imc_9_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_10_2.tif",
+            "label": "train/breast_imc_10_2_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_11_0.tif",
+            "label": "train/breast_imc_11_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_11_1.tif",
+            "label": "train/breast_imc_11_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_13_2.tif",
+            "label": "train/breast_imc_13_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_14_2.tif",
+            "label": "train/breast_imc_14_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_15_2.tif",
+            "label": "train/breast_imc_15_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_16_2.tif",
+            "label": "train/breast_imc_16_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_18_2.tif",
+            "label": "train/breast_imc_18_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_19_1.tif",
+            "label": "train/breast_imc_19_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_20_0.tif",
+            "label": "train/breast_imc_20_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_20_3.tif",
+            "label": "train/breast_imc_20_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_21_1.tif",
+            "label": "train/breast_imc_21_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_22_0.tif",
+            "label": "train/breast_imc_22_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_24_1.tif",
+            "label": "train/breast_imc_24_1_masks.tif",
+            "fold": 1
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+            "label": "train/breast_imc_26_1_masks.tif",
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+        },
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+            "label": "train/breast_imc_30_0_masks.tif",
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+        },
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+            "label": "train/breast_imc_31_2_masks.tif",
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+        },
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+            "label": "train/breast_imc_31_3_masks.tif",
+            "fold": 1
+        },
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+            "image": "train/breast_imc_3_3.tif",
+            "label": "train/breast_imc_3_3_masks.tif",
+            "fold": 3
+        },
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+            "image": "train/breast_imc_5_1.tif",
+            "label": "train/breast_imc_5_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_5_3.tif",
+            "label": "train/breast_imc_5_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_6_0.tif",
+            "label": "train/breast_imc_6_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_6_2.tif",
+            "label": "train/breast_imc_6_2_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_8_2.tif",
+            "label": "train/breast_imc_8_2_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_9_3.tif",
+            "label": "train/breast_imc_9_3_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_10_0.tif",
+            "label": "train/breast_imc_10_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_10_1.tif",
+            "label": "train/breast_imc_10_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_10_3.tif",
+            "label": "train/breast_imc_10_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_12_1.tif",
+            "label": "train/breast_imc_12_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_13_0.tif",
+            "label": "train/breast_imc_13_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_16_1.tif",
+            "label": "train/breast_imc_16_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_17_1.tif",
+            "label": "train/breast_imc_17_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_17_2.tif",
+            "label": "train/breast_imc_17_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_18_1.tif",
+            "label": "train/breast_imc_18_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_19_2.tif",
+            "label": "train/breast_imc_19_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_20_2.tif",
+            "label": "train/breast_imc_20_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_22_1.tif",
+            "label": "train/breast_imc_22_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_22_2.tif",
+            "label": "train/breast_imc_22_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_22_3.tif",
+            "label": "train/breast_imc_22_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_23_1.tif",
+            "label": "train/breast_imc_23_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_24_2.tif",
+            "label": "train/breast_imc_24_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_28_0.tif",
+            "label": "train/breast_imc_28_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_28_2.tif",
+            "label": "train/breast_imc_28_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_29_3.tif",
+            "label": "train/breast_imc_29_3_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_0_0.tif",
+            "label": "train/breast_imc_0_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_0_3.tif",
+            "label": "train/breast_imc_0_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_1_3.tif",
+            "label": "train/breast_imc_1_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_2_0.tif",
+            "label": "train/breast_imc_2_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_3_1.tif",
+            "label": "train/breast_imc_3_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_5_2.tif",
+            "label": "train/breast_imc_5_2_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_7_2.tif",
+            "label": "train/breast_imc_7_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_11_3.tif",
+            "label": "train/breast_imc_11_3_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_14_1.tif",
+            "label": "train/breast_imc_14_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_15_0.tif",
+            "label": "train/breast_imc_15_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_15_1.tif",
+            "label": "train/breast_imc_15_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_15_3.tif",
+            "label": "train/breast_imc_15_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_17_0.tif",
+            "label": "train/breast_imc_17_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_20_1.tif",
+            "label": "train/breast_imc_20_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_21_2.tif",
+            "label": "train/breast_imc_21_2_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_21_3.tif",
+            "label": "train/breast_imc_21_3_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_23_2.tif",
+            "label": "train/breast_imc_23_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_23_3.tif",
+            "label": "train/breast_imc_23_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_24_0.tif",
+            "label": "train/breast_imc_24_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_25_0.tif",
+            "label": "train/breast_imc_25_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_27_3.tif",
+            "label": "train/breast_imc_27_3_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_28_3.tif",
+            "label": "train/breast_imc_28_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_29_1.tif",
+            "label": "train/breast_imc_29_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_30_3.tif",
+            "label": "train/breast_imc_30_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_31_0.tif",
+            "label": "train/breast_imc_31_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_0_1.tif",
+            "label": "train/breast_imc_0_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_1_1.tif",
+            "label": "train/breast_imc_1_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_1_2.tif",
+            "label": "train/breast_imc_1_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_2_1.tif",
+            "label": "train/breast_imc_2_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_2_3.tif",
+            "label": "train/breast_imc_2_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_3_0.tif",
+            "label": "train/breast_imc_3_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_3_2.tif",
+            "label": "train/breast_imc_3_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_4_0.tif",
+            "label": "train/breast_imc_4_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_4_2.tif",
+            "label": "train/breast_imc_4_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/breast_imc_5_0.tif",
+            "label": "train/breast_imc_5_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_6_1.tif",
+            "label": "train/breast_imc_6_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_7_0.tif",
+            "label": "train/breast_imc_7_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_7_1.tif",
+            "label": "train/breast_imc_7_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_9_1.tif",
+            "label": "train/breast_imc_9_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_12_2.tif",
+            "label": "train/breast_imc_12_2_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_16_0.tif",
+            "label": "train/breast_imc_16_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_17_3.tif",
+            "label": "train/breast_imc_17_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_18_0.tif",
+            "label": "train/breast_imc_18_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_18_3.tif",
+            "label": "train/breast_imc_18_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_19_0.tif",
+            "label": "train/breast_imc_19_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_19_3.tif",
+            "label": "train/breast_imc_19_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_21_0.tif",
+            "label": "train/breast_imc_21_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/breast_imc_25_1.tif",
+            "label": "train/breast_imc_25_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/breast_imc_26_3.tif",
+            "label": "train/breast_imc_26_3_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/breast_imc_27_1.tif",
+            "label": "train/breast_imc_27_1_masks.tif",
+            "fold": 2
+        }
+    ],
+    "testing": [
+        {
+            "image": "test/breast_imc_2.tif",
+            "label": "test/breast_imc_2_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_3.tif",
+            "label": "test/breast_imc_3_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_7.tif",
+            "label": "test/breast_imc_7_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_13.tif",
+            "label": "test/breast_imc_13_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_4.tif",
+            "label": "test/breast_imc_4_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_6.tif",
+            "label": "test/breast_imc_6_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_10.tif",
+            "label": "test/breast_imc_10_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_0.tif",
+            "label": "test/breast_imc_0_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_1.tif",
+            "label": "test/breast_imc_1_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_14.tif",
+            "label": "test/breast_imc_14_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_8.tif",
+            "label": "test/breast_imc_8_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_9.tif",
+            "label": "test/breast_imc_9_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_15.tif",
+            "label": "test/breast_imc_15_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_5.tif",
+            "label": "test/breast_imc_5_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_11.tif",
+            "label": "test/breast_imc_11_masks.tif"
+        },
+        {
+            "image": "test/breast_imc_12.tif",
+            "label": "test/breast_imc_12_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/tissuenet_breast_mibi_datalist.json b/vista2d/datalists/tissuenet_breast_mibi_datalist.json
new file mode 100644
index 0000000..1c06949
--- /dev/null
+++ b/vista2d/datalists/tissuenet_breast_mibi_datalist.json
@@ -0,0 +1,2174 @@
+{
+    "training": [
+        {
+            "image": "val/breast_mibi_2.tif",
+            "label": "val/breast_mibi_2_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_10.tif",
+            "label": "val/breast_mibi_10_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_27.tif",
+            "label": "val/breast_mibi_27_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_31.tif",
+            "label": "val/breast_mibi_31_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_38.tif",
+            "label": "val/breast_mibi_38_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_39.tif",
+            "label": "val/breast_mibi_39_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_41.tif",
+            "label": "val/breast_mibi_41_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_45.tif",
+            "label": "val/breast_mibi_45_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_49.tif",
+            "label": "val/breast_mibi_49_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_53.tif",
+            "label": "val/breast_mibi_53_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_54.tif",
+            "label": "val/breast_mibi_54_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_59.tif",
+            "label": "val/breast_mibi_59_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_60.tif",
+            "label": "val/breast_mibi_60_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_67.tif",
+            "label": "val/breast_mibi_67_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_69.tif",
+            "label": "val/breast_mibi_69_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_70.tif",
+            "label": "val/breast_mibi_70_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_85.tif",
+            "label": "val/breast_mibi_85_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_87.tif",
+            "label": "val/breast_mibi_87_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_19.tif",
+            "label": "val/breast_mibi_19_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_23.tif",
+            "label": "val/breast_mibi_23_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_32.tif",
+            "label": "val/breast_mibi_32_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_33.tif",
+            "label": "val/breast_mibi_33_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_34.tif",
+            "label": "val/breast_mibi_34_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_35.tif",
+            "label": "val/breast_mibi_35_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_36.tif",
+            "label": "val/breast_mibi_36_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_40.tif",
+            "label": "val/breast_mibi_40_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_43.tif",
+            "label": "val/breast_mibi_43_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_44.tif",
+            "label": "val/breast_mibi_44_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_46.tif",
+            "label": "val/breast_mibi_46_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_mibi_48.tif",
+            "label": "val/breast_mibi_48_masks.tif",
+            "fold": 0
+        },
+        {
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+        {
+            "image": "test/breast_mibi_30.tif",
+            "label": "test/breast_mibi_30_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_32.tif",
+            "label": "test/breast_mibi_32_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_33.tif",
+            "label": "test/breast_mibi_33_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_34.tif",
+            "label": "test/breast_mibi_34_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_38.tif",
+            "label": "test/breast_mibi_38_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_10.tif",
+            "label": "test/breast_mibi_10_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_14.tif",
+            "label": "test/breast_mibi_14_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_18.tif",
+            "label": "test/breast_mibi_18_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_20.tif",
+            "label": "test/breast_mibi_20_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_24.tif",
+            "label": "test/breast_mibi_24_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_26.tif",
+            "label": "test/breast_mibi_26_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_36.tif",
+            "label": "test/breast_mibi_36_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_41.tif",
+            "label": "test/breast_mibi_41_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_1.tif",
+            "label": "test/breast_mibi_1_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_6.tif",
+            "label": "test/breast_mibi_6_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_7.tif",
+            "label": "test/breast_mibi_7_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_13.tif",
+            "label": "test/breast_mibi_13_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_16.tif",
+            "label": "test/breast_mibi_16_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_25.tif",
+            "label": "test/breast_mibi_25_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_28.tif",
+            "label": "test/breast_mibi_28_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_40.tif",
+            "label": "test/breast_mibi_40_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_0.tif",
+            "label": "test/breast_mibi_0_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_5.tif",
+            "label": "test/breast_mibi_5_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_8.tif",
+            "label": "test/breast_mibi_8_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_9.tif",
+            "label": "test/breast_mibi_9_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_11.tif",
+            "label": "test/breast_mibi_11_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_12.tif",
+            "label": "test/breast_mibi_12_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_15.tif",
+            "label": "test/breast_mibi_15_masks.tif"
+        },
+        {
+            "image": "test/breast_mibi_37.tif",
+            "label": "test/breast_mibi_37_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/tissuenet_breast_vectra_datalist.json b/vista2d/datalists/tissuenet_breast_vectra_datalist.json
new file mode 100644
index 0000000..43304d1
--- /dev/null
+++ b/vista2d/datalists/tissuenet_breast_vectra_datalist.json
@@ -0,0 +1,7475 @@
+{
+    "training": [
+        {
+            "image": "val/breast_vectra_4.tif",
+            "label": "val/breast_vectra_4_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_5.tif",
+            "label": "val/breast_vectra_5_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_6.tif",
+            "label": "val/breast_vectra_6_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_11.tif",
+            "label": "val/breast_vectra_11_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_12.tif",
+            "label": "val/breast_vectra_12_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_18.tif",
+            "label": "val/breast_vectra_18_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_27.tif",
+            "label": "val/breast_vectra_27_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_59.tif",
+            "label": "val/breast_vectra_59_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_65.tif",
+            "label": "val/breast_vectra_65_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_67.tif",
+            "label": "val/breast_vectra_67_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_73.tif",
+            "label": "val/breast_vectra_73_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_85.tif",
+            "label": "val/breast_vectra_85_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_89.tif",
+            "label": "val/breast_vectra_89_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_90.tif",
+            "label": "val/breast_vectra_90_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_91.tif",
+            "label": "val/breast_vectra_91_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_92.tif",
+            "label": "val/breast_vectra_92_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_93.tif",
+            "label": "val/breast_vectra_93_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_95.tif",
+            "label": "val/breast_vectra_95_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_105.tif",
+            "label": "val/breast_vectra_105_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_106.tif",
+            "label": "val/breast_vectra_106_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_107.tif",
+            "label": "val/breast_vectra_107_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_111.tif",
+            "label": "val/breast_vectra_111_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_112.tif",
+            "label": "val/breast_vectra_112_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_119.tif",
+            "label": "val/breast_vectra_119_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_122.tif",
+            "label": "val/breast_vectra_122_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_125.tif",
+            "label": "val/breast_vectra_125_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_127.tif",
+            "label": "val/breast_vectra_127_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_131.tif",
+            "label": "val/breast_vectra_131_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_150.tif",
+            "label": "val/breast_vectra_150_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_158.tif",
+            "label": "val/breast_vectra_158_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_163.tif",
+            "label": "val/breast_vectra_163_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_165.tif",
+            "label": "val/breast_vectra_165_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_167.tif",
+            "label": "val/breast_vectra_167_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_169.tif",
+            "label": "val/breast_vectra_169_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_173.tif",
+            "label": "val/breast_vectra_173_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_174.tif",
+            "label": "val/breast_vectra_174_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_180.tif",
+            "label": "val/breast_vectra_180_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_186.tif",
+            "label": "val/breast_vectra_186_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_188.tif",
+            "label": "val/breast_vectra_188_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_199.tif",
+            "label": "val/breast_vectra_199_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_201.tif",
+            "label": "val/breast_vectra_201_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_204.tif",
+            "label": "val/breast_vectra_204_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_211.tif",
+            "label": "val/breast_vectra_211_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_218.tif",
+            "label": "val/breast_vectra_218_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_219.tif",
+            "label": "val/breast_vectra_219_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_224.tif",
+            "label": "val/breast_vectra_224_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_227.tif",
+            "label": "val/breast_vectra_227_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_232.tif",
+            "label": "val/breast_vectra_232_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_236.tif",
+            "label": "val/breast_vectra_236_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_238.tif",
+            "label": "val/breast_vectra_238_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_242.tif",
+            "label": "val/breast_vectra_242_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_249.tif",
+            "label": "val/breast_vectra_249_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_260.tif",
+            "label": "val/breast_vectra_260_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_270.tif",
+            "label": "val/breast_vectra_270_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_271.tif",
+            "label": "val/breast_vectra_271_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_272.tif",
+            "label": "val/breast_vectra_272_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_275.tif",
+            "label": "val/breast_vectra_275_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_286.tif",
+            "label": "val/breast_vectra_286_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_295.tif",
+            "label": "val/breast_vectra_295_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_298.tif",
+            "label": "val/breast_vectra_298_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_299.tif",
+            "label": "val/breast_vectra_299_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_303.tif",
+            "label": "val/breast_vectra_303_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_308.tif",
+            "label": "val/breast_vectra_308_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_314.tif",
+            "label": "val/breast_vectra_314_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_324.tif",
+            "label": "val/breast_vectra_324_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_329.tif",
+            "label": "val/breast_vectra_329_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_337.tif",
+            "label": "val/breast_vectra_337_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_344.tif",
+            "label": "val/breast_vectra_344_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_349.tif",
+            "label": "val/breast_vectra_349_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_350.tif",
+            "label": "val/breast_vectra_350_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_352.tif",
+            "label": "val/breast_vectra_352_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_0.tif",
+            "label": "val/breast_vectra_0_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_9.tif",
+            "label": "val/breast_vectra_9_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_14.tif",
+            "label": "val/breast_vectra_14_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_16.tif",
+            "label": "val/breast_vectra_16_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_17.tif",
+            "label": "val/breast_vectra_17_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_19.tif",
+            "label": "val/breast_vectra_19_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_29.tif",
+            "label": "val/breast_vectra_29_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_34.tif",
+            "label": "val/breast_vectra_34_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_38.tif",
+            "label": "val/breast_vectra_38_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_39.tif",
+            "label": "val/breast_vectra_39_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_41.tif",
+            "label": "val/breast_vectra_41_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_42.tif",
+            "label": "val/breast_vectra_42_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_44.tif",
+            "label": "val/breast_vectra_44_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_51.tif",
+            "label": "val/breast_vectra_51_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_58.tif",
+            "label": "val/breast_vectra_58_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_62.tif",
+            "label": "val/breast_vectra_62_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_70.tif",
+            "label": "val/breast_vectra_70_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_78.tif",
+            "label": "val/breast_vectra_78_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_80.tif",
+            "label": "val/breast_vectra_80_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_88.tif",
+            "label": "val/breast_vectra_88_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_100.tif",
+            "label": "val/breast_vectra_100_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_102.tif",
+            "label": "val/breast_vectra_102_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_110.tif",
+            "label": "val/breast_vectra_110_masks.tif",
+            "fold": 0
+        },
+        {
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+            "label": "val/breast_vectra_117_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_120.tif",
+            "label": "val/breast_vectra_120_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_123.tif",
+            "label": "val/breast_vectra_123_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_132.tif",
+            "label": "val/breast_vectra_132_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_138.tif",
+            "label": "val/breast_vectra_138_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_139.tif",
+            "label": "val/breast_vectra_139_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_146.tif",
+            "label": "val/breast_vectra_146_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/breast_vectra_147.tif",
+            "label": "val/breast_vectra_147_masks.tif",
+            "fold": 0
+        },
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+            "image": "test/breast_vectra_21.tif",
+            "label": "test/breast_vectra_21_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_22.tif",
+            "label": "test/breast_vectra_22_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_30.tif",
+            "label": "test/breast_vectra_30_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_47.tif",
+            "label": "test/breast_vectra_47_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_49.tif",
+            "label": "test/breast_vectra_49_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_57.tif",
+            "label": "test/breast_vectra_57_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_60.tif",
+            "label": "test/breast_vectra_60_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_61.tif",
+            "label": "test/breast_vectra_61_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_63.tif",
+            "label": "test/breast_vectra_63_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_70.tif",
+            "label": "test/breast_vectra_70_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_81.tif",
+            "label": "test/breast_vectra_81_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_86.tif",
+            "label": "test/breast_vectra_86_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_88.tif",
+            "label": "test/breast_vectra_88_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_94.tif",
+            "label": "test/breast_vectra_94_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_95.tif",
+            "label": "test/breast_vectra_95_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_96.tif",
+            "label": "test/breast_vectra_96_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_117.tif",
+            "label": "test/breast_vectra_117_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_123.tif",
+            "label": "test/breast_vectra_123_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_125.tif",
+            "label": "test/breast_vectra_125_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_129.tif",
+            "label": "test/breast_vectra_129_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_1.tif",
+            "label": "test/breast_vectra_1_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_6.tif",
+            "label": "test/breast_vectra_6_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_11.tif",
+            "label": "test/breast_vectra_11_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_14.tif",
+            "label": "test/breast_vectra_14_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_16.tif",
+            "label": "test/breast_vectra_16_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_18.tif",
+            "label": "test/breast_vectra_18_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_20.tif",
+            "label": "test/breast_vectra_20_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_25.tif",
+            "label": "test/breast_vectra_25_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_28.tif",
+            "label": "test/breast_vectra_28_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_29.tif",
+            "label": "test/breast_vectra_29_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_37.tif",
+            "label": "test/breast_vectra_37_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_50.tif",
+            "label": "test/breast_vectra_50_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_64.tif",
+            "label": "test/breast_vectra_64_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_68.tif",
+            "label": "test/breast_vectra_68_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_71.tif",
+            "label": "test/breast_vectra_71_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_72.tif",
+            "label": "test/breast_vectra_72_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_76.tif",
+            "label": "test/breast_vectra_76_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_79.tif",
+            "label": "test/breast_vectra_79_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_84.tif",
+            "label": "test/breast_vectra_84_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_87.tif",
+            "label": "test/breast_vectra_87_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_101.tif",
+            "label": "test/breast_vectra_101_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_105.tif",
+            "label": "test/breast_vectra_105_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_106.tif",
+            "label": "test/breast_vectra_106_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_107.tif",
+            "label": "test/breast_vectra_107_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_109.tif",
+            "label": "test/breast_vectra_109_masks.tif"
+        },
+        {
+            "image": "test/breast_vectra_119.tif",
+            "label": "test/breast_vectra_119_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/tissuenet_gi_codex_datalist.json b/vista2d/datalists/tissuenet_gi_codex_datalist.json
new file mode 100644
index 0000000..082fa15
--- /dev/null
+++ b/vista2d/datalists/tissuenet_gi_codex_datalist.json
@@ -0,0 +1,6964 @@
+{
+    "training": [
+        {
+            "image": "val/gi_codex_4.tif",
+            "label": "val/gi_codex_4_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_11.tif",
+            "label": "val/gi_codex_11_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_18.tif",
+            "label": "val/gi_codex_18_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_19.tif",
+            "label": "val/gi_codex_19_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_27.tif",
+            "label": "val/gi_codex_27_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_29.tif",
+            "label": "val/gi_codex_29_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_31.tif",
+            "label": "val/gi_codex_31_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_33.tif",
+            "label": "val/gi_codex_33_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_34.tif",
+            "label": "val/gi_codex_34_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_35.tif",
+            "label": "val/gi_codex_35_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_38.tif",
+            "label": "val/gi_codex_38_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_39.tif",
+            "label": "val/gi_codex_39_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_44.tif",
+            "label": "val/gi_codex_44_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_51.tif",
+            "label": "val/gi_codex_51_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_62.tif",
+            "label": "val/gi_codex_62_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_67.tif",
+            "label": "val/gi_codex_67_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_69.tif",
+            "label": "val/gi_codex_69_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_85.tif",
+            "label": "val/gi_codex_85_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_88.tif",
+            "label": "val/gi_codex_88_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_89.tif",
+            "label": "val/gi_codex_89_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_90.tif",
+            "label": "val/gi_codex_90_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_93.tif",
+            "label": "val/gi_codex_93_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_95.tif",
+            "label": "val/gi_codex_95_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_99.tif",
+            "label": "val/gi_codex_99_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_107.tif",
+            "label": "val/gi_codex_107_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_116.tif",
+            "label": "val/gi_codex_116_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_117.tif",
+            "label": "val/gi_codex_117_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_120.tif",
+            "label": "val/gi_codex_120_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_123.tif",
+            "label": "val/gi_codex_123_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_130.tif",
+            "label": "val/gi_codex_130_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_138.tif",
+            "label": "val/gi_codex_138_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_162.tif",
+            "label": "val/gi_codex_162_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_173.tif",
+            "label": "val/gi_codex_173_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_174.tif",
+            "label": "val/gi_codex_174_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_179.tif",
+            "label": "val/gi_codex_179_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_181.tif",
+            "label": "val/gi_codex_181_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_191.tif",
+            "label": "val/gi_codex_191_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_199.tif",
+            "label": "val/gi_codex_199_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_202.tif",
+            "label": "val/gi_codex_202_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_206.tif",
+            "label": "val/gi_codex_206_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_213.tif",
+            "label": "val/gi_codex_213_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_217.tif",
+            "label": "val/gi_codex_217_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_218.tif",
+            "label": "val/gi_codex_218_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_220.tif",
+            "label": "val/gi_codex_220_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_228.tif",
+            "label": "val/gi_codex_228_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_230.tif",
+            "label": "val/gi_codex_230_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_234.tif",
+            "label": "val/gi_codex_234_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_237.tif",
+            "label": "val/gi_codex_237_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_0.tif",
+            "label": "val/gi_codex_0_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_5.tif",
+            "label": "val/gi_codex_5_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_14.tif",
+            "label": "val/gi_codex_14_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_16.tif",
+            "label": "val/gi_codex_16_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_17.tif",
+            "label": "val/gi_codex_17_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_21.tif",
+            "label": "val/gi_codex_21_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_28.tif",
+            "label": "val/gi_codex_28_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_40.tif",
+            "label": "val/gi_codex_40_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_42.tif",
+            "label": "val/gi_codex_42_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_47.tif",
+            "label": "val/gi_codex_47_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_53.tif",
+            "label": "val/gi_codex_53_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_55.tif",
+            "label": "val/gi_codex_55_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_56.tif",
+            "label": "val/gi_codex_56_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_58.tif",
+            "label": "val/gi_codex_58_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_59.tif",
+            "label": "val/gi_codex_59_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_73.tif",
+            "label": "val/gi_codex_73_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_78.tif",
+            "label": "val/gi_codex_78_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_84.tif",
+            "label": "val/gi_codex_84_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_91.tif",
+            "label": "val/gi_codex_91_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_102.tif",
+            "label": "val/gi_codex_102_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_106.tif",
+            "label": "val/gi_codex_106_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_108.tif",
+            "label": "val/gi_codex_108_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_112.tif",
+            "label": "val/gi_codex_112_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_114.tif",
+            "label": "val/gi_codex_114_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_118.tif",
+            "label": "val/gi_codex_118_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_145.tif",
+            "label": "val/gi_codex_145_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_147.tif",
+            "label": "val/gi_codex_147_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_152.tif",
+            "label": "val/gi_codex_152_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_164.tif",
+            "label": "val/gi_codex_164_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_165.tif",
+            "label": "val/gi_codex_165_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_166.tif",
+            "label": "val/gi_codex_166_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_168.tif",
+            "label": "val/gi_codex_168_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_169.tif",
+            "label": "val/gi_codex_169_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_170.tif",
+            "label": "val/gi_codex_170_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_171.tif",
+            "label": "val/gi_codex_171_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_177.tif",
+            "label": "val/gi_codex_177_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_180.tif",
+            "label": "val/gi_codex_180_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_182.tif",
+            "label": "val/gi_codex_182_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_190.tif",
+            "label": "val/gi_codex_190_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_196.tif",
+            "label": "val/gi_codex_196_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_205.tif",
+            "label": "val/gi_codex_205_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_208.tif",
+            "label": "val/gi_codex_208_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_216.tif",
+            "label": "val/gi_codex_216_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_219.tif",
+            "label": "val/gi_codex_219_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_222.tif",
+            "label": "val/gi_codex_222_masks.tif",
+            "fold": 0
+        },
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+            "image": "train/gi_codex_265_1.tif",
+            "label": "train/gi_codex_265_1_masks.tif",
+            "fold": 1
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+            "image": "test/gi_codex_2.tif",
+            "label": "test/gi_codex_2_masks.tif"
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+        {
+            "image": "test/gi_codex_17.tif",
+            "label": "test/gi_codex_17_masks.tif"
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+            "label": "test/gi_codex_42_masks.tif"
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+            "label": "test/gi_codex_44_masks.tif"
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+            "label": "test/gi_codex_53_masks.tif"
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+            "label": "test/gi_codex_65_masks.tif"
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+            "image": "test/gi_codex_82.tif",
+            "label": "test/gi_codex_82_masks.tif"
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+            "label": "test/gi_codex_90_masks.tif"
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+            "label": "test/gi_codex_91_masks.tif"
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+            "label": "test/gi_codex_95_masks.tif"
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+            "label": "test/gi_codex_100_masks.tif"
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+            "label": "test/gi_codex_103_masks.tif"
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+            "label": "test/gi_codex_105_masks.tif"
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+            "label": "test/gi_codex_10_masks.tif"
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+            "label": "test/gi_codex_19_masks.tif"
+        },
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+            "image": "test/gi_codex_27.tif",
+            "label": "test/gi_codex_27_masks.tif"
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+            "label": "test/gi_codex_31_masks.tif"
+        },
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+            "image": "test/gi_codex_32.tif",
+            "label": "test/gi_codex_32_masks.tif"
+        },
+        {
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+            "label": "test/gi_codex_33_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_36.tif",
+            "label": "test/gi_codex_36_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_39.tif",
+            "label": "test/gi_codex_39_masks.tif"
+        },
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+            "label": "test/gi_codex_40_masks.tif"
+        },
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+            "image": "test/gi_codex_46.tif",
+            "label": "test/gi_codex_46_masks.tif"
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+            "label": "test/gi_codex_48_masks.tif"
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+            "label": "test/gi_codex_54_masks.tif"
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+            "label": "test/gi_codex_55_masks.tif"
+        },
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+            "label": "test/gi_codex_59_masks.tif"
+        },
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+            "label": "test/gi_codex_66_masks.tif"
+        },
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+            "label": "test/gi_codex_67_masks.tif"
+        },
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+            "label": "test/gi_codex_80_masks.tif"
+        },
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+            "label": "test/gi_codex_87_masks.tif"
+        },
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+            "label": "test/gi_codex_88_masks.tif"
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+            "label": "test/gi_codex_89_masks.tif"
+        },
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+            "label": "test/gi_codex_93_masks.tif"
+        },
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+            "label": "test/gi_codex_106_masks.tif"
+        },
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+            "image": "test/gi_codex_111.tif",
+            "label": "test/gi_codex_111_masks.tif"
+        },
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+            "image": "test/gi_codex_4.tif",
+            "label": "test/gi_codex_4_masks.tif"
+        },
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+            "image": "test/gi_codex_15.tif",
+            "label": "test/gi_codex_15_masks.tif"
+        },
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+            "image": "test/gi_codex_23.tif",
+            "label": "test/gi_codex_23_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_24.tif",
+            "label": "test/gi_codex_24_masks.tif"
+        },
+        {
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+            "label": "test/gi_codex_26_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_34.tif",
+            "label": "test/gi_codex_34_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_41.tif",
+            "label": "test/gi_codex_41_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_43.tif",
+            "label": "test/gi_codex_43_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_45.tif",
+            "label": "test/gi_codex_45_masks.tif"
+        },
+        {
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+            "label": "test/gi_codex_51_masks.tif"
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+            "image": "test/gi_codex_52.tif",
+            "label": "test/gi_codex_52_masks.tif"
+        },
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+            "label": "test/gi_codex_62_masks.tif"
+        },
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+            "label": "test/gi_codex_70_masks.tif"
+        },
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+            "image": "test/gi_codex_73.tif",
+            "label": "test/gi_codex_73_masks.tif"
+        },
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+            "label": "test/gi_codex_77_masks.tif"
+        },
+        {
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+            "label": "test/gi_codex_81_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_83.tif",
+            "label": "test/gi_codex_83_masks.tif"
+        },
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+            "image": "test/gi_codex_94.tif",
+            "label": "test/gi_codex_94_masks.tif"
+        },
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+            "image": "test/gi_codex_97.tif",
+            "label": "test/gi_codex_97_masks.tif"
+        },
+        {
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+            "label": "test/gi_codex_102_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_104.tif",
+            "label": "test/gi_codex_104_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_109.tif",
+            "label": "test/gi_codex_109_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_0.tif",
+            "label": "test/gi_codex_0_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_3.tif",
+            "label": "test/gi_codex_3_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_7.tif",
+            "label": "test/gi_codex_7_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_8.tif",
+            "label": "test/gi_codex_8_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_13.tif",
+            "label": "test/gi_codex_13_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_14.tif",
+            "label": "test/gi_codex_14_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_21.tif",
+            "label": "test/gi_codex_21_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_22.tif",
+            "label": "test/gi_codex_22_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_30.tif",
+            "label": "test/gi_codex_30_masks.tif"
+        },
+        {
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+            "label": "test/gi_codex_47_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_49.tif",
+            "label": "test/gi_codex_49_masks.tif"
+        },
+        {
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+            "label": "test/gi_codex_57_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_60.tif",
+            "label": "test/gi_codex_60_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_61.tif",
+            "label": "test/gi_codex_61_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_63.tif",
+            "label": "test/gi_codex_63_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_68.tif",
+            "label": "test/gi_codex_68_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_85.tif",
+            "label": "test/gi_codex_85_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_86.tif",
+            "label": "test/gi_codex_86_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_96.tif",
+            "label": "test/gi_codex_96_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_101.tif",
+            "label": "test/gi_codex_101_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_108.tif",
+            "label": "test/gi_codex_108_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_110.tif",
+            "label": "test/gi_codex_110_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_1.tif",
+            "label": "test/gi_codex_1_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_5.tif",
+            "label": "test/gi_codex_5_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_6.tif",
+            "label": "test/gi_codex_6_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_9.tif",
+            "label": "test/gi_codex_9_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_11.tif",
+            "label": "test/gi_codex_11_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_12.tif",
+            "label": "test/gi_codex_12_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_16.tif",
+            "label": "test/gi_codex_16_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_18.tif",
+            "label": "test/gi_codex_18_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_20.tif",
+            "label": "test/gi_codex_20_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_25.tif",
+            "label": "test/gi_codex_25_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_28.tif",
+            "label": "test/gi_codex_28_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_29.tif",
+            "label": "test/gi_codex_29_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_37.tif",
+            "label": "test/gi_codex_37_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_50.tif",
+            "label": "test/gi_codex_50_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_64.tif",
+            "label": "test/gi_codex_64_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_71.tif",
+            "label": "test/gi_codex_71_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_72.tif",
+            "label": "test/gi_codex_72_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_75.tif",
+            "label": "test/gi_codex_75_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_76.tif",
+            "label": "test/gi_codex_76_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_79.tif",
+            "label": "test/gi_codex_79_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_84.tif",
+            "label": "test/gi_codex_84_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_107.tif",
+            "label": "test/gi_codex_107_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/tissuenet_gi_datalist.json b/vista2d/datalists/tissuenet_gi_datalist.json
new file mode 100644
index 0000000..465964e
--- /dev/null
+++ b/vista2d/datalists/tissuenet_gi_datalist.json
@@ -0,0 +1,20960 @@
+{
+    "training": [
+        {
+            "image": "val/gi_codex_0.tif",
+            "label": "val/gi_codex_0_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_3.tif",
+            "label": "val/gi_codex_3_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_codex_8.tif",
+            "label": "val/gi_codex_8_masks.tif",
+            "fold": 0
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+            "label": "test/gi_mxif_1_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_2.tif",
+            "label": "test/gi_mxif_2_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_8.tif",
+            "label": "test/gi_mxif_8_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_9.tif",
+            "label": "test/gi_mxif_9_masks.tif"
+        },
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+            "image": "test/gi_mxif_11.tif",
+            "label": "test/gi_mxif_11_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_12.tif",
+            "label": "test/gi_mxif_12_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_20.tif",
+            "label": "test/gi_mxif_20_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_24.tif",
+            "label": "test/gi_mxif_24_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_26.tif",
+            "label": "test/gi_mxif_26_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_28.tif",
+            "label": "test/gi_mxif_28_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_29.tif",
+            "label": "test/gi_mxif_29_masks.tif"
+        },
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+            "image": "test/gi_mxif_34.tif",
+            "label": "test/gi_mxif_34_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_40.tif",
+            "label": "test/gi_mxif_40_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_42.tif",
+            "label": "test/gi_mxif_42_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_43.tif",
+            "label": "test/gi_mxif_43_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_45.tif",
+            "label": "test/gi_mxif_45_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_47.tif",
+            "label": "test/gi_mxif_47_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_70.tif",
+            "label": "test/gi_mxif_70_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_71.tif",
+            "label": "test/gi_mxif_71_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_76.tif",
+            "label": "test/gi_mxif_76_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_80.tif",
+            "label": "test/gi_mxif_80_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_2.tif",
+            "label": "test/gi_codex_2_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_10.tif",
+            "label": "test/gi_codex_10_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_13.tif",
+            "label": "test/gi_codex_13_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_20.tif",
+            "label": "test/gi_codex_20_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_23.tif",
+            "label": "test/gi_codex_23_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_36.tif",
+            "label": "test/gi_codex_36_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_45.tif",
+            "label": "test/gi_codex_45_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_52.tif",
+            "label": "test/gi_codex_52_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_54.tif",
+            "label": "test/gi_codex_54_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_57.tif",
+            "label": "test/gi_codex_57_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_60.tif",
+            "label": "test/gi_codex_60_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_75.tif",
+            "label": "test/gi_codex_75_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_77.tif",
+            "label": "test/gi_codex_77_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_81.tif",
+            "label": "test/gi_codex_81_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_82.tif",
+            "label": "test/gi_codex_82_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_94.tif",
+            "label": "test/gi_codex_94_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_96.tif",
+            "label": "test/gi_codex_96_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_97.tif",
+            "label": "test/gi_codex_97_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_98.tif",
+            "label": "test/gi_codex_98_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_103.tif",
+            "label": "test/gi_codex_103_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_2.tif",
+            "label": "test/gi_mibi_2_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_6.tif",
+            "label": "test/gi_mibi_6_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_12.tif",
+            "label": "test/gi_mibi_12_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_18.tif",
+            "label": "test/gi_mibi_18_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_23.tif",
+            "label": "test/gi_mibi_23_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_28.tif",
+            "label": "test/gi_mibi_28_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_33.tif",
+            "label": "test/gi_mibi_33_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_36.tif",
+            "label": "test/gi_mibi_36_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_37.tif",
+            "label": "test/gi_mibi_37_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_40.tif",
+            "label": "test/gi_mibi_40_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_48.tif",
+            "label": "test/gi_mibi_48_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_58.tif",
+            "label": "test/gi_mibi_58_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_64.tif",
+            "label": "test/gi_mibi_64_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_70.tif",
+            "label": "test/gi_mibi_70_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_81.tif",
+            "label": "test/gi_mibi_81_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_82.tif",
+            "label": "test/gi_mibi_82_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_86.tif",
+            "label": "test/gi_mibi_86_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_88.tif",
+            "label": "test/gi_mibi_88_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_90.tif",
+            "label": "test/gi_mibi_90_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_98.tif",
+            "label": "test/gi_mibi_98_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_101.tif",
+            "label": "test/gi_mibi_101_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_102.tif",
+            "label": "test/gi_mibi_102_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_108.tif",
+            "label": "test/gi_mibi_108_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_116.tif",
+            "label": "test/gi_mibi_116_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_124.tif",
+            "label": "test/gi_mibi_124_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_126.tif",
+            "label": "test/gi_mibi_126_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_128.tif",
+            "label": "test/gi_mibi_128_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_132.tif",
+            "label": "test/gi_mibi_132_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_134.tif",
+            "label": "test/gi_mibi_134_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_4.tif",
+            "label": "test/gi_mxif_4_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_7.tif",
+            "label": "test/gi_mxif_7_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_10.tif",
+            "label": "test/gi_mxif_10_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_13.tif",
+            "label": "test/gi_mxif_13_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_18.tif",
+            "label": "test/gi_mxif_18_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_38.tif",
+            "label": "test/gi_mxif_38_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_44.tif",
+            "label": "test/gi_mxif_44_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_46.tif",
+            "label": "test/gi_mxif_46_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_49.tif",
+            "label": "test/gi_mxif_49_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_50.tif",
+            "label": "test/gi_mxif_50_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_54.tif",
+            "label": "test/gi_mxif_54_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_55.tif",
+            "label": "test/gi_mxif_55_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_56.tif",
+            "label": "test/gi_mxif_56_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_57.tif",
+            "label": "test/gi_mxif_57_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_58.tif",
+            "label": "test/gi_mxif_58_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_64.tif",
+            "label": "test/gi_mxif_64_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_81.tif",
+            "label": "test/gi_mxif_81_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_82.tif",
+            "label": "test/gi_mxif_82_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_1.tif",
+            "label": "test/gi_codex_1_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_3.tif",
+            "label": "test/gi_codex_3_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_7.tif",
+            "label": "test/gi_codex_7_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_15.tif",
+            "label": "test/gi_codex_15_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_22.tif",
+            "label": "test/gi_codex_22_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_25.tif",
+            "label": "test/gi_codex_25_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_26.tif",
+            "label": "test/gi_codex_26_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_30.tif",
+            "label": "test/gi_codex_30_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_37.tif",
+            "label": "test/gi_codex_37_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_43.tif",
+            "label": "test/gi_codex_43_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_49.tif",
+            "label": "test/gi_codex_49_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_50.tif",
+            "label": "test/gi_codex_50_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_64.tif",
+            "label": "test/gi_codex_64_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_68.tif",
+            "label": "test/gi_codex_68_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_71.tif",
+            "label": "test/gi_codex_71_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_72.tif",
+            "label": "test/gi_codex_72_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_74.tif",
+            "label": "test/gi_codex_74_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_76.tif",
+            "label": "test/gi_codex_76_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_83.tif",
+            "label": "test/gi_codex_83_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_86.tif",
+            "label": "test/gi_codex_86_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_87.tif",
+            "label": "test/gi_codex_87_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_104.tif",
+            "label": "test/gi_codex_104_masks.tif"
+        },
+        {
+            "image": "test/gi_codex_109.tif",
+            "label": "test/gi_codex_109_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_3.tif",
+            "label": "test/gi_mibi_3_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_9.tif",
+            "label": "test/gi_mibi_9_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_14.tif",
+            "label": "test/gi_mibi_14_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_17.tif",
+            "label": "test/gi_mibi_17_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_21.tif",
+            "label": "test/gi_mibi_21_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_24.tif",
+            "label": "test/gi_mibi_24_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_29.tif",
+            "label": "test/gi_mibi_29_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_31.tif",
+            "label": "test/gi_mibi_31_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_32.tif",
+            "label": "test/gi_mibi_32_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_39.tif",
+            "label": "test/gi_mibi_39_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_41.tif",
+            "label": "test/gi_mibi_41_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_43.tif",
+            "label": "test/gi_mibi_43_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_44.tif",
+            "label": "test/gi_mibi_44_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_54.tif",
+            "label": "test/gi_mibi_54_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_66.tif",
+            "label": "test/gi_mibi_66_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_71.tif",
+            "label": "test/gi_mibi_71_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_78.tif",
+            "label": "test/gi_mibi_78_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_83.tif",
+            "label": "test/gi_mibi_83_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_84.tif",
+            "label": "test/gi_mibi_84_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_91.tif",
+            "label": "test/gi_mibi_91_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_97.tif",
+            "label": "test/gi_mibi_97_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_103.tif",
+            "label": "test/gi_mibi_103_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_104.tif",
+            "label": "test/gi_mibi_104_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_114.tif",
+            "label": "test/gi_mibi_114_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_123.tif",
+            "label": "test/gi_mibi_123_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_125.tif",
+            "label": "test/gi_mibi_125_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_127.tif",
+            "label": "test/gi_mibi_127_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_129.tif",
+            "label": "test/gi_mibi_129_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_131.tif",
+            "label": "test/gi_mibi_131_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_3.tif",
+            "label": "test/gi_mxif_3_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_5.tif",
+            "label": "test/gi_mxif_5_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_6.tif",
+            "label": "test/gi_mxif_6_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_15.tif",
+            "label": "test/gi_mxif_15_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_16.tif",
+            "label": "test/gi_mxif_16_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_17.tif",
+            "label": "test/gi_mxif_17_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_27.tif",
+            "label": "test/gi_mxif_27_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_30.tif",
+            "label": "test/gi_mxif_30_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_31.tif",
+            "label": "test/gi_mxif_31_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_32.tif",
+            "label": "test/gi_mxif_32_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_33.tif",
+            "label": "test/gi_mxif_33_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_59.tif",
+            "label": "test/gi_mxif_59_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_63.tif",
+            "label": "test/gi_mxif_63_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_65.tif",
+            "label": "test/gi_mxif_65_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_78.tif",
+            "label": "test/gi_mxif_78_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/tissuenet_gi_mibi_datalist.json b/vista2d/datalists/tissuenet_gi_mibi_datalist.json
new file mode 100644
index 0000000..0da873f
--- /dev/null
+++ b/vista2d/datalists/tissuenet_gi_mibi_datalist.json
@@ -0,0 +1,8804 @@
+{
+    "training": [
+        {
+            "image": "val/gi_mibi_4.tif",
+            "label": "val/gi_mibi_4_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mibi_9.tif",
+            "label": "val/gi_mibi_9_masks.tif",
+            "fold": 0
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+            "image": "test/gi_mibi_75.tif",
+            "label": "test/gi_mibi_75_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_76.tif",
+            "label": "test/gi_mibi_76_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_78.tif",
+            "label": "test/gi_mibi_78_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_82.tif",
+            "label": "test/gi_mibi_82_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_85.tif",
+            "label": "test/gi_mibi_85_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_95.tif",
+            "label": "test/gi_mibi_95_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_98.tif",
+            "label": "test/gi_mibi_98_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_99.tif",
+            "label": "test/gi_mibi_99_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_104.tif",
+            "label": "test/gi_mibi_104_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_107.tif",
+            "label": "test/gi_mibi_107_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_108.tif",
+            "label": "test/gi_mibi_108_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_116.tif",
+            "label": "test/gi_mibi_116_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_130.tif",
+            "label": "test/gi_mibi_130_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_136.tif",
+            "label": "test/gi_mibi_136_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_0.tif",
+            "label": "test/gi_mibi_0_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_1.tif",
+            "label": "test/gi_mibi_1_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_3.tif",
+            "label": "test/gi_mibi_3_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_7.tif",
+            "label": "test/gi_mibi_7_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_8.tif",
+            "label": "test/gi_mibi_8_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_9.tif",
+            "label": "test/gi_mibi_9_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_21.tif",
+            "label": "test/gi_mibi_21_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_22.tif",
+            "label": "test/gi_mibi_22_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_24.tif",
+            "label": "test/gi_mibi_24_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_30.tif",
+            "label": "test/gi_mibi_30_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_43.tif",
+            "label": "test/gi_mibi_43_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_47.tif",
+            "label": "test/gi_mibi_47_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_49.tif",
+            "label": "test/gi_mibi_49_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_57.tif",
+            "label": "test/gi_mibi_57_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_60.tif",
+            "label": "test/gi_mibi_60_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_61.tif",
+            "label": "test/gi_mibi_61_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_63.tif",
+            "label": "test/gi_mibi_63_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_70.tif",
+            "label": "test/gi_mibi_70_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_81.tif",
+            "label": "test/gi_mibi_81_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_88.tif",
+            "label": "test/gi_mibi_88_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_91.tif",
+            "label": "test/gi_mibi_91_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_103.tif",
+            "label": "test/gi_mibi_103_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_110.tif",
+            "label": "test/gi_mibi_110_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_115.tif",
+            "label": "test/gi_mibi_115_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_118.tif",
+            "label": "test/gi_mibi_118_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_131.tif",
+            "label": "test/gi_mibi_131_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_135.tif",
+            "label": "test/gi_mibi_135_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_11.tif",
+            "label": "test/gi_mibi_11_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_13.tif",
+            "label": "test/gi_mibi_13_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_14.tif",
+            "label": "test/gi_mibi_14_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_18.tif",
+            "label": "test/gi_mibi_18_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_20.tif",
+            "label": "test/gi_mibi_20_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_25.tif",
+            "label": "test/gi_mibi_25_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_28.tif",
+            "label": "test/gi_mibi_28_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_29.tif",
+            "label": "test/gi_mibi_29_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_37.tif",
+            "label": "test/gi_mibi_37_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_50.tif",
+            "label": "test/gi_mibi_50_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_68.tif",
+            "label": "test/gi_mibi_68_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_71.tif",
+            "label": "test/gi_mibi_71_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_72.tif",
+            "label": "test/gi_mibi_72_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_79.tif",
+            "label": "test/gi_mibi_79_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_84.tif",
+            "label": "test/gi_mibi_84_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_86.tif",
+            "label": "test/gi_mibi_86_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_87.tif",
+            "label": "test/gi_mibi_87_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_94.tif",
+            "label": "test/gi_mibi_94_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_96.tif",
+            "label": "test/gi_mibi_96_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_101.tif",
+            "label": "test/gi_mibi_101_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_105.tif",
+            "label": "test/gi_mibi_105_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_106.tif",
+            "label": "test/gi_mibi_106_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_113.tif",
+            "label": "test/gi_mibi_113_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_117.tif",
+            "label": "test/gi_mibi_117_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_124.tif",
+            "label": "test/gi_mibi_124_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_129.tif",
+            "label": "test/gi_mibi_129_masks.tif"
+        },
+        {
+            "image": "test/gi_mibi_133.tif",
+            "label": "test/gi_mibi_133_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/tissuenet_gi_mxif_datalist.json b/vista2d/datalists/tissuenet_gi_mxif_datalist.json
new file mode 100644
index 0000000..a684ff1
--- /dev/null
+++ b/vista2d/datalists/tissuenet_gi_mxif_datalist.json
@@ -0,0 +1,5204 @@
+{
+    "training": [
+        {
+            "image": "val/gi_mxif_0.tif",
+            "label": "val/gi_mxif_0_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_4.tif",
+            "label": "val/gi_mxif_4_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_11.tif",
+            "label": "val/gi_mxif_11_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_18.tif",
+            "label": "val/gi_mxif_18_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_19.tif",
+            "label": "val/gi_mxif_19_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_29.tif",
+            "label": "val/gi_mxif_29_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_31.tif",
+            "label": "val/gi_mxif_31_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_33.tif",
+            "label": "val/gi_mxif_33_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_34.tif",
+            "label": "val/gi_mxif_34_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_35.tif",
+            "label": "val/gi_mxif_35_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_38.tif",
+            "label": "val/gi_mxif_38_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_39.tif",
+            "label": "val/gi_mxif_39_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_44.tif",
+            "label": "val/gi_mxif_44_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_51.tif",
+            "label": "val/gi_mxif_51_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_58.tif",
+            "label": "val/gi_mxif_58_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_67.tif",
+            "label": "val/gi_mxif_67_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_69.tif",
+            "label": "val/gi_mxif_69_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_73.tif",
+            "label": "val/gi_mxif_73_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_85.tif",
+            "label": "val/gi_mxif_85_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_89.tif",
+            "label": "val/gi_mxif_89_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_90.tif",
+            "label": "val/gi_mxif_90_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_99.tif",
+            "label": "val/gi_mxif_99_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_102.tif",
+            "label": "val/gi_mxif_102_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_106.tif",
+            "label": "val/gi_mxif_106_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_114.tif",
+            "label": "val/gi_mxif_114_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_117.tif",
+            "label": "val/gi_mxif_117_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_119.tif",
+            "label": "val/gi_mxif_119_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_120.tif",
+            "label": "val/gi_mxif_120_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_127.tif",
+            "label": "val/gi_mxif_127_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_138.tif",
+            "label": "val/gi_mxif_138_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_147.tif",
+            "label": "val/gi_mxif_147_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_169.tif",
+            "label": "val/gi_mxif_169_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_170.tif",
+            "label": "val/gi_mxif_170_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_172.tif",
+            "label": "val/gi_mxif_172_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_174.tif",
+            "label": "val/gi_mxif_174_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_176.tif",
+            "label": "val/gi_mxif_176_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_179.tif",
+            "label": "val/gi_mxif_179_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_188.tif",
+            "label": "val/gi_mxif_188_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_196.tif",
+            "label": "val/gi_mxif_196_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_199.tif",
+            "label": "val/gi_mxif_199_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_209.tif",
+            "label": "val/gi_mxif_209_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_210.tif",
+            "label": "val/gi_mxif_210_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_211.tif",
+            "label": "val/gi_mxif_211_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_225.tif",
+            "label": "val/gi_mxif_225_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_228.tif",
+            "label": "val/gi_mxif_228_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_231.tif",
+            "label": "val/gi_mxif_231_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_233.tif",
+            "label": "val/gi_mxif_233_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_5.tif",
+            "label": "val/gi_mxif_5_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_14.tif",
+            "label": "val/gi_mxif_14_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_16.tif",
+            "label": "val/gi_mxif_16_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_17.tif",
+            "label": "val/gi_mxif_17_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_21.tif",
+            "label": "val/gi_mxif_21_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_27.tif",
+            "label": "val/gi_mxif_27_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_28.tif",
+            "label": "val/gi_mxif_28_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_40.tif",
+            "label": "val/gi_mxif_40_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_42.tif",
+            "label": "val/gi_mxif_42_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_47.tif",
+            "label": "val/gi_mxif_47_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_48.tif",
+            "label": "val/gi_mxif_48_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_53.tif",
+            "label": "val/gi_mxif_53_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_55.tif",
+            "label": "val/gi_mxif_55_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_56.tif",
+            "label": "val/gi_mxif_56_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_59.tif",
+            "label": "val/gi_mxif_59_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_62.tif",
+            "label": "val/gi_mxif_62_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_78.tif",
+            "label": "val/gi_mxif_78_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_84.tif",
+            "label": "val/gi_mxif_84_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_88.tif",
+            "label": "val/gi_mxif_88_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_91.tif",
+            "label": "val/gi_mxif_91_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_93.tif",
+            "label": "val/gi_mxif_93_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_95.tif",
+            "label": "val/gi_mxif_95_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_108.tif",
+            "label": "val/gi_mxif_108_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_110.tif",
+            "label": "val/gi_mxif_110_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_123.tif",
+            "label": "val/gi_mxif_123_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_132.tif",
+            "label": "val/gi_mxif_132_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_145.tif",
+            "label": "val/gi_mxif_145_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_152.tif",
+            "label": "val/gi_mxif_152_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_154.tif",
+            "label": "val/gi_mxif_154_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/gi_mxif_158.tif",
+            "label": "val/gi_mxif_158_masks.tif",
+            "fold": 0
+        },
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+            "label": "test/gi_mxif_59_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_60.tif",
+            "label": "test/gi_mxif_60_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_62.tif",
+            "label": "test/gi_mxif_62_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_65.tif",
+            "label": "test/gi_mxif_65_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_68.tif",
+            "label": "test/gi_mxif_68_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_73.tif",
+            "label": "test/gi_mxif_73_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_81.tif",
+            "label": "test/gi_mxif_81_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_3.tif",
+            "label": "test/gi_mxif_3_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_4.tif",
+            "label": "test/gi_mxif_4_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_15.tif",
+            "label": "test/gi_mxif_15_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_21.tif",
+            "label": "test/gi_mxif_21_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_26.tif",
+            "label": "test/gi_mxif_26_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_30.tif",
+            "label": "test/gi_mxif_30_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_42.tif",
+            "label": "test/gi_mxif_42_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_46.tif",
+            "label": "test/gi_mxif_46_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_47.tif",
+            "label": "test/gi_mxif_47_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_51.tif",
+            "label": "test/gi_mxif_51_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_54.tif",
+            "label": "test/gi_mxif_54_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_55.tif",
+            "label": "test/gi_mxif_55_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_61.tif",
+            "label": "test/gi_mxif_61_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_70.tif",
+            "label": "test/gi_mxif_70_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_74.tif",
+            "label": "test/gi_mxif_74_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_78.tif",
+            "label": "test/gi_mxif_78_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_85.tif",
+            "label": "test/gi_mxif_85_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_0.tif",
+            "label": "test/gi_mxif_0_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_7.tif",
+            "label": "test/gi_mxif_7_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_8.tif",
+            "label": "test/gi_mxif_8_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_11.tif",
+            "label": "test/gi_mxif_11_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_13.tif",
+            "label": "test/gi_mxif_13_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_14.tif",
+            "label": "test/gi_mxif_14_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_17.tif",
+            "label": "test/gi_mxif_17_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_22.tif",
+            "label": "test/gi_mxif_22_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_24.tif",
+            "label": "test/gi_mxif_24_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_28.tif",
+            "label": "test/gi_mxif_28_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_29.tif",
+            "label": "test/gi_mxif_29_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_41.tif",
+            "label": "test/gi_mxif_41_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_57.tif",
+            "label": "test/gi_mxif_57_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_77.tif",
+            "label": "test/gi_mxif_77_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_80.tif",
+            "label": "test/gi_mxif_80_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_83.tif",
+            "label": "test/gi_mxif_83_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_84.tif",
+            "label": "test/gi_mxif_84_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_1.tif",
+            "label": "test/gi_mxif_1_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_5.tif",
+            "label": "test/gi_mxif_5_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_6.tif",
+            "label": "test/gi_mxif_6_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_9.tif",
+            "label": "test/gi_mxif_9_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_12.tif",
+            "label": "test/gi_mxif_12_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_16.tif",
+            "label": "test/gi_mxif_16_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_18.tif",
+            "label": "test/gi_mxif_18_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_20.tif",
+            "label": "test/gi_mxif_20_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_25.tif",
+            "label": "test/gi_mxif_25_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_37.tif",
+            "label": "test/gi_mxif_37_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_50.tif",
+            "label": "test/gi_mxif_50_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_64.tif",
+            "label": "test/gi_mxif_64_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_71.tif",
+            "label": "test/gi_mxif_71_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_72.tif",
+            "label": "test/gi_mxif_72_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_75.tif",
+            "label": "test/gi_mxif_75_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_76.tif",
+            "label": "test/gi_mxif_76_masks.tif"
+        },
+        {
+            "image": "test/gi_mxif_79.tif",
+            "label": "test/gi_mxif_79_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/tissuenet_immune_cycif_datalist.json b/vista2d/datalists/tissuenet_immune_cycif_datalist.json
new file mode 100644
index 0000000..210e28d
--- /dev/null
+++ b/vista2d/datalists/tissuenet_immune_cycif_datalist.json
@@ -0,0 +1,10411 @@
+{
+    "training": [
+        {
+            "image": "val/immune_cycif_4.tif",
+            "label": "val/immune_cycif_4_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_5.tif",
+            "label": "val/immune_cycif_5_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_6.tif",
+            "label": "val/immune_cycif_6_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_11.tif",
+            "label": "val/immune_cycif_11_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_17.tif",
+            "label": "val/immune_cycif_17_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_29.tif",
+            "label": "val/immune_cycif_29_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_31.tif",
+            "label": "val/immune_cycif_31_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_34.tif",
+            "label": "val/immune_cycif_34_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_47.tif",
+            "label": "val/immune_cycif_47_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_58.tif",
+            "label": "val/immune_cycif_58_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_62.tif",
+            "label": "val/immune_cycif_62_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_65.tif",
+            "label": "val/immune_cycif_65_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_66.tif",
+            "label": "val/immune_cycif_66_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_67.tif",
+            "label": "val/immune_cycif_67_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_90.tif",
+            "label": "val/immune_cycif_90_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_92.tif",
+            "label": "val/immune_cycif_92_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_102.tif",
+            "label": "val/immune_cycif_102_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_107.tif",
+            "label": "val/immune_cycif_107_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_110.tif",
+            "label": "val/immune_cycif_110_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_119.tif",
+            "label": "val/immune_cycif_119_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_128.tif",
+            "label": "val/immune_cycif_128_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_132.tif",
+            "label": "val/immune_cycif_132_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_146.tif",
+            "label": "val/immune_cycif_146_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_147.tif",
+            "label": "val/immune_cycif_147_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_159.tif",
+            "label": "val/immune_cycif_159_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_162.tif",
+            "label": "val/immune_cycif_162_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_172.tif",
+            "label": "val/immune_cycif_172_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_179.tif",
+            "label": "val/immune_cycif_179_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_180.tif",
+            "label": "val/immune_cycif_180_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_185.tif",
+            "label": "val/immune_cycif_185_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_187.tif",
+            "label": "val/immune_cycif_187_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_189.tif",
+            "label": "val/immune_cycif_189_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_192.tif",
+            "label": "val/immune_cycif_192_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_207.tif",
+            "label": "val/immune_cycif_207_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_208.tif",
+            "label": "val/immune_cycif_208_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_213.tif",
+            "label": "val/immune_cycif_213_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_214.tif",
+            "label": "val/immune_cycif_214_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_225.tif",
+            "label": "val/immune_cycif_225_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_230.tif",
+            "label": "val/immune_cycif_230_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_232.tif",
+            "label": "val/immune_cycif_232_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_242.tif",
+            "label": "val/immune_cycif_242_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_245.tif",
+            "label": "val/immune_cycif_245_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_247.tif",
+            "label": "val/immune_cycif_247_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_250.tif",
+            "label": "val/immune_cycif_250_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_260.tif",
+            "label": "val/immune_cycif_260_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_270.tif",
+            "label": "val/immune_cycif_270_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_272.tif",
+            "label": "val/immune_cycif_272_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_275.tif",
+            "label": "val/immune_cycif_275_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_277.tif",
+            "label": "val/immune_cycif_277_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_284.tif",
+            "label": "val/immune_cycif_284_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_285.tif",
+            "label": "val/immune_cycif_285_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_291.tif",
+            "label": "val/immune_cycif_291_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_292.tif",
+            "label": "val/immune_cycif_292_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_295.tif",
+            "label": "val/immune_cycif_295_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_304.tif",
+            "label": "val/immune_cycif_304_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_310.tif",
+            "label": "val/immune_cycif_310_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_311.tif",
+            "label": "val/immune_cycif_311_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_312.tif",
+            "label": "val/immune_cycif_312_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_314.tif",
+            "label": "val/immune_cycif_314_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_315.tif",
+            "label": "val/immune_cycif_315_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_323.tif",
+            "label": "val/immune_cycif_323_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_324.tif",
+            "label": "val/immune_cycif_324_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_329.tif",
+            "label": "val/immune_cycif_329_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_330.tif",
+            "label": "val/immune_cycif_330_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_331.tif",
+            "label": "val/immune_cycif_331_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_334.tif",
+            "label": "val/immune_cycif_334_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_344.tif",
+            "label": "val/immune_cycif_344_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_346.tif",
+            "label": "val/immune_cycif_346_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_358.tif",
+            "label": "val/immune_cycif_358_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_361.tif",
+            "label": "val/immune_cycif_361_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_372.tif",
+            "label": "val/immune_cycif_372_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_379.tif",
+            "label": "val/immune_cycif_379_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_385.tif",
+            "label": "val/immune_cycif_385_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_388.tif",
+            "label": "val/immune_cycif_388_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_400.tif",
+            "label": "val/immune_cycif_400_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_403.tif",
+            "label": "val/immune_cycif_403_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_406.tif",
+            "label": "val/immune_cycif_406_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_408.tif",
+            "label": "val/immune_cycif_408_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_411.tif",
+            "label": "val/immune_cycif_411_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_416.tif",
+            "label": "val/immune_cycif_416_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_420.tif",
+            "label": "val/immune_cycif_420_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_424.tif",
+            "label": "val/immune_cycif_424_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_428.tif",
+            "label": "val/immune_cycif_428_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_430.tif",
+            "label": "val/immune_cycif_430_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_432.tif",
+            "label": "val/immune_cycif_432_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_cycif_435.tif",
+            "label": "val/immune_cycif_435_masks.tif",
+            "fold": 0
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+            "label": "test/immune_cycif_1_masks.tif"
+        },
+        {
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+            "label": "test/immune_cycif_7_masks.tif"
+        },
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+            "label": "test/immune_cycif_8_masks.tif"
+        },
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+            "label": "test/immune_cycif_20_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_22.tif",
+            "label": "test/immune_cycif_22_masks.tif"
+        },
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+            "label": "test/immune_cycif_25_masks.tif"
+        },
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+            "label": "test/immune_cycif_37_masks.tif"
+        },
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+            "label": "test/immune_cycif_50_masks.tif"
+        },
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+            "label": "test/immune_cycif_57_masks.tif"
+        },
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+            "label": "test/immune_cycif_60_masks.tif"
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+            "label": "test/immune_cycif_61_masks.tif"
+        },
+        {
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+            "label": "test/immune_cycif_63_masks.tif"
+        },
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+            "label": "test/immune_cycif_68_masks.tif"
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+            "label": "test/immune_cycif_71_masks.tif"
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+            "label": "test/immune_cycif_72_masks.tif"
+        },
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+            "label": "test/immune_cycif_79_masks.tif"
+        },
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+            "label": "test/immune_cycif_86_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_96.tif",
+            "label": "test/immune_cycif_96_masks.tif"
+        },
+        {
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+            "label": "test/immune_cycif_101_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_115.tif",
+            "label": "test/immune_cycif_115_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_128.tif",
+            "label": "test/immune_cycif_128_masks.tif"
+        },
+        {
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+            "label": "test/immune_cycif_129_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_133.tif",
+            "label": "test/immune_cycif_133_masks.tif"
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+            "label": "test/immune_cycif_134_masks.tif"
+        },
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+            "label": "test/immune_cycif_137_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_139.tif",
+            "label": "test/immune_cycif_139_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_140.tif",
+            "label": "test/immune_cycif_140_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_141.tif",
+            "label": "test/immune_cycif_141_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_142.tif",
+            "label": "test/immune_cycif_142_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_144.tif",
+            "label": "test/immune_cycif_144_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_146.tif",
+            "label": "test/immune_cycif_146_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_152.tif",
+            "label": "test/immune_cycif_152_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_156.tif",
+            "label": "test/immune_cycif_156_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_157.tif",
+            "label": "test/immune_cycif_157_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_171.tif",
+            "label": "test/immune_cycif_171_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/tissuenet_immune_datalist.json b/vista2d/datalists/tissuenet_immune_datalist.json
new file mode 100644
index 0000000..d45428f
--- /dev/null
+++ b/vista2d/datalists/tissuenet_immune_datalist.json
@@ -0,0 +1,13220 @@
+{
+    "training": [
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+            "label": "val/immune_cycif_0_masks.tif",
+            "fold": 0
+        },
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+            "label": "val/immune_cycif_11_masks.tif",
+            "fold": 0
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+        {
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+            "label": "val/immune_cycif_13_masks.tif",
+            "fold": 0
+        },
+        {
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+            "label": "val/immune_cycif_17_masks.tif",
+            "fold": 0
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+        {
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+            "label": "val/immune_cycif_23_masks.tif",
+            "fold": 0
+        },
+        {
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+            "label": "val/immune_cycif_29_masks.tif",
+            "fold": 0
+        },
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+        {
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+            "label": "val/immune_cycif_41_masks.tif",
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+            "label": "val/immune_cycif_47_masks.tif",
+            "fold": 0
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+            "label": "val/immune_cycif_111_masks.tif",
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+            "image": "val/immune_cycif_120.tif",
+            "label": "val/immune_cycif_120_masks.tif",
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+            "label": "val/immune_cycif_160_masks.tif",
+            "fold": 0
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+            "label": "val/immune_cycif_162_masks.tif",
+            "fold": 0
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+            "fold": 0
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+            "label": "val/immune_cycif_172_masks.tif",
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+            "label": "val/immune_cycif_179_masks.tif",
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+            "label": "val/immune_cycif_195_masks.tif",
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+        },
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+            "label": "val/immune_cycif_224_masks.tif",
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+            "label": "val/immune_cycif_242_masks.tif",
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+        {
+            "image": "val/immune_cycif_245.tif",
+            "label": "val/immune_cycif_245_masks.tif",
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+            "label": "val/immune_cycif_247_masks.tif",
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+        {
+            "image": "val/immune_cycif_250.tif",
+            "label": "val/immune_cycif_250_masks.tif",
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+            "image": "val/immune_cycif_262.tif",
+            "label": "val/immune_cycif_262_masks.tif",
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+            "label": "val/immune_cycif_286_masks.tif",
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+            "label": "val/immune_cycif_360_masks.tif",
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+            "label": "val/immune_vectra_27_masks.tif",
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+            "label": "test/immune_cycif_87_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_92.tif",
+            "label": "test/immune_cycif_92_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_100.tif",
+            "label": "test/immune_cycif_100_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_104.tif",
+            "label": "test/immune_cycif_104_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_109.tif",
+            "label": "test/immune_cycif_109_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_111.tif",
+            "label": "test/immune_cycif_111_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_113.tif",
+            "label": "test/immune_cycif_113_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_125.tif",
+            "label": "test/immune_cycif_125_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_126.tif",
+            "label": "test/immune_cycif_126_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_135.tif",
+            "label": "test/immune_cycif_135_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_136.tif",
+            "label": "test/immune_cycif_136_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_143.tif",
+            "label": "test/immune_cycif_143_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_153.tif",
+            "label": "test/immune_cycif_153_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_155.tif",
+            "label": "test/immune_cycif_155_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_161.tif",
+            "label": "test/immune_cycif_161_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_170.tif",
+            "label": "test/immune_cycif_170_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_2.tif",
+            "label": "test/immune_mibi_2_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_7.tif",
+            "label": "test/immune_mibi_7_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_8.tif",
+            "label": "test/immune_mibi_8_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_16.tif",
+            "label": "test/immune_mibi_16_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_0.tif",
+            "label": "test/immune_vectra_0_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_2.tif",
+            "label": "test/immune_vectra_2_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_3.tif",
+            "label": "test/immune_vectra_3_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_21.tif",
+            "label": "test/immune_vectra_21_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_22.tif",
+            "label": "test/immune_vectra_22_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_1.tif",
+            "label": "test/immune_cycif_1_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_7.tif",
+            "label": "test/immune_cycif_7_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_8.tif",
+            "label": "test/immune_cycif_8_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_20.tif",
+            "label": "test/immune_cycif_20_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_22.tif",
+            "label": "test/immune_cycif_22_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_25.tif",
+            "label": "test/immune_cycif_25_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_30.tif",
+            "label": "test/immune_cycif_30_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_37.tif",
+            "label": "test/immune_cycif_37_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_50.tif",
+            "label": "test/immune_cycif_50_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_57.tif",
+            "label": "test/immune_cycif_57_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_60.tif",
+            "label": "test/immune_cycif_60_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_61.tif",
+            "label": "test/immune_cycif_61_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_63.tif",
+            "label": "test/immune_cycif_63_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_68.tif",
+            "label": "test/immune_cycif_68_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_71.tif",
+            "label": "test/immune_cycif_71_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_72.tif",
+            "label": "test/immune_cycif_72_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_79.tif",
+            "label": "test/immune_cycif_79_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_86.tif",
+            "label": "test/immune_cycif_86_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_96.tif",
+            "label": "test/immune_cycif_96_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_101.tif",
+            "label": "test/immune_cycif_101_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_115.tif",
+            "label": "test/immune_cycif_115_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_121.tif",
+            "label": "test/immune_cycif_121_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_128.tif",
+            "label": "test/immune_cycif_128_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_129.tif",
+            "label": "test/immune_cycif_129_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_131.tif",
+            "label": "test/immune_cycif_131_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_133.tif",
+            "label": "test/immune_cycif_133_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_134.tif",
+            "label": "test/immune_cycif_134_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_137.tif",
+            "label": "test/immune_cycif_137_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_139.tif",
+            "label": "test/immune_cycif_139_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_140.tif",
+            "label": "test/immune_cycif_140_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_141.tif",
+            "label": "test/immune_cycif_141_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_142.tif",
+            "label": "test/immune_cycif_142_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_144.tif",
+            "label": "test/immune_cycif_144_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_146.tif",
+            "label": "test/immune_cycif_146_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_149.tif",
+            "label": "test/immune_cycif_149_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_156.tif",
+            "label": "test/immune_cycif_156_masks.tif"
+        },
+        {
+            "image": "test/immune_cycif_157.tif",
+            "label": "test/immune_cycif_157_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_0.tif",
+            "label": "test/immune_mibi_0_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_3.tif",
+            "label": "test/immune_mibi_3_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_17.tif",
+            "label": "test/immune_mibi_17_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_8.tif",
+            "label": "test/immune_vectra_8_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_9.tif",
+            "label": "test/immune_vectra_9_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_17.tif",
+            "label": "test/immune_vectra_17_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_24.tif",
+            "label": "test/immune_vectra_24_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_31.tif",
+            "label": "test/immune_vectra_31_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_32.tif",
+            "label": "test/immune_vectra_32_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/tissuenet_immune_mibi_datalist.json b/vista2d/datalists/tissuenet_immune_mibi_datalist.json
new file mode 100644
index 0000000..3676147
--- /dev/null
+++ b/vista2d/datalists/tissuenet_immune_mibi_datalist.json
@@ -0,0 +1,891 @@
+{
+    "training": [
+        {
+            "image": "val/immune_mibi_2.tif",
+            "label": "val/immune_mibi_2_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_3.tif",
+            "label": "val/immune_mibi_3_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_19.tif",
+            "label": "val/immune_mibi_19_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_21.tif",
+            "label": "val/immune_mibi_21_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_22.tif",
+            "label": "val/immune_mibi_22_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_30.tif",
+            "label": "val/immune_mibi_30_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_34.tif",
+            "label": "val/immune_mibi_34_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_35.tif",
+            "label": "val/immune_mibi_35_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_41.tif",
+            "label": "val/immune_mibi_41_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_17.tif",
+            "label": "val/immune_mibi_17_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_23.tif",
+            "label": "val/immune_mibi_23_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_29.tif",
+            "label": "val/immune_mibi_29_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_31.tif",
+            "label": "val/immune_mibi_31_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_32.tif",
+            "label": "val/immune_mibi_32_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_33.tif",
+            "label": "val/immune_mibi_33_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_36.tif",
+            "label": "val/immune_mibi_36_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_40.tif",
+            "label": "val/immune_mibi_40_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_44.tif",
+            "label": "val/immune_mibi_44_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_4.tif",
+            "label": "val/immune_mibi_4_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_10.tif",
+            "label": "val/immune_mibi_10_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_14.tif",
+            "label": "val/immune_mibi_14_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_20.tif",
+            "label": "val/immune_mibi_20_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_24.tif",
+            "label": "val/immune_mibi_24_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_26.tif",
+            "label": "val/immune_mibi_26_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_27.tif",
+            "label": "val/immune_mibi_27_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_38.tif",
+            "label": "val/immune_mibi_38_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_39.tif",
+            "label": "val/immune_mibi_39_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_1.tif",
+            "label": "val/immune_mibi_1_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_6.tif",
+            "label": "val/immune_mibi_6_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_7.tif",
+            "label": "val/immune_mibi_7_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_13.tif",
+            "label": "val/immune_mibi_13_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_16.tif",
+            "label": "val/immune_mibi_16_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_18.tif",
+            "label": "val/immune_mibi_18_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_25.tif",
+            "label": "val/immune_mibi_25_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_28.tif",
+            "label": "val/immune_mibi_28_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_42.tif",
+            "label": "val/immune_mibi_42_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_0.tif",
+            "label": "val/immune_mibi_0_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_5.tif",
+            "label": "val/immune_mibi_5_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_8.tif",
+            "label": "val/immune_mibi_8_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_9.tif",
+            "label": "val/immune_mibi_9_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_11.tif",
+            "label": "val/immune_mibi_11_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_12.tif",
+            "label": "val/immune_mibi_12_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_15.tif",
+            "label": "val/immune_mibi_15_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_37.tif",
+            "label": "val/immune_mibi_37_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_mibi_43.tif",
+            "label": "val/immune_mibi_43_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "train/immune_mibi_0_2.tif",
+            "label": "train/immune_mibi_0_2_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_4_1.tif",
+            "label": "train/immune_mibi_4_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_8_3.tif",
+            "label": "train/immune_mibi_8_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_9_2.tif",
+            "label": "train/immune_mibi_9_2_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_10_2.tif",
+            "label": "train/immune_mibi_10_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_11_0.tif",
+            "label": "train/immune_mibi_11_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_13_1.tif",
+            "label": "train/immune_mibi_13_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_13_2.tif",
+            "label": "train/immune_mibi_13_2_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_14_0.tif",
+            "label": "train/immune_mibi_14_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_14_2.tif",
+            "label": "train/immune_mibi_14_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_16_1.tif",
+            "label": "train/immune_mibi_16_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_17_1.tif",
+            "label": "train/immune_mibi_17_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_18_1.tif",
+            "label": "train/immune_mibi_18_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_19_2.tif",
+            "label": "train/immune_mibi_19_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_20_0.tif",
+            "label": "train/immune_mibi_20_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_23_2.tif",
+            "label": "train/immune_mibi_23_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_23_3.tif",
+            "label": "train/immune_mibi_23_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_24_0.tif",
+            "label": "train/immune_mibi_24_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_24_1.tif",
+            "label": "train/immune_mibi_24_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_24_3.tif",
+            "label": "train/immune_mibi_24_3_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_25_2.tif",
+            "label": "train/immune_mibi_25_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_26_0.tif",
+            "label": "train/immune_mibi_26_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_27_1.tif",
+            "label": "train/immune_mibi_27_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_28_2.tif",
+            "label": "train/immune_mibi_28_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_2_2.tif",
+            "label": "train/immune_mibi_2_2_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_4_3.tif",
+            "label": "train/immune_mibi_4_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_6_3.tif",
+            "label": "train/immune_mibi_6_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_7_3.tif",
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+            "label": "train/immune_mibi_8_1_masks.tif",
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+            "label": "train/immune_mibi_20_2_masks.tif",
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+            "image": "train/immune_mibi_20_3.tif",
+            "label": "train/immune_mibi_20_3_masks.tif",
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+        },
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+            "label": "train/immune_mibi_22_0_masks.tif",
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+            "image": "train/immune_mibi_22_3.tif",
+            "label": "train/immune_mibi_22_3_masks.tif",
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+        },
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+            "image": "train/immune_mibi_23_0.tif",
+            "label": "train/immune_mibi_23_0_masks.tif",
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+        },
+        {
+            "image": "train/immune_mibi_23_1.tif",
+            "label": "train/immune_mibi_23_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_28_3.tif",
+            "label": "train/immune_mibi_28_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_1_0.tif",
+            "label": "train/immune_mibi_1_0_masks.tif",
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+        },
+        {
+            "image": "train/immune_mibi_3_3.tif",
+            "label": "train/immune_mibi_3_3_masks.tif",
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+        },
+        {
+            "image": "train/immune_mibi_5_1.tif",
+            "label": "train/immune_mibi_5_1_masks.tif",
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+        },
+        {
+            "image": "train/immune_mibi_5_3.tif",
+            "label": "train/immune_mibi_5_3_masks.tif",
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+            "label": "train/immune_mibi_6_0_masks.tif",
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+            "label": "train/immune_mibi_6_2_masks.tif",
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+        },
+        {
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+            "label": "train/immune_mibi_8_2_masks.tif",
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+        },
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+            "label": "train/immune_mibi_10_1_masks.tif",
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+        },
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+            "label": "train/immune_mibi_10_3_masks.tif",
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+        },
+        {
+            "image": "train/immune_mibi_11_1.tif",
+            "label": "train/immune_mibi_11_1_masks.tif",
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+        },
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+            "label": "train/immune_mibi_12_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_12_3.tif",
+            "label": "train/immune_mibi_12_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_13_0.tif",
+            "label": "train/immune_mibi_13_0_masks.tif",
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+        },
+        {
+            "image": "train/immune_mibi_15_2.tif",
+            "label": "train/immune_mibi_15_2_masks.tif",
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+        },
+        {
+            "image": "train/immune_mibi_18_2.tif",
+            "label": "train/immune_mibi_18_2_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_20_1.tif",
+            "label": "train/immune_mibi_20_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_21_1.tif",
+            "label": "train/immune_mibi_21_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_24_2.tif",
+            "label": "train/immune_mibi_24_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_25_1.tif",
+            "label": "train/immune_mibi_25_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_26_2.tif",
+            "label": "train/immune_mibi_26_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_27_0.tif",
+            "label": "train/immune_mibi_27_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_27_2.tif",
+            "label": "train/immune_mibi_27_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_28_1.tif",
+            "label": "train/immune_mibi_28_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_0_0.tif",
+            "label": "train/immune_mibi_0_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_0_3.tif",
+            "label": "train/immune_mibi_0_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_1_3.tif",
+            "label": "train/immune_mibi_1_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_2_0.tif",
+            "label": "train/immune_mibi_2_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_3_1.tif",
+            "label": "train/immune_mibi_3_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_5_2.tif",
+            "label": "train/immune_mibi_5_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_7_2.tif",
+            "label": "train/immune_mibi_7_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_11_3.tif",
+            "label": "train/immune_mibi_11_3_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_14_1.tif",
+            "label": "train/immune_mibi_14_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_15_0.tif",
+            "label": "train/immune_mibi_15_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_15_1.tif",
+            "label": "train/immune_mibi_15_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_15_3.tif",
+            "label": "train/immune_mibi_15_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_17_0.tif",
+            "label": "train/immune_mibi_17_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_17_2.tif",
+            "label": "train/immune_mibi_17_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_21_2.tif",
+            "label": "train/immune_mibi_21_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_21_3.tif",
+            "label": "train/immune_mibi_21_3_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_22_1.tif",
+            "label": "train/immune_mibi_22_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_22_2.tif",
+            "label": "train/immune_mibi_22_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_25_0.tif",
+            "label": "train/immune_mibi_25_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_25_3.tif",
+            "label": "train/immune_mibi_25_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_26_1.tif",
+            "label": "train/immune_mibi_26_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_27_3.tif",
+            "label": "train/immune_mibi_27_3_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_28_0.tif",
+            "label": "train/immune_mibi_28_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_0_1.tif",
+            "label": "train/immune_mibi_0_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_1_1.tif",
+            "label": "train/immune_mibi_1_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_1_2.tif",
+            "label": "train/immune_mibi_1_2_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_2_1.tif",
+            "label": "train/immune_mibi_2_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_2_3.tif",
+            "label": "train/immune_mibi_2_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_3_0.tif",
+            "label": "train/immune_mibi_3_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_3_2.tif",
+            "label": "train/immune_mibi_3_2_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_4_0.tif",
+            "label": "train/immune_mibi_4_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_4_2.tif",
+            "label": "train/immune_mibi_4_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_5_0.tif",
+            "label": "train/immune_mibi_5_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_6_1.tif",
+            "label": "train/immune_mibi_6_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_7_0.tif",
+            "label": "train/immune_mibi_7_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_7_1.tif",
+            "label": "train/immune_mibi_7_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_9_1.tif",
+            "label": "train/immune_mibi_9_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_12_2.tif",
+            "label": "train/immune_mibi_12_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_mibi_16_0.tif",
+            "label": "train/immune_mibi_16_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_17_3.tif",
+            "label": "train/immune_mibi_17_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_18_0.tif",
+            "label": "train/immune_mibi_18_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_mibi_18_3.tif",
+            "label": "train/immune_mibi_18_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_19_0.tif",
+            "label": "train/immune_mibi_19_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_19_3.tif",
+            "label": "train/immune_mibi_19_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_mibi_21_0.tif",
+            "label": "train/immune_mibi_21_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_mibi_26_3.tif",
+            "label": "train/immune_mibi_26_3_masks.tif",
+            "fold": 2
+        }
+    ],
+    "testing": [
+        {
+            "image": "test/immune_mibi_3.tif",
+            "label": "test/immune_mibi_3_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_6.tif",
+            "label": "test/immune_mibi_6_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_10.tif",
+            "label": "test/immune_mibi_10_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_16.tif",
+            "label": "test/immune_mibi_16_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_2.tif",
+            "label": "test/immune_mibi_2_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_4.tif",
+            "label": "test/immune_mibi_4_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_14.tif",
+            "label": "test/immune_mibi_14_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_17.tif",
+            "label": "test/immune_mibi_17_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_0.tif",
+            "label": "test/immune_mibi_0_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_1.tif",
+            "label": "test/immune_mibi_1_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_7.tif",
+            "label": "test/immune_mibi_7_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_13.tif",
+            "label": "test/immune_mibi_13_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_9.tif",
+            "label": "test/immune_mibi_9_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_15.tif",
+            "label": "test/immune_mibi_15_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_18.tif",
+            "label": "test/immune_mibi_18_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_19.tif",
+            "label": "test/immune_mibi_19_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_5.tif",
+            "label": "test/immune_mibi_5_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_8.tif",
+            "label": "test/immune_mibi_8_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_11.tif",
+            "label": "test/immune_mibi_11_masks.tif"
+        },
+        {
+            "image": "test/immune_mibi_12.tif",
+            "label": "test/immune_mibi_12_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/tissuenet_immune_vectra_datalist.json b/vista2d/datalists/tissuenet_immune_vectra_datalist.json
new file mode 100644
index 0000000..889e6b5
--- /dev/null
+++ b/vista2d/datalists/tissuenet_immune_vectra_datalist.json
@@ -0,0 +1,1930 @@
+{
+    "training": [
+        {
+            "image": "val/immune_vectra_10.tif",
+            "label": "val/immune_vectra_10_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_17.tif",
+            "label": "val/immune_vectra_17_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_31.tif",
+            "label": "val/immune_vectra_31_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_32.tif",
+            "label": "val/immune_vectra_32_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_33.tif",
+            "label": "val/immune_vectra_33_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_36.tif",
+            "label": "val/immune_vectra_36_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_39.tif",
+            "label": "val/immune_vectra_39_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_44.tif",
+            "label": "val/immune_vectra_44_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_51.tif",
+            "label": "val/immune_vectra_51_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_52.tif",
+            "label": "val/immune_vectra_52_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_56.tif",
+            "label": "val/immune_vectra_56_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_65.tif",
+            "label": "val/immune_vectra_65_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_69.tif",
+            "label": "val/immune_vectra_69_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_78.tif",
+            "label": "val/immune_vectra_78_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_80.tif",
+            "label": "val/immune_vectra_80_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_81.tif",
+            "label": "val/immune_vectra_81_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_82.tif",
+            "label": "val/immune_vectra_82_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_84.tif",
+            "label": "val/immune_vectra_84_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_92.tif",
+            "label": "val/immune_vectra_92_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_93.tif",
+            "label": "val/immune_vectra_93_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_2.tif",
+            "label": "val/immune_vectra_2_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_19.tif",
+            "label": "val/immune_vectra_19_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/immune_vectra_23.tif",
+            "label": "val/immune_vectra_23_masks.tif",
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+            "label": "train/immune_vectra_27_3_masks.tif",
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+        },
+        {
+            "image": "train/immune_vectra_28_1.tif",
+            "label": "train/immune_vectra_28_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_vectra_31_1.tif",
+            "label": "train/immune_vectra_31_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_31_2.tif",
+            "label": "train/immune_vectra_31_2_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_32_3.tif",
+            "label": "train/immune_vectra_32_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_33_3.tif",
+            "label": "train/immune_vectra_33_3_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_vectra_34_0.tif",
+            "label": "train/immune_vectra_34_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_35_3.tif",
+            "label": "train/immune_vectra_35_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_37_1.tif",
+            "label": "train/immune_vectra_37_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_37_3.tif",
+            "label": "train/immune_vectra_37_3_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_vectra_38_1.tif",
+            "label": "train/immune_vectra_38_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_38_3.tif",
+            "label": "train/immune_vectra_38_3_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_vectra_40_2.tif",
+            "label": "train/immune_vectra_40_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_vectra_41_1.tif",
+            "label": "train/immune_vectra_41_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_vectra_41_2.tif",
+            "label": "train/immune_vectra_41_2_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_44_1.tif",
+            "label": "train/immune_vectra_44_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_46_2.tif",
+            "label": "train/immune_vectra_46_2_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_48_2.tif",
+            "label": "train/immune_vectra_48_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_vectra_49_0.tif",
+            "label": "train/immune_vectra_49_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_vectra_49_2.tif",
+            "label": "train/immune_vectra_49_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_vectra_49_3.tif",
+            "label": "train/immune_vectra_49_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_51_3.tif",
+            "label": "train/immune_vectra_51_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_vectra_52_2.tif",
+            "label": "train/immune_vectra_52_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_vectra_54_1.tif",
+            "label": "train/immune_vectra_54_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_54_2.tif",
+            "label": "train/immune_vectra_54_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_vectra_60_0.tif",
+            "label": "train/immune_vectra_60_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_vectra_0_1.tif",
+            "label": "train/immune_vectra_0_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_vectra_1_3.tif",
+            "label": "train/immune_vectra_1_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_2_0.tif",
+            "label": "train/immune_vectra_2_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_5_0.tif",
+            "label": "train/immune_vectra_5_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_5_2.tif",
+            "label": "train/immune_vectra_5_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_vectra_6_1.tif",
+            "label": "train/immune_vectra_6_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_7_2.tif",
+            "label": "train/immune_vectra_7_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_vectra_9_1.tif",
+            "label": "train/immune_vectra_9_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_vectra_12_2.tif",
+            "label": "train/immune_vectra_12_2_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_14_1.tif",
+            "label": "train/immune_vectra_14_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_15_0.tif",
+            "label": "train/immune_vectra_15_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_vectra_15_1.tif",
+            "label": "train/immune_vectra_15_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_15_3.tif",
+            "label": "train/immune_vectra_15_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_vectra_17_0.tif",
+            "label": "train/immune_vectra_17_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_17_3.tif",
+            "label": "train/immune_vectra_17_3_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_vectra_18_0.tif",
+            "label": "train/immune_vectra_18_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_19_3.tif",
+            "label": "train/immune_vectra_19_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_21_2.tif",
+            "label": "train/immune_vectra_21_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_vectra_24_0.tif",
+            "label": "train/immune_vectra_24_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_vectra_25_1.tif",
+            "label": "train/immune_vectra_25_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_vectra_28_3.tif",
+            "label": "train/immune_vectra_28_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_30_1.tif",
+            "label": "train/immune_vectra_30_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_32_0.tif",
+            "label": "train/immune_vectra_32_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_32_1.tif",
+            "label": "train/immune_vectra_32_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_vectra_33_1.tif",
+            "label": "train/immune_vectra_33_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_33_2.tif",
+            "label": "train/immune_vectra_33_2_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_34_1.tif",
+            "label": "train/immune_vectra_34_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_34_3.tif",
+            "label": "train/immune_vectra_34_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_35_0.tif",
+            "label": "train/immune_vectra_35_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_35_1.tif",
+            "label": "train/immune_vectra_35_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_35_2.tif",
+            "label": "train/immune_vectra_35_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_vectra_36_0.tif",
+            "label": "train/immune_vectra_36_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_36_2.tif",
+            "label": "train/immune_vectra_36_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_vectra_39_0.tif",
+            "label": "train/immune_vectra_39_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_39_1.tif",
+            "label": "train/immune_vectra_39_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_vectra_43_3.tif",
+            "label": "train/immune_vectra_43_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_44_2.tif",
+            "label": "train/immune_vectra_44_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_vectra_48_0.tif",
+            "label": "train/immune_vectra_48_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_50_0.tif",
+            "label": "train/immune_vectra_50_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_vectra_50_3.tif",
+            "label": "train/immune_vectra_50_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_51_0.tif",
+            "label": "train/immune_vectra_51_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_52_1.tif",
+            "label": "train/immune_vectra_52_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_vectra_53_0.tif",
+            "label": "train/immune_vectra_53_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_53_3.tif",
+            "label": "train/immune_vectra_53_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_55_2.tif",
+            "label": "train/immune_vectra_55_2_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_55_3.tif",
+            "label": "train/immune_vectra_55_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/immune_vectra_57_0.tif",
+            "label": "train/immune_vectra_57_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/immune_vectra_58_3.tif",
+            "label": "train/immune_vectra_58_3_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_vectra_59_1.tif",
+            "label": "train/immune_vectra_59_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/immune_vectra_60_3.tif",
+            "label": "train/immune_vectra_60_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/immune_vectra_62_3.tif",
+            "label": "train/immune_vectra_62_3_masks.tif",
+            "fold": 1
+        }
+    ],
+    "testing": [
+        {
+            "image": "test/immune_vectra_3.tif",
+            "label": "test/immune_vectra_3_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_17.tif",
+            "label": "test/immune_vectra_17_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_19.tif",
+            "label": "test/immune_vectra_19_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_21.tif",
+            "label": "test/immune_vectra_21_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_23.tif",
+            "label": "test/immune_vectra_23_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_28.tif",
+            "label": "test/immune_vectra_28_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_30.tif",
+            "label": "test/immune_vectra_30_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_34.tif",
+            "label": "test/immune_vectra_34_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_4.tif",
+            "label": "test/immune_vectra_4_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_24.tif",
+            "label": "test/immune_vectra_24_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_25.tif",
+            "label": "test/immune_vectra_25_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_26.tif",
+            "label": "test/immune_vectra_26_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_27.tif",
+            "label": "test/immune_vectra_27_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_29.tif",
+            "label": "test/immune_vectra_29_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_33.tif",
+            "label": "test/immune_vectra_33_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_2.tif",
+            "label": "test/immune_vectra_2_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_10.tif",
+            "label": "test/immune_vectra_10_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_14.tif",
+            "label": "test/immune_vectra_14_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_20.tif",
+            "label": "test/immune_vectra_20_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_22.tif",
+            "label": "test/immune_vectra_22_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_31.tif",
+            "label": "test/immune_vectra_31_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_32.tif",
+            "label": "test/immune_vectra_32_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_1.tif",
+            "label": "test/immune_vectra_1_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_6.tif",
+            "label": "test/immune_vectra_6_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_7.tif",
+            "label": "test/immune_vectra_7_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_13.tif",
+            "label": "test/immune_vectra_13_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_16.tif",
+            "label": "test/immune_vectra_16_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_18.tif",
+            "label": "test/immune_vectra_18_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_35.tif",
+            "label": "test/immune_vectra_35_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_0.tif",
+            "label": "test/immune_vectra_0_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_5.tif",
+            "label": "test/immune_vectra_5_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_8.tif",
+            "label": "test/immune_vectra_8_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_9.tif",
+            "label": "test/immune_vectra_9_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_11.tif",
+            "label": "test/immune_vectra_11_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_12.tif",
+            "label": "test/immune_vectra_12_masks.tif"
+        },
+        {
+            "image": "test/immune_vectra_15.tif",
+            "label": "test/immune_vectra_15_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/tissuenet_lung_cycif_datalist.json b/vista2d/datalists/tissuenet_lung_cycif_datalist.json
new file mode 100644
index 0000000..e1d7c6a
--- /dev/null
+++ b/vista2d/datalists/tissuenet_lung_cycif_datalist.json
@@ -0,0 +1,8329 @@
+{
+    "training": [
+        {
+            "image": "val/lung_cycif_4.tif",
+            "label": "val/lung_cycif_4_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/lung_cycif_5.tif",
+            "label": "val/lung_cycif_5_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/lung_cycif_6.tif",
+            "label": "val/lung_cycif_6_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/lung_cycif_11.tif",
+            "label": "val/lung_cycif_11_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/lung_cycif_13.tif",
+            "label": "val/lung_cycif_13_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/lung_cycif_17.tif",
+            "label": "val/lung_cycif_17_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/lung_cycif_18.tif",
+            "label": "val/lung_cycif_18_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/lung_cycif_41.tif",
+            "label": "val/lung_cycif_41_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/lung_cycif_62.tif",
+            "label": "val/lung_cycif_62_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/lung_cycif_65.tif",
+            "label": "val/lung_cycif_65_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/lung_cycif_67.tif",
+            "label": "val/lung_cycif_67_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/lung_cycif_78.tif",
+            "label": "val/lung_cycif_78_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/lung_cycif_81.tif",
+            "label": "val/lung_cycif_81_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/lung_cycif_90.tif",
+            "label": "val/lung_cycif_90_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/lung_cycif_93.tif",
+            "label": "val/lung_cycif_93_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/lung_cycif_95.tif",
+            "label": "val/lung_cycif_95_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/lung_cycif_102.tif",
+            "label": "val/lung_cycif_102_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/lung_cycif_106.tif",
+            "label": "val/lung_cycif_106_masks.tif",
+            "fold": 0
+        },
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+        },
+        {
+            "image": "test/lung_cycif_145.tif",
+            "label": "test/lung_cycif_145_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_2.tif",
+            "label": "test/lung_cycif_2_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_6.tif",
+            "label": "test/lung_cycif_6_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_9.tif",
+            "label": "test/lung_cycif_9_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_10.tif",
+            "label": "test/lung_cycif_10_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_12.tif",
+            "label": "test/lung_cycif_12_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_23.tif",
+            "label": "test/lung_cycif_23_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_27.tif",
+            "label": "test/lung_cycif_27_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_32.tif",
+            "label": "test/lung_cycif_32_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_34.tif",
+            "label": "test/lung_cycif_34_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_38.tif",
+            "label": "test/lung_cycif_38_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_46.tif",
+            "label": "test/lung_cycif_46_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_55.tif",
+            "label": "test/lung_cycif_55_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_62.tif",
+            "label": "test/lung_cycif_62_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_65.tif",
+            "label": "test/lung_cycif_65_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_67.tif",
+            "label": "test/lung_cycif_67_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_77.tif",
+            "label": "test/lung_cycif_77_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_90.tif",
+            "label": "test/lung_cycif_90_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_95.tif",
+            "label": "test/lung_cycif_95_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_97.tif",
+            "label": "test/lung_cycif_97_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_100.tif",
+            "label": "test/lung_cycif_100_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_107.tif",
+            "label": "test/lung_cycif_107_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_108.tif",
+            "label": "test/lung_cycif_108_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_110.tif",
+            "label": "test/lung_cycif_110_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_113.tif",
+            "label": "test/lung_cycif_113_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_121.tif",
+            "label": "test/lung_cycif_121_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_124.tif",
+            "label": "test/lung_cycif_124_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_131.tif",
+            "label": "test/lung_cycif_131_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_135.tif",
+            "label": "test/lung_cycif_135_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_146.tif",
+            "label": "test/lung_cycif_146_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_3.tif",
+            "label": "test/lung_cycif_3_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_15.tif",
+            "label": "test/lung_cycif_15_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_21.tif",
+            "label": "test/lung_cycif_21_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_24.tif",
+            "label": "test/lung_cycif_24_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_26.tif",
+            "label": "test/lung_cycif_26_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_30.tif",
+            "label": "test/lung_cycif_30_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_41.tif",
+            "label": "test/lung_cycif_41_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_43.tif",
+            "label": "test/lung_cycif_43_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_47.tif",
+            "label": "test/lung_cycif_47_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_49.tif",
+            "label": "test/lung_cycif_49_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_52.tif",
+            "label": "test/lung_cycif_52_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_64.tif",
+            "label": "test/lung_cycif_64_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_70.tif",
+            "label": "test/lung_cycif_70_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_76.tif",
+            "label": "test/lung_cycif_76_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_80.tif",
+            "label": "test/lung_cycif_80_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_82.tif",
+            "label": "test/lung_cycif_82_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_87.tif",
+            "label": "test/lung_cycif_87_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_94.tif",
+            "label": "test/lung_cycif_94_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_102.tif",
+            "label": "test/lung_cycif_102_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_106.tif",
+            "label": "test/lung_cycif_106_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_112.tif",
+            "label": "test/lung_cycif_112_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_116.tif",
+            "label": "test/lung_cycif_116_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_126.tif",
+            "label": "test/lung_cycif_126_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_127.tif",
+            "label": "test/lung_cycif_127_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_130.tif",
+            "label": "test/lung_cycif_130_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_139.tif",
+            "label": "test/lung_cycif_139_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_142.tif",
+            "label": "test/lung_cycif_142_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_143.tif",
+            "label": "test/lung_cycif_143_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_144.tif",
+            "label": "test/lung_cycif_144_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_0.tif",
+            "label": "test/lung_cycif_0_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_1.tif",
+            "label": "test/lung_cycif_1_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_7.tif",
+            "label": "test/lung_cycif_7_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_8.tif",
+            "label": "test/lung_cycif_8_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_13.tif",
+            "label": "test/lung_cycif_13_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_20.tif",
+            "label": "test/lung_cycif_20_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_22.tif",
+            "label": "test/lung_cycif_22_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_25.tif",
+            "label": "test/lung_cycif_25_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_37.tif",
+            "label": "test/lung_cycif_37_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_50.tif",
+            "label": "test/lung_cycif_50_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_57.tif",
+            "label": "test/lung_cycif_57_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_60.tif",
+            "label": "test/lung_cycif_60_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_61.tif",
+            "label": "test/lung_cycif_61_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_63.tif",
+            "label": "test/lung_cycif_63_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_68.tif",
+            "label": "test/lung_cycif_68_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_71.tif",
+            "label": "test/lung_cycif_71_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_72.tif",
+            "label": "test/lung_cycif_72_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_79.tif",
+            "label": "test/lung_cycif_79_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_81.tif",
+            "label": "test/lung_cycif_81_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_86.tif",
+            "label": "test/lung_cycif_86_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_88.tif",
+            "label": "test/lung_cycif_88_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_96.tif",
+            "label": "test/lung_cycif_96_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_101.tif",
+            "label": "test/lung_cycif_101_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_115.tif",
+            "label": "test/lung_cycif_115_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_129.tif",
+            "label": "test/lung_cycif_129_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_133.tif",
+            "label": "test/lung_cycif_133_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_134.tif",
+            "label": "test/lung_cycif_134_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_137.tif",
+            "label": "test/lung_cycif_137_masks.tif"
+        },
+        {
+            "image": "test/lung_cycif_140.tif",
+            "label": "test/lung_cycif_140_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/tissuenet_pancreas_codex_datalist.json b/vista2d/datalists/tissuenet_pancreas_codex_datalist.json
new file mode 100644
index 0000000..c195eb3
--- /dev/null
+++ b/vista2d/datalists/tissuenet_pancreas_codex_datalist.json
@@ -0,0 +1,16646 @@
+{
+    "training": [
+        {
+            "image": "val/pancreas_codex_0.tif",
+            "label": "val/pancreas_codex_0_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_3.tif",
+            "label": "val/pancreas_codex_3_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_8.tif",
+            "label": "val/pancreas_codex_8_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_13.tif",
+            "label": "val/pancreas_codex_13_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_16.tif",
+            "label": "val/pancreas_codex_16_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_19.tif",
+            "label": "val/pancreas_codex_19_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_23.tif",
+            "label": "val/pancreas_codex_23_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_35.tif",
+            "label": "val/pancreas_codex_35_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_40.tif",
+            "label": "val/pancreas_codex_40_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_41.tif",
+            "label": "val/pancreas_codex_41_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_47.tif",
+            "label": "val/pancreas_codex_47_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_50.tif",
+            "label": "val/pancreas_codex_50_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_56.tif",
+            "label": "val/pancreas_codex_56_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_57.tif",
+            "label": "val/pancreas_codex_57_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_59.tif",
+            "label": "val/pancreas_codex_59_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_60.tif",
+            "label": "val/pancreas_codex_60_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_61.tif",
+            "label": "val/pancreas_codex_61_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_65.tif",
+            "label": "val/pancreas_codex_65_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_66.tif",
+            "label": "val/pancreas_codex_66_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_68.tif",
+            "label": "val/pancreas_codex_68_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_69.tif",
+            "label": "val/pancreas_codex_69_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_74.tif",
+            "label": "val/pancreas_codex_74_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_81.tif",
+            "label": "val/pancreas_codex_81_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_84.tif",
+            "label": "val/pancreas_codex_84_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_85.tif",
+            "label": "val/pancreas_codex_85_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_92.tif",
+            "label": "val/pancreas_codex_92_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_101.tif",
+            "label": "val/pancreas_codex_101_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_104.tif",
+            "label": "val/pancreas_codex_104_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_108.tif",
+            "label": "val/pancreas_codex_108_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_111.tif",
+            "label": "val/pancreas_codex_111_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_117.tif",
+            "label": "val/pancreas_codex_117_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_120.tif",
+            "label": "val/pancreas_codex_120_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_132.tif",
+            "label": "val/pancreas_codex_132_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_134.tif",
+            "label": "val/pancreas_codex_134_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_148.tif",
+            "label": "val/pancreas_codex_148_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_154.tif",
+            "label": "val/pancreas_codex_154_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_159.tif",
+            "label": "val/pancreas_codex_159_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_161.tif",
+            "label": "val/pancreas_codex_161_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_172.tif",
+            "label": "val/pancreas_codex_172_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_175.tif",
+            "label": "val/pancreas_codex_175_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_180.tif",
+            "label": "val/pancreas_codex_180_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_185.tif",
+            "label": "val/pancreas_codex_185_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_187.tif",
+            "label": "val/pancreas_codex_187_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_194.tif",
+            "label": "val/pancreas_codex_194_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_195.tif",
+            "label": "val/pancreas_codex_195_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_207.tif",
+            "label": "val/pancreas_codex_207_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_214.tif",
+            "label": "val/pancreas_codex_214_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_216.tif",
+            "label": "val/pancreas_codex_216_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_218.tif",
+            "label": "val/pancreas_codex_218_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_223.tif",
+            "label": "val/pancreas_codex_223_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_224.tif",
+            "label": "val/pancreas_codex_224_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_233.tif",
+            "label": "val/pancreas_codex_233_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_241.tif",
+            "label": "val/pancreas_codex_241_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_242.tif",
+            "label": "val/pancreas_codex_242_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_247.tif",
+            "label": "val/pancreas_codex_247_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_257.tif",
+            "label": "val/pancreas_codex_257_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_259.tif",
+            "label": "val/pancreas_codex_259_masks.tif",
+            "fold": 0
+        },
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+            "label": "test/pancreas_codex_253_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_257.tif",
+            "label": "test/pancreas_codex_257_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_259.tif",
+            "label": "test/pancreas_codex_259_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_279.tif",
+            "label": "test/pancreas_codex_279_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_281.tif",
+            "label": "test/pancreas_codex_281_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_2.tif",
+            "label": "test/pancreas_codex_2_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_6.tif",
+            "label": "test/pancreas_codex_6_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_9.tif",
+            "label": "test/pancreas_codex_9_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_10.tif",
+            "label": "test/pancreas_codex_10_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_13.tif",
+            "label": "test/pancreas_codex_13_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_20.tif",
+            "label": "test/pancreas_codex_20_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_23.tif",
+            "label": "test/pancreas_codex_23_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_32.tif",
+            "label": "test/pancreas_codex_32_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_36.tif",
+            "label": "test/pancreas_codex_36_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_45.tif",
+            "label": "test/pancreas_codex_45_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_46.tif",
+            "label": "test/pancreas_codex_46_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_65.tif",
+            "label": "test/pancreas_codex_65_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_74.tif",
+            "label": "test/pancreas_codex_74_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_75.tif",
+            "label": "test/pancreas_codex_75_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_77.tif",
+            "label": "test/pancreas_codex_77_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_83.tif",
+            "label": "test/pancreas_codex_83_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_87.tif",
+            "label": "test/pancreas_codex_87_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_92.tif",
+            "label": "test/pancreas_codex_92_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_96.tif",
+            "label": "test/pancreas_codex_96_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_100.tif",
+            "label": "test/pancreas_codex_100_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_103.tif",
+            "label": "test/pancreas_codex_103_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_104.tif",
+            "label": "test/pancreas_codex_104_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_113.tif",
+            "label": "test/pancreas_codex_113_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_124.tif",
+            "label": "test/pancreas_codex_124_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_125.tif",
+            "label": "test/pancreas_codex_125_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_126.tif",
+            "label": "test/pancreas_codex_126_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_128.tif",
+            "label": "test/pancreas_codex_128_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_136.tif",
+            "label": "test/pancreas_codex_136_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_143.tif",
+            "label": "test/pancreas_codex_143_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_151.tif",
+            "label": "test/pancreas_codex_151_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_153.tif",
+            "label": "test/pancreas_codex_153_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_155.tif",
+            "label": "test/pancreas_codex_155_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_160.tif",
+            "label": "test/pancreas_codex_160_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_162.tif",
+            "label": "test/pancreas_codex_162_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_165.tif",
+            "label": "test/pancreas_codex_165_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_166.tif",
+            "label": "test/pancreas_codex_166_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_183.tif",
+            "label": "test/pancreas_codex_183_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_187.tif",
+            "label": "test/pancreas_codex_187_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_189.tif",
+            "label": "test/pancreas_codex_189_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_190.tif",
+            "label": "test/pancreas_codex_190_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_195.tif",
+            "label": "test/pancreas_codex_195_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_199.tif",
+            "label": "test/pancreas_codex_199_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_221.tif",
+            "label": "test/pancreas_codex_221_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_223.tif",
+            "label": "test/pancreas_codex_223_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_233.tif",
+            "label": "test/pancreas_codex_233_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_242.tif",
+            "label": "test/pancreas_codex_242_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_244.tif",
+            "label": "test/pancreas_codex_244_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_245.tif",
+            "label": "test/pancreas_codex_245_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_249.tif",
+            "label": "test/pancreas_codex_249_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_250.tif",
+            "label": "test/pancreas_codex_250_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_256.tif",
+            "label": "test/pancreas_codex_256_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_260.tif",
+            "label": "test/pancreas_codex_260_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_261.tif",
+            "label": "test/pancreas_codex_261_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_263.tif",
+            "label": "test/pancreas_codex_263_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_264.tif",
+            "label": "test/pancreas_codex_264_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_272.tif",
+            "label": "test/pancreas_codex_272_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_275.tif",
+            "label": "test/pancreas_codex_275_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_1.tif",
+            "label": "test/pancreas_codex_1_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_3.tif",
+            "label": "test/pancreas_codex_3_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_7.tif",
+            "label": "test/pancreas_codex_7_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_15.tif",
+            "label": "test/pancreas_codex_15_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_22.tif",
+            "label": "test/pancreas_codex_22_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_24.tif",
+            "label": "test/pancreas_codex_24_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_25.tif",
+            "label": "test/pancreas_codex_25_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_26.tif",
+            "label": "test/pancreas_codex_26_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_30.tif",
+            "label": "test/pancreas_codex_30_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_37.tif",
+            "label": "test/pancreas_codex_37_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_41.tif",
+            "label": "test/pancreas_codex_41_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_43.tif",
+            "label": "test/pancreas_codex_43_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_49.tif",
+            "label": "test/pancreas_codex_49_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_50.tif",
+            "label": "test/pancreas_codex_50_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_52.tif",
+            "label": "test/pancreas_codex_52_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_57.tif",
+            "label": "test/pancreas_codex_57_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_64.tif",
+            "label": "test/pancreas_codex_64_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_68.tif",
+            "label": "test/pancreas_codex_68_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_71.tif",
+            "label": "test/pancreas_codex_71_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_72.tif",
+            "label": "test/pancreas_codex_72_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_76.tif",
+            "label": "test/pancreas_codex_76_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_80.tif",
+            "label": "test/pancreas_codex_80_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_86.tif",
+            "label": "test/pancreas_codex_86_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_109.tif",
+            "label": "test/pancreas_codex_109_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_111.tif",
+            "label": "test/pancreas_codex_111_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_115.tif",
+            "label": "test/pancreas_codex_115_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_121.tif",
+            "label": "test/pancreas_codex_121_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_129.tif",
+            "label": "test/pancreas_codex_129_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_131.tif",
+            "label": "test/pancreas_codex_131_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_133.tif",
+            "label": "test/pancreas_codex_133_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_135.tif",
+            "label": "test/pancreas_codex_135_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_141.tif",
+            "label": "test/pancreas_codex_141_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_144.tif",
+            "label": "test/pancreas_codex_144_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_149.tif",
+            "label": "test/pancreas_codex_149_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_156.tif",
+            "label": "test/pancreas_codex_156_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_178.tif",
+            "label": "test/pancreas_codex_178_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_196.tif",
+            "label": "test/pancreas_codex_196_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_198.tif",
+            "label": "test/pancreas_codex_198_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_203.tif",
+            "label": "test/pancreas_codex_203_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_209.tif",
+            "label": "test/pancreas_codex_209_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_210.tif",
+            "label": "test/pancreas_codex_210_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_215.tif",
+            "label": "test/pancreas_codex_215_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_216.tif",
+            "label": "test/pancreas_codex_216_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_226.tif",
+            "label": "test/pancreas_codex_226_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_235.tif",
+            "label": "test/pancreas_codex_235_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_237.tif",
+            "label": "test/pancreas_codex_237_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_241.tif",
+            "label": "test/pancreas_codex_241_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_243.tif",
+            "label": "test/pancreas_codex_243_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_248.tif",
+            "label": "test/pancreas_codex_248_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_252.tif",
+            "label": "test/pancreas_codex_252_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_254.tif",
+            "label": "test/pancreas_codex_254_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_255.tif",
+            "label": "test/pancreas_codex_255_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_258.tif",
+            "label": "test/pancreas_codex_258_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_265.tif",
+            "label": "test/pancreas_codex_265_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_267.tif",
+            "label": "test/pancreas_codex_267_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_268.tif",
+            "label": "test/pancreas_codex_268_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_276.tif",
+            "label": "test/pancreas_codex_276_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/tissuenet_pancreas_datalist.json b/vista2d/datalists/tissuenet_pancreas_datalist.json
new file mode 100644
index 0000000..9dcdc84
--- /dev/null
+++ b/vista2d/datalists/tissuenet_pancreas_datalist.json
@@ -0,0 +1,17174 @@
+{
+    "training": [
+        {
+            "image": "val/pancreas_codex_0.tif",
+            "label": "val/pancreas_codex_0_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_3.tif",
+            "label": "val/pancreas_codex_3_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_8.tif",
+            "label": "val/pancreas_codex_8_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_13.tif",
+            "label": "val/pancreas_codex_13_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_16.tif",
+            "label": "val/pancreas_codex_16_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_19.tif",
+            "label": "val/pancreas_codex_19_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_23.tif",
+            "label": "val/pancreas_codex_23_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_35.tif",
+            "label": "val/pancreas_codex_35_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_41.tif",
+            "label": "val/pancreas_codex_41_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_47.tif",
+            "label": "val/pancreas_codex_47_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_50.tif",
+            "label": "val/pancreas_codex_50_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_56.tif",
+            "label": "val/pancreas_codex_56_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_57.tif",
+            "label": "val/pancreas_codex_57_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_59.tif",
+            "label": "val/pancreas_codex_59_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_60.tif",
+            "label": "val/pancreas_codex_60_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_61.tif",
+            "label": "val/pancreas_codex_61_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_65.tif",
+            "label": "val/pancreas_codex_65_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_66.tif",
+            "label": "val/pancreas_codex_66_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_69.tif",
+            "label": "val/pancreas_codex_69_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_74.tif",
+            "label": "val/pancreas_codex_74_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_81.tif",
+            "label": "val/pancreas_codex_81_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_84.tif",
+            "label": "val/pancreas_codex_84_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_85.tif",
+            "label": "val/pancreas_codex_85_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_90.tif",
+            "label": "val/pancreas_codex_90_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_101.tif",
+            "label": "val/pancreas_codex_101_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_104.tif",
+            "label": "val/pancreas_codex_104_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_108.tif",
+            "label": "val/pancreas_codex_108_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_codex_111.tif",
+            "label": "val/pancreas_codex_111_masks.tif",
+            "fold": 0
+        },
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+            "image": "test/pancreas_codex_221.tif",
+            "label": "test/pancreas_codex_221_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_222.tif",
+            "label": "test/pancreas_codex_222_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_227.tif",
+            "label": "test/pancreas_codex_227_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_243.tif",
+            "label": "test/pancreas_codex_243_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_246.tif",
+            "label": "test/pancreas_codex_246_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_248.tif",
+            "label": "test/pancreas_codex_248_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_251.tif",
+            "label": "test/pancreas_codex_251_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_257.tif",
+            "label": "test/pancreas_codex_257_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_267.tif",
+            "label": "test/pancreas_codex_267_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_268.tif",
+            "label": "test/pancreas_codex_268_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_270.tif",
+            "label": "test/pancreas_codex_270_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_282.tif",
+            "label": "test/pancreas_codex_282_masks.tif"
+        },
+        {
+            "image": "test/pancreas_vectra_6.tif",
+            "label": "test/pancreas_vectra_6_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_2.tif",
+            "label": "test/pancreas_codex_2_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_6.tif",
+            "label": "test/pancreas_codex_6_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_10.tif",
+            "label": "test/pancreas_codex_10_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_13.tif",
+            "label": "test/pancreas_codex_13_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_20.tif",
+            "label": "test/pancreas_codex_20_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_32.tif",
+            "label": "test/pancreas_codex_32_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_36.tif",
+            "label": "test/pancreas_codex_36_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_45.tif",
+            "label": "test/pancreas_codex_45_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_46.tif",
+            "label": "test/pancreas_codex_46_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_52.tif",
+            "label": "test/pancreas_codex_52_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_57.tif",
+            "label": "test/pancreas_codex_57_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_65.tif",
+            "label": "test/pancreas_codex_65_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_74.tif",
+            "label": "test/pancreas_codex_74_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_75.tif",
+            "label": "test/pancreas_codex_75_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_77.tif",
+            "label": "test/pancreas_codex_77_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_81.tif",
+            "label": "test/pancreas_codex_81_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_83.tif",
+            "label": "test/pancreas_codex_83_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_87.tif",
+            "label": "test/pancreas_codex_87_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_92.tif",
+            "label": "test/pancreas_codex_92_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_96.tif",
+            "label": "test/pancreas_codex_96_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_103.tif",
+            "label": "test/pancreas_codex_103_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_113.tif",
+            "label": "test/pancreas_codex_113_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_124.tif",
+            "label": "test/pancreas_codex_124_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_128.tif",
+            "label": "test/pancreas_codex_128_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_136.tif",
+            "label": "test/pancreas_codex_136_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_140.tif",
+            "label": "test/pancreas_codex_140_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_143.tif",
+            "label": "test/pancreas_codex_143_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_148.tif",
+            "label": "test/pancreas_codex_148_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_149.tif",
+            "label": "test/pancreas_codex_149_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_151.tif",
+            "label": "test/pancreas_codex_151_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_152.tif",
+            "label": "test/pancreas_codex_152_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_160.tif",
+            "label": "test/pancreas_codex_160_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_162.tif",
+            "label": "test/pancreas_codex_162_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_165.tif",
+            "label": "test/pancreas_codex_165_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_166.tif",
+            "label": "test/pancreas_codex_166_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_183.tif",
+            "label": "test/pancreas_codex_183_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_190.tif",
+            "label": "test/pancreas_codex_190_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_198.tif",
+            "label": "test/pancreas_codex_198_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_199.tif",
+            "label": "test/pancreas_codex_199_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_200.tif",
+            "label": "test/pancreas_codex_200_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_204.tif",
+            "label": "test/pancreas_codex_204_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_205.tif",
+            "label": "test/pancreas_codex_205_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_206.tif",
+            "label": "test/pancreas_codex_206_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_214.tif",
+            "label": "test/pancreas_codex_214_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_220.tif",
+            "label": "test/pancreas_codex_220_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_229.tif",
+            "label": "test/pancreas_codex_229_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_230.tif",
+            "label": "test/pancreas_codex_230_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_236.tif",
+            "label": "test/pancreas_codex_236_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_250.tif",
+            "label": "test/pancreas_codex_250_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_260.tif",
+            "label": "test/pancreas_codex_260_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_261.tif",
+            "label": "test/pancreas_codex_261_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_262.tif",
+            "label": "test/pancreas_codex_262_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_265.tif",
+            "label": "test/pancreas_codex_265_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_266.tif",
+            "label": "test/pancreas_codex_266_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_269.tif",
+            "label": "test/pancreas_codex_269_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_274.tif",
+            "label": "test/pancreas_codex_274_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_277.tif",
+            "label": "test/pancreas_codex_277_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_283.tif",
+            "label": "test/pancreas_codex_283_masks.tif"
+        },
+        {
+            "image": "test/pancreas_vectra_7.tif",
+            "label": "test/pancreas_vectra_7_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_1.tif",
+            "label": "test/pancreas_codex_1_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_3.tif",
+            "label": "test/pancreas_codex_3_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_7.tif",
+            "label": "test/pancreas_codex_7_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_9.tif",
+            "label": "test/pancreas_codex_9_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_15.tif",
+            "label": "test/pancreas_codex_15_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_22.tif",
+            "label": "test/pancreas_codex_22_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_23.tif",
+            "label": "test/pancreas_codex_23_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_25.tif",
+            "label": "test/pancreas_codex_25_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_26.tif",
+            "label": "test/pancreas_codex_26_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_30.tif",
+            "label": "test/pancreas_codex_30_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_37.tif",
+            "label": "test/pancreas_codex_37_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_43.tif",
+            "label": "test/pancreas_codex_43_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_49.tif",
+            "label": "test/pancreas_codex_49_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_50.tif",
+            "label": "test/pancreas_codex_50_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_64.tif",
+            "label": "test/pancreas_codex_64_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_68.tif",
+            "label": "test/pancreas_codex_68_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_71.tif",
+            "label": "test/pancreas_codex_71_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_72.tif",
+            "label": "test/pancreas_codex_72_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_76.tif",
+            "label": "test/pancreas_codex_76_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_86.tif",
+            "label": "test/pancreas_codex_86_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_100.tif",
+            "label": "test/pancreas_codex_100_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_104.tif",
+            "label": "test/pancreas_codex_104_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_109.tif",
+            "label": "test/pancreas_codex_109_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_111.tif",
+            "label": "test/pancreas_codex_111_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_115.tif",
+            "label": "test/pancreas_codex_115_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_121.tif",
+            "label": "test/pancreas_codex_121_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_125.tif",
+            "label": "test/pancreas_codex_125_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_126.tif",
+            "label": "test/pancreas_codex_126_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_129.tif",
+            "label": "test/pancreas_codex_129_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_133.tif",
+            "label": "test/pancreas_codex_133_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_135.tif",
+            "label": "test/pancreas_codex_135_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_141.tif",
+            "label": "test/pancreas_codex_141_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_144.tif",
+            "label": "test/pancreas_codex_144_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_153.tif",
+            "label": "test/pancreas_codex_153_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_155.tif",
+            "label": "test/pancreas_codex_155_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_156.tif",
+            "label": "test/pancreas_codex_156_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_178.tif",
+            "label": "test/pancreas_codex_178_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_195.tif",
+            "label": "test/pancreas_codex_195_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_196.tif",
+            "label": "test/pancreas_codex_196_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_203.tif",
+            "label": "test/pancreas_codex_203_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_209.tif",
+            "label": "test/pancreas_codex_209_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_215.tif",
+            "label": "test/pancreas_codex_215_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_216.tif",
+            "label": "test/pancreas_codex_216_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_226.tif",
+            "label": "test/pancreas_codex_226_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_231.tif",
+            "label": "test/pancreas_codex_231_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_235.tif",
+            "label": "test/pancreas_codex_235_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_237.tif",
+            "label": "test/pancreas_codex_237_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_241.tif",
+            "label": "test/pancreas_codex_241_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_252.tif",
+            "label": "test/pancreas_codex_252_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_254.tif",
+            "label": "test/pancreas_codex_254_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_255.tif",
+            "label": "test/pancreas_codex_255_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_259.tif",
+            "label": "test/pancreas_codex_259_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_264.tif",
+            "label": "test/pancreas_codex_264_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_273.tif",
+            "label": "test/pancreas_codex_273_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_275.tif",
+            "label": "test/pancreas_codex_275_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_276.tif",
+            "label": "test/pancreas_codex_276_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_278.tif",
+            "label": "test/pancreas_codex_278_masks.tif"
+        },
+        {
+            "image": "test/pancreas_codex_281.tif",
+            "label": "test/pancreas_codex_281_masks.tif"
+        },
+        {
+            "image": "test/pancreas_vectra_3.tif",
+            "label": "test/pancreas_vectra_3_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/tissuenet_pancreas_vectra_datalist.json b/vista2d/datalists/tissuenet_pancreas_vectra_datalist.json
new file mode 100644
index 0000000..d87a0d9
--- /dev/null
+++ b/vista2d/datalists/tissuenet_pancreas_vectra_datalist.json
@@ -0,0 +1,534 @@
+{
+    "training": [
+        {
+            "image": "val/pancreas_vectra_3.tif",
+            "label": "val/pancreas_vectra_3_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_6.tif",
+            "label": "val/pancreas_vectra_6_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_10.tif",
+            "label": "val/pancreas_vectra_10_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_16.tif",
+            "label": "val/pancreas_vectra_16_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_2.tif",
+            "label": "val/pancreas_vectra_2_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_4.tif",
+            "label": "val/pancreas_vectra_4_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_14.tif",
+            "label": "val/pancreas_vectra_14_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_17.tif",
+            "label": "val/pancreas_vectra_17_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_0.tif",
+            "label": "val/pancreas_vectra_0_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_1.tif",
+            "label": "val/pancreas_vectra_1_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_7.tif",
+            "label": "val/pancreas_vectra_7_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_13.tif",
+            "label": "val/pancreas_vectra_13_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_9.tif",
+            "label": "val/pancreas_vectra_9_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_15.tif",
+            "label": "val/pancreas_vectra_15_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_18.tif",
+            "label": "val/pancreas_vectra_18_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_19.tif",
+            "label": "val/pancreas_vectra_19_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_5.tif",
+            "label": "val/pancreas_vectra_5_masks.tif",
+            "fold": 0
+        },
+        {
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+            "label": "val/pancreas_vectra_8_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_11.tif",
+            "label": "val/pancreas_vectra_11_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/pancreas_vectra_12.tif",
+            "label": "val/pancreas_vectra_12_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "train/pancreas_vectra_0_2.tif",
+            "label": "train/pancreas_vectra_0_2_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_2_2.tif",
+            "label": "train/pancreas_vectra_2_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_4_3.tif",
+            "label": "train/pancreas_vectra_4_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_6_2.tif",
+            "label": "train/pancreas_vectra_6_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_6_3.tif",
+            "label": "train/pancreas_vectra_6_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_8_1.tif",
+            "label": "train/pancreas_vectra_8_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_8_2.tif",
+            "label": "train/pancreas_vectra_8_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_9_3.tif",
+            "label": "train/pancreas_vectra_9_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_10_0.tif",
+            "label": "train/pancreas_vectra_10_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_11_0.tif",
+            "label": "train/pancreas_vectra_11_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_11_3.tif",
+            "label": "train/pancreas_vectra_11_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_12_3.tif",
+            "label": "train/pancreas_vectra_12_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_13_3.tif",
+            "label": "train/pancreas_vectra_13_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_14_0.tif",
+            "label": "train/pancreas_vectra_14_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_15_1.tif",
+            "label": "train/pancreas_vectra_15_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_17_3.tif",
+            "label": "train/pancreas_vectra_17_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_3_3.tif",
+            "label": "train/pancreas_vectra_3_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_5_1.tif",
+            "label": "train/pancreas_vectra_5_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_7_3.tif",
+            "label": "train/pancreas_vectra_7_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_8_3.tif",
+            "label": "train/pancreas_vectra_8_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_9_0.tif",
+            "label": "train/pancreas_vectra_9_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_9_2.tif",
+            "label": "train/pancreas_vectra_9_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_10_3.tif",
+            "label": "train/pancreas_vectra_10_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_13_1.tif",
+            "label": "train/pancreas_vectra_13_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_14_1.tif",
+            "label": "train/pancreas_vectra_14_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_14_3.tif",
+            "label": "train/pancreas_vectra_14_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_15_0.tif",
+            "label": "train/pancreas_vectra_15_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_16_2.tif",
+            "label": "train/pancreas_vectra_16_2_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_16_3.tif",
+            "label": "train/pancreas_vectra_16_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_17_1.tif",
+            "label": "train/pancreas_vectra_17_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_18_2.tif",
+            "label": "train/pancreas_vectra_18_2_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_0_0.tif",
+            "label": "train/pancreas_vectra_0_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_0_3.tif",
+            "label": "train/pancreas_vectra_0_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_1_3.tif",
+            "label": "train/pancreas_vectra_1_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_2_0.tif",
+            "label": "train/pancreas_vectra_2_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_3_1.tif",
+            "label": "train/pancreas_vectra_3_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_4_1.tif",
+            "label": "train/pancreas_vectra_4_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_6_0.tif",
+            "label": "train/pancreas_vectra_6_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_11_1.tif",
+            "label": "train/pancreas_vectra_11_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_11_2.tif",
+            "label": "train/pancreas_vectra_11_2_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_12_0.tif",
+            "label": "train/pancreas_vectra_12_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_13_2.tif",
+            "label": "train/pancreas_vectra_13_2_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_14_2.tif",
+            "label": "train/pancreas_vectra_14_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_15_3.tif",
+            "label": "train/pancreas_vectra_15_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_17_0.tif",
+            "label": "train/pancreas_vectra_17_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_18_1.tif",
+            "label": "train/pancreas_vectra_18_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_1_0.tif",
+            "label": "train/pancreas_vectra_1_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_2_3.tif",
+            "label": "train/pancreas_vectra_2_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_3_2.tif",
+            "label": "train/pancreas_vectra_3_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_5_2.tif",
+            "label": "train/pancreas_vectra_5_2_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_5_3.tif",
+            "label": "train/pancreas_vectra_5_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_7_0.tif",
+            "label": "train/pancreas_vectra_7_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_7_1.tif",
+            "label": "train/pancreas_vectra_7_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_7_2.tif",
+            "label": "train/pancreas_vectra_7_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_8_0.tif",
+            "label": "train/pancreas_vectra_8_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_10_1.tif",
+            "label": "train/pancreas_vectra_10_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_10_2.tif",
+            "label": "train/pancreas_vectra_10_2_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_12_1.tif",
+            "label": "train/pancreas_vectra_12_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_13_0.tif",
+            "label": "train/pancreas_vectra_13_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_16_1.tif",
+            "label": "train/pancreas_vectra_16_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_17_2.tif",
+            "label": "train/pancreas_vectra_17_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_0_1.tif",
+            "label": "train/pancreas_vectra_0_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_1_1.tif",
+            "label": "train/pancreas_vectra_1_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_1_2.tif",
+            "label": "train/pancreas_vectra_1_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_2_1.tif",
+            "label": "train/pancreas_vectra_2_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_3_0.tif",
+            "label": "train/pancreas_vectra_3_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_4_0.tif",
+            "label": "train/pancreas_vectra_4_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_4_2.tif",
+            "label": "train/pancreas_vectra_4_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_5_0.tif",
+            "label": "train/pancreas_vectra_5_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_6_1.tif",
+            "label": "train/pancreas_vectra_6_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_9_1.tif",
+            "label": "train/pancreas_vectra_9_1_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_12_2.tif",
+            "label": "train/pancreas_vectra_12_2_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/pancreas_vectra_15_2.tif",
+            "label": "train/pancreas_vectra_15_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/pancreas_vectra_16_0.tif",
+            "label": "train/pancreas_vectra_16_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/pancreas_vectra_18_0.tif",
+            "label": "train/pancreas_vectra_18_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/pancreas_vectra_18_3.tif",
+            "label": "train/pancreas_vectra_18_3_masks.tif",
+            "fold": 4
+        }
+    ],
+    "testing": [
+        {
+            "image": "test/pancreas_vectra_2.tif",
+            "label": "test/pancreas_vectra_2_masks.tif"
+        },
+        {
+            "image": "test/pancreas_vectra_3.tif",
+            "label": "test/pancreas_vectra_3_masks.tif"
+        },
+        {
+            "image": "test/pancreas_vectra_4.tif",
+            "label": "test/pancreas_vectra_4_masks.tif"
+        },
+        {
+            "image": "test/pancreas_vectra_1.tif",
+            "label": "test/pancreas_vectra_1_masks.tif"
+        },
+        {
+            "image": "test/pancreas_vectra_6.tif",
+            "label": "test/pancreas_vectra_6_masks.tif"
+        },
+        {
+            "image": "test/pancreas_vectra_10.tif",
+            "label": "test/pancreas_vectra_10_masks.tif"
+        },
+        {
+            "image": "test/pancreas_vectra_0.tif",
+            "label": "test/pancreas_vectra_0_masks.tif"
+        },
+        {
+            "image": "test/pancreas_vectra_7.tif",
+            "label": "test/pancreas_vectra_7_masks.tif"
+        },
+        {
+            "image": "test/pancreas_vectra_9.tif",
+            "label": "test/pancreas_vectra_9_masks.tif"
+        },
+        {
+            "image": "test/pancreas_vectra_11.tif",
+            "label": "test/pancreas_vectra_11_masks.tif"
+        },
+        {
+            "image": "test/pancreas_vectra_5.tif",
+            "label": "test/pancreas_vectra_5_masks.tif"
+        },
+        {
+            "image": "test/pancreas_vectra_8.tif",
+            "label": "test/pancreas_vectra_8_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/tissuenet_skin_mibi_datalist.json b/vista2d/datalists/tissuenet_skin_mibi_datalist.json
new file mode 100644
index 0000000..582ae6b
--- /dev/null
+++ b/vista2d/datalists/tissuenet_skin_mibi_datalist.json
@@ -0,0 +1,2466 @@
+{
+    "training": [
+        {
+            "image": "val/skin_mibi_2.tif",
+            "label": "val/skin_mibi_2_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_17.tif",
+            "label": "val/skin_mibi_17_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_35.tif",
+            "label": "val/skin_mibi_35_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_38.tif",
+            "label": "val/skin_mibi_38_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_42.tif",
+            "label": "val/skin_mibi_42_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_44.tif",
+            "label": "val/skin_mibi_44_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_53.tif",
+            "label": "val/skin_mibi_53_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_56.tif",
+            "label": "val/skin_mibi_56_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_58.tif",
+            "label": "val/skin_mibi_58_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_65.tif",
+            "label": "val/skin_mibi_65_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_69.tif",
+            "label": "val/skin_mibi_69_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_74.tif",
+            "label": "val/skin_mibi_74_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_78.tif",
+            "label": "val/skin_mibi_78_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_82.tif",
+            "label": "val/skin_mibi_82_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_90.tif",
+            "label": "val/skin_mibi_90_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_91.tif",
+            "label": "val/skin_mibi_91_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_92.tif",
+            "label": "val/skin_mibi_92_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_95.tif",
+            "label": "val/skin_mibi_95_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_98.tif",
+            "label": "val/skin_mibi_98_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_99.tif",
+            "label": "val/skin_mibi_99_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_100.tif",
+            "label": "val/skin_mibi_100_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_103.tif",
+            "label": "val/skin_mibi_103_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_105.tif",
+            "label": "val/skin_mibi_105_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_10.tif",
+            "label": "val/skin_mibi_10_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_19.tif",
+            "label": "val/skin_mibi_19_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_27.tif",
+            "label": "val/skin_mibi_27_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_31.tif",
+            "label": "val/skin_mibi_31_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_32.tif",
+            "label": "val/skin_mibi_32_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_33.tif",
+            "label": "val/skin_mibi_33_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_36.tif",
+            "label": "val/skin_mibi_36_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_39.tif",
+            "label": "val/skin_mibi_39_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_40.tif",
+            "label": "val/skin_mibi_40_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_46.tif",
+            "label": "val/skin_mibi_46_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_48.tif",
+            "label": "val/skin_mibi_48_masks.tif",
+            "fold": 0
+        },
+        {
+            "image": "val/skin_mibi_54.tif",
+            "label": "val/skin_mibi_54_masks.tif",
+            "fold": 0
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+        },
+        {
+            "image": "train/skin_mibi_54_0.tif",
+            "label": "train/skin_mibi_54_0_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/skin_mibi_56_2.tif",
+            "label": "train/skin_mibi_56_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/skin_mibi_58_3.tif",
+            "label": "train/skin_mibi_58_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/skin_mibi_59_1.tif",
+            "label": "train/skin_mibi_59_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/skin_mibi_59_3.tif",
+            "label": "train/skin_mibi_59_3_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/skin_mibi_60_1.tif",
+            "label": "train/skin_mibi_60_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/skin_mibi_60_3.tif",
+            "label": "train/skin_mibi_60_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/skin_mibi_63_0.tif",
+            "label": "train/skin_mibi_63_0_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/skin_mibi_63_2.tif",
+            "label": "train/skin_mibi_63_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/skin_mibi_63_3.tif",
+            "label": "train/skin_mibi_63_3_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/skin_mibi_66_0.tif",
+            "label": "train/skin_mibi_66_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/skin_mibi_66_1.tif",
+            "label": "train/skin_mibi_66_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/skin_mibi_66_2.tif",
+            "label": "train/skin_mibi_66_2_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/skin_mibi_69_0.tif",
+            "label": "train/skin_mibi_69_0_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/skin_mibi_69_3.tif",
+            "label": "train/skin_mibi_69_3_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/skin_mibi_70_0.tif",
+            "label": "train/skin_mibi_70_0_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/skin_mibi_70_1.tif",
+            "label": "train/skin_mibi_70_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/skin_mibi_70_2.tif",
+            "label": "train/skin_mibi_70_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/skin_mibi_71_2.tif",
+            "label": "train/skin_mibi_71_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/skin_mibi_74_1.tif",
+            "label": "train/skin_mibi_74_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/skin_mibi_78_1.tif",
+            "label": "train/skin_mibi_78_1_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/skin_mibi_79_1.tif",
+            "label": "train/skin_mibi_79_1_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/skin_mibi_79_2.tif",
+            "label": "train/skin_mibi_79_2_masks.tif",
+            "fold": 4
+        },
+        {
+            "image": "train/skin_mibi_79_3.tif",
+            "label": "train/skin_mibi_79_3_masks.tif",
+            "fold": 3
+        },
+        {
+            "image": "train/skin_mibi_80_1.tif",
+            "label": "train/skin_mibi_80_1_masks.tif",
+            "fold": 1
+        },
+        {
+            "image": "train/skin_mibi_83_2.tif",
+            "label": "train/skin_mibi_83_2_masks.tif",
+            "fold": 2
+        },
+        {
+            "image": "train/skin_mibi_83_3.tif",
+            "label": "train/skin_mibi_83_3_masks.tif",
+            "fold": 1
+        }
+    ],
+    "testing": [
+        {
+            "image": "test/skin_mibi_2.tif",
+            "label": "test/skin_mibi_2_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_3.tif",
+            "label": "test/skin_mibi_3_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_19.tif",
+            "label": "test/skin_mibi_19_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_21.tif",
+            "label": "test/skin_mibi_21_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_22.tif",
+            "label": "test/skin_mibi_22_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_30.tif",
+            "label": "test/skin_mibi_30_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_34.tif",
+            "label": "test/skin_mibi_34_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_35.tif",
+            "label": "test/skin_mibi_35_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_41.tif",
+            "label": "test/skin_mibi_41_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_17.tif",
+            "label": "test/skin_mibi_17_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_23.tif",
+            "label": "test/skin_mibi_23_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_29.tif",
+            "label": "test/skin_mibi_29_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_31.tif",
+            "label": "test/skin_mibi_31_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_32.tif",
+            "label": "test/skin_mibi_32_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_33.tif",
+            "label": "test/skin_mibi_33_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_36.tif",
+            "label": "test/skin_mibi_36_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_40.tif",
+            "label": "test/skin_mibi_40_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_44.tif",
+            "label": "test/skin_mibi_44_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_4.tif",
+            "label": "test/skin_mibi_4_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_10.tif",
+            "label": "test/skin_mibi_10_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_14.tif",
+            "label": "test/skin_mibi_14_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_20.tif",
+            "label": "test/skin_mibi_20_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_24.tif",
+            "label": "test/skin_mibi_24_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_26.tif",
+            "label": "test/skin_mibi_26_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_27.tif",
+            "label": "test/skin_mibi_27_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_38.tif",
+            "label": "test/skin_mibi_38_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_39.tif",
+            "label": "test/skin_mibi_39_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_1.tif",
+            "label": "test/skin_mibi_1_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_6.tif",
+            "label": "test/skin_mibi_6_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_7.tif",
+            "label": "test/skin_mibi_7_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_13.tif",
+            "label": "test/skin_mibi_13_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_16.tif",
+            "label": "test/skin_mibi_16_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_18.tif",
+            "label": "test/skin_mibi_18_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_25.tif",
+            "label": "test/skin_mibi_25_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_28.tif",
+            "label": "test/skin_mibi_28_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_42.tif",
+            "label": "test/skin_mibi_42_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_0.tif",
+            "label": "test/skin_mibi_0_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_5.tif",
+            "label": "test/skin_mibi_5_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_8.tif",
+            "label": "test/skin_mibi_8_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_9.tif",
+            "label": "test/skin_mibi_9_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_11.tif",
+            "label": "test/skin_mibi_11_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_12.tif",
+            "label": "test/skin_mibi_12_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_15.tif",
+            "label": "test/skin_mibi_15_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_37.tif",
+            "label": "test/skin_mibi_37_masks.tif"
+        },
+        {
+            "image": "test/skin_mibi_43.tif",
+            "label": "test/skin_mibi_43_masks.tif"
+        }
+    ]
+}
diff --git a/vista2d/datalists/yeaz_bf_dataset_list.json b/vista2d/datalists/yeaz_bf_dataset_list.json
new file mode 100644
index 0000000..942c12c
--- /dev/null
+++ b/vista2d/datalists/yeaz_bf_dataset_list.json
@@ -0,0 +1,1476 @@
+{
+    "testing": [
+        {
+            "image": "gold-standard-BF-V-1/cdc20F5BF_5_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F5BF_5_crop_2_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F10BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F10BF_10_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F10BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F10BF_20_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF8BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF8BF_2_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F3BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F3BF_20_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF10BF_10_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/wtF10BF_10_crop_2_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF8BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF8BF_20_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF2BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF2BF_1_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F4BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F4BF_5_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F1BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F1BF_1_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF10BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF10BF_1_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF9BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF9BF_2_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F9BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F9BF_1.5_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF2BF_1_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/ddF2BF_1_crop_2_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF2BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF2BF_5_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF15BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF15BF_10_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF13BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF13BF_1_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F2BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F2BF_1_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF12BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF12BF_5_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF7BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF7BF_10_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF2BF_10_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/ddF2BF_10_crop_2_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF6BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF6BF_10_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF10BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF10BF_10_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F5BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F5BF_1_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF7BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF7BF_5_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF7BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF7BF_1.5_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F9BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F9BF_1_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF3BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF3BF_1.5_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF14BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF14BF_1_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF10BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF10BF_1.5_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF10BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF10BF_2_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF10BF_2_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/wtF10BF_2_crop_2_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF7BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF7BF_20_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF6BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF6BF_10_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF2BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF2BF_1_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF7BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF7BF_1_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F7BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F7BF_1_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF10BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF10BF_5_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF11BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF11BF_20_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF8BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF8BF_1_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF5BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF5BF_5_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF7BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF7BF_10_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF5BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF5BF_1_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF10BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF10BF_5_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF1BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF1BF_1_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF2BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF2BF_5_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF10BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF10BF_1_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF2BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF2BF_2_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF6BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF6BF_2_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F6BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F6BF_20_crop_1_mask.tif"
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+        {
+            "image": "gold-standard-BF-V-1/wtF6BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF6BF_1_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF8BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF8BF_5_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF10BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF10BF_2_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF3BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF3BF_5_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF10BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF10BF_1.5_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F5BF_1.5_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F5BF_1.5_crop_2_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF4BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF4BF_1.5_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF5BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF5BF_1_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF6BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF6BF_2_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF10BF_20_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/ddF10BF_20_crop_2_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F5BF_20_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F5BF_20_crop_2_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F1BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F1BF_10_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF9BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF9BF_10_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F2BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F2BF_5_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F4BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F4BF_20_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF13BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF13BF_1.5_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF10BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF10BF_5_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF5BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF5BF_20_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF12BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF12BF_1_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF5BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF5BF_2_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF13BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF13BF_20_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF2BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF2BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F1BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F1BF_2_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF8BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF8BF_10_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF9BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF9BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF4BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF4BF_5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF10BF_20_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/wtF10BF_20_crop_2_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF3BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF3BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF6BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF6BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F10BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F10BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F4BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F4BF_10_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F3BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F3BF_2_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF7BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF7BF_20_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF9BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF9BF_5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF4BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF4BF_1.5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF15BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF15BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF10BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF10BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF2BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF2BF_1.5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF6BF_1_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/ddF6BF_1_crop_2_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F9BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F9BF_5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF9BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF9BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF2BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF2BF_20_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF8BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF8BF_5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF9BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF9BF_10_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF13BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF13BF_5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF12BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF12BF_10_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF4BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF4BF_5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF7BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF7BF_1.5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF3BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF3BF_5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF4BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF4BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF6BF_1.5_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/ddF6BF_1.5_crop_2_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF4BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF4BF_10_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF3BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF3BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF2BF_1.5_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/ddF2BF_1.5_crop_2_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF8BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF8BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF6BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF6BF_10_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF12BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF12BF_20_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F10BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F10BF_2_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF4BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF4BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF5BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF5BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF4BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF4BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF8BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF8BF_1.5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF5BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF5BF_1.5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF15BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF15BF_5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF3BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF3BF_5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF4BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF4BF_1.5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF7BF_5_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/ddF7BF_5_crop_2_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F5BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F5BF_10_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF10BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF10BF_20_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F6BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F6BF_10_crop_1_mask.tif",
+            "fold": 4
+        }
+    ]
+}
diff --git a/vista2d/datalists/yeaz_dataset_list.json b/vista2d/datalists/yeaz_dataset_list.json
new file mode 100644
index 0000000..7cb52d8
--- /dev/null
+++ b/vista2d/datalists/yeaz_dataset_list.json
@@ -0,0 +1,1749 @@
+{
+    "training": [
+        {
+            "image": "gold-standard-BF-V-1/ddF8BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF8BF_2_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF10BF_10_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/wtF10BF_10_crop_2_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F1BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F1BF_1_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF10BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF10BF_10_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF7BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF7BF_1.5_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF10BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF10BF_2_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF6BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF6BF_10_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF8BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF8BF_1_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF5BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF5BF_1_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF2BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF2BF_5_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF10BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF10BF_1_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF3BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF3BF_2_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF7BF_10_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/ddF7BF_10_crop_2_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F3BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F3BF_10_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF2BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF2BF_10_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F6BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F6BF_1_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF7BF_20_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/ddF7BF_20_crop_2_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF4BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF4BF_2_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF5BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF5BF_5_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF7BF_1_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/ddF7BF_1_crop_2_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF9BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF9BF_1.5_crop_1_mask.tif",
+            "fold": 0
+        },
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+            "image": "gold-standard-BF-V-1/cdc20F8BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F8BF_5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF10BF_5_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/wtF10BF_5_crop_2_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF9BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF9BF_2_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF3BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF3BF_20_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF3BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF3BF_2_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF1BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF1BF_1.5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F1BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F1BF_5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF4BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF4BF_1.5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF3BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF3BF_10_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF8BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF8BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF15BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF15BF_1.5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF14BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF14BF_5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF2BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF2BF_20_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF8BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF8BF_2_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF2BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF2BF_2_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF6BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF6BF_20_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF5BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF5BF_20_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF12BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF12BF_10_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF4BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF4BF_5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF11BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF11BF_1.5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF9BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF9BF_20_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF8BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF8BF_5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF6BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF6BF_1.5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF5BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF5BF_10_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF7BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF7BF_1.5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF4BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF4BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF2BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF2BF_10_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF7BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF7BF_5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF6BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF6BF_5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF6BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF6BF_1.5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F10BF_2_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F10BF_2_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF11BF_10_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF11BF_10_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F7BF_5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F7BF_5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF1BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF1BF_20_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF5BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF5BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/ddF4BF_1_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/ddF4BF_1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF5BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF5BF_1.5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/clnF5BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/clnF5BF_1.5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/cdc20F5BF_10_crop_2_im.tif",
+            "label": "gold-standard-BF-V-1/cdc20F5BF_10_crop_2_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF13BF_1.5_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF13BF_1.5_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-BF-V-1/wtF2BF_20_crop_1_im.tif",
+            "label": "gold-standard-BF-V-1/wtF2BF_20_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/m_reexport1_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/m_reexport1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV4_20p_PhC_present_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV4_20p_PhC_present_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/d_reexport2_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/d_reexport2_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/a_reexport3_crop_2_im.tif",
+            "label": "gold-standard-PhC-plus-2/a_reexport3_crop_2_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV10_20p_PhC_present_crop_2_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV10_20p_PhC_present_crop_2_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/Ahmad_frame_16_crop_2_im.tif",
+            "label": "gold-standard-PhC-plus-2/Ahmad_frame_16_crop_2_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/Ahmad_frame_19_crop_2_im.tif",
+            "label": "gold-standard-PhC-plus-2/Ahmad_frame_19_crop_2_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV14_20p_PhC_present_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV14_20p_PhC_present_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/Ahmad_frame_16_crop_4_im.tif",
+            "label": "gold-standard-PhC-plus-2/Ahmad_frame_16_crop_4_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/Ahmad_frame_16_crop_3_im.tif",
+            "label": "gold-standard-PhC-plus-2/Ahmad_frame_16_crop_3_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/a_reexport2_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/a_reexport2_crop_1_mask.tif",
+            "fold": 4
+        }
+    ]
+}
diff --git a/vista2d/datalists/yeaz_phc_dataset_list.json b/vista2d/datalists/yeaz_phc_dataset_list.json
new file mode 100644
index 0000000..4c1242b
--- /dev/null
+++ b/vista2d/datalists/yeaz_phc_dataset_list.json
@@ -0,0 +1,213 @@
+{
+    "testing": [
+        {
+            "image": "gold-standard-PhC-plus-2/m_reexport1_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/m_reexport1_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/d_reexport2_crop_3_im.tif",
+            "label": "gold-standard-PhC-plus-2/d_reexport2_crop_3_mask.tif"
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV4_20p_PhC_present_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV4_20p_PhC_present_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/d_reexport2_crop_2_im.tif",
+            "label": "gold-standard-PhC-plus-2/d_reexport2_crop_2_mask.tif"
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV10_20p_PhC_present_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV10_20p_PhC_present_crop_1_mask.tif"
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/d_reexport1_crop_3_im.tif",
+            "label": "gold-standard-PhC-plus-2/d_reexport1_crop_3_mask.tif"
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/d_reexport1_crop_2_im.tif",
+            "label": "gold-standard-PhC-plus-2/d_reexport1_crop_2_mask.tif"
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/d_reexport2_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/d_reexport2_crop_1_mask.tif"
+        }
+    ],
+    "training": [
+        {
+            "image": "gold-standard-PhC-plus-2/d_reexport1_crop_4_im.tif",
+            "label": "gold-standard-PhC-plus-2/d_reexport1_crop_4_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV11_20p_PhC_present_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV11_20p_PhC_present_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV13_20p_PhC_present_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV13_20p_PhC_present_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/Ahmad_frame_19_crop_2_im.tif",
+            "label": "gold-standard-PhC-plus-2/Ahmad_frame_19_crop_2_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/V11032020_p3_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/V11032020_p3_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV14_20p_PhC_present_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV14_20p_PhC_present_crop_1_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/Ahmad_frame_16_crop_4_im.tif",
+            "label": "gold-standard-PhC-plus-2/Ahmad_frame_16_crop_4_mask.tif",
+            "fold": 0
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV1_20p_PhC_present_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV1_20p_PhC_present_crop_1_mask.tif",
+            "fold": 1
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/a_reexport3_crop_2_im.tif",
+            "label": "gold-standard-PhC-plus-2/a_reexport3_crop_2_mask.tif",
+            "fold": 1
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/v_clnnull_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/v_clnnull_crop_1_mask.tif",
+            "fold": 1
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/Ahmad_frame_19_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/Ahmad_frame_19_crop_1_mask.tif",
+            "fold": 1
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV8_20p_PhC_present_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV8_20p_PhC_present_crop_1_mask.tif",
+            "fold": 1
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/Ahmad_frame_16_crop_2_im.tif",
+            "label": "gold-standard-PhC-plus-2/Ahmad_frame_16_crop_2_mask.tif",
+            "fold": 1
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/Ahmad_frame_16_crop_3_im.tif",
+            "label": "gold-standard-PhC-plus-2/Ahmad_frame_16_crop_3_mask.tif",
+            "fold": 1
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/2020_3_24_AS20_1_AS18_Pos11_crop_2_im.tif",
+            "label": "gold-standard-PhC-plus-2/2020_3_24_AS20_1_AS18_Pos11_crop_2_mask.tif",
+            "fold": 2
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/2020_3_24_AS20_1_AS18_Pos11_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/2020_3_24_AS20_1_AS18_Pos11_crop_1_mask.tif",
+            "fold": 2
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV10_20p_PhC_present_crop_2_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV10_20p_PhC_present_crop_2_mask.tif",
+            "fold": 2
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV9_20p_PhC_present_crop_2_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV9_20p_PhC_present_crop_2_mask.tif",
+            "fold": 2
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV5_20p_PhC_present_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV5_20p_PhC_present_crop_1_mask.tif",
+            "fold": 2
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV12_20p_PhC_present_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV12_20p_PhC_present_crop_1_mask.tif",
+            "fold": 2
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV9_20p_PhC_present_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV9_20p_PhC_present_crop_1_mask.tif",
+            "fold": 2
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV15_20p_PhC_present_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV15_20p_PhC_present_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV7_20p_PhC_present_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV7_20p_PhC_present_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV2_20p_PhC_present_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV2_20p_PhC_present_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/V11032020_p1_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/V11032020_p1_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/v_cdc20null_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/v_cdc20null_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV3_20p_PhC_present_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV3_20p_PhC_present_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/Ahmad_frame_16_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/Ahmad_frame_16_crop_1_mask.tif",
+            "fold": 3
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/d_reexport1_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/d_reexport1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/FOV6_20p_PhC_present_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/FOV6_20p_PhC_present_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/V11032020_p2_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/V11032020_p2_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/a_reexport3_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/a_reexport3_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/a_reexport1_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/a_reexport1_crop_1_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/Ahmad_frame_19_crop_3_im.tif",
+            "label": "gold-standard-PhC-plus-2/Ahmad_frame_19_crop_3_mask.tif",
+            "fold": 4
+        },
+        {
+            "image": "gold-standard-PhC-plus-2/a_reexport2_crop_1_im.tif",
+            "label": "gold-standard-PhC-plus-2/a_reexport2_crop_1_mask.tif",
+            "fold": 4
+        }
+    ]
+}
diff --git a/vista2d/docs/data_license.txt b/vista2d/docs/data_license.txt
new file mode 100644
index 0000000..89fce56
--- /dev/null
+++ b/vista2d/docs/data_license.txt
@@ -0,0 +1,361 @@
+Third Party Licenses
+-----------------------------------------------------------------------
+
+/*********************************************************************/
+i.Cellpose dataset
+
+    https://www.cellpose.org/dataset
+    The user agrees to the listed conditions of Cellpose dataset by default that are cited below:
+
+    Howard Hughes Medical Institute
+
+    Research Content Terms and Conditions
+
+    Please read these Research Content Terms and Conditions (“Terms and Conditions”) carefully before you download or use
+    any images in any format from the cellpose.org website (“Content”), and do not download or use Content if you do not
+    agree with these Terms and Conditions. The Howard Hughes Medical Institute (“HHMI”, “we”, “us” and “our”) may at any
+    time revise these Terms and Conditions by updating this posting. You are bound by any such revisions and should
+    therefore periodically visit this page to review the then-current Terms and Conditions.
+
+    BY ACCEPTING THESE TERMS AND CONDITIONS, DOWNLOADING THE CONTENT OR USING THE CONTENT, YOU ARE CONFIRMING YOUR AGREEMENT
+    TO BE BOUND BY THESE TERMS AND CONDITIONS INCLUDING THE WARRANTY DISCLAIMERS, LIMITATIONS OF LIABILITY AND TERMINATION
+    PROVISIONS BELOW. IF ANY OF THESE TERMS AND CONDITIONS OR ANY FUTURE CHANGES ARE UNACCEPTABLE TO YOU, DO NOT DOWNLOAD
+    OR USE THE CONTENT AT THE CELLPOSE.ORG WEBSITE. BY DOWNLOADING OR USING CONTENT FROM CELLPOSE.ORG YOU ACCEPT AND AGREE
+    TO THESE TERMS AND CONDITIONS WITHOUT ANY RESERVATIONS, MODIFICATIONS, ADDITIONS, OR DELETIONS. IF YOU DO NOT AGREE TO
+    THESE TERMS AND CONDITIONS, YOU ARE NOT AUTHORIZED TO DOWNLOAD OR USE THE CONTENT. IF YOU REPRESENT A CORPORATION,
+    PARTNERSHIP, OR OTHER NON-INDIVIDUAL ENTITY, THE PERSON ACCEPTING THESE TERMS AND CONDITIONS ON BEHALF OF THAT ENTITY
+    REPRESENTS AND WARRANTS THAT THEY HAVE ALL NECESSARY AUTHORITY TO BIND THAT ENTITY.
+
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+    Use and Restrictions
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+    Indemnity
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+    right to assume, at our sole expense, the exclusive defense and control of any matter subject to indemnification by
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+
+    Disclaimers
+    WE MAKE NO EXPRESS WARRANTIES OR REPRESENTATIONS AS TO THE QUALITY, COMPREHENSIVENESS, AND ACCURACY OF THE CONTENT, AND
+    WE DISCLAIM ANY IMPLIED WARRANTIES OR REPRESENTATIONS, INCLUDING BUT NOT LIMITED TO IMPLIED WARRANTIES OF MERCHANTABILITY,
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+    outside the United States, you do so at your own risk and you are responsible for compliance with any local laws.
+
+/*********************************************************************/
+
+/*********************************************************************/
+ii. TissueNet dataset
+
+    https://datasets.deepcell.org/
+
+                            Modified Apache License
+                           Version 2.0, January 2004
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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+
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+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
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+
+   7. Disclaimer of Warranty. Unless required by applicable law or
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+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
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+
+  10. Neither the name of Caltech nor the names of its contributors may be
+      used to endorse or promote products derived from this software without
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+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
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+
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+   you may not use this file except in compliance with the License.
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+   Unless required by applicable law or agreed to in writing, software
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+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
+/*********************************************************************/
+
+/*********************************************************************/
+iii. Kaggle Nuclei Segmentation
+    https://www.nature.com/articles/s41592-019-0612-7#rightslink
+
+    CC BY 4.0
+    http://creativecommons.org/licenses/by/4.0/
+
+/*********************************************************************/
+
+/*********************************************************************/
+iv. Omnipose
+
+    https://github.com/kevinjohncutler/omnipose/blob/main/LICENSE
+
+    Omnipose NonCommercial License
+    Copyright (c) 2021 University of Washington.
+
+    Redistribution and use for noncommercial purposes in source and binary forms, with or without modification, are permitted
+    provided that the following conditions are met:
+    1.	The software is used solely for noncommercial purposes. For commercial use rights, contact University of Washington,
+    CoMotion, at license@uw.edu.
+    2.	Redistributions of source code must retain the above copyright notice, this list of conditions and the below
+    disclaimer.
+    3.	Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following
+    disclaimer in the documentation and/or other materials provided with the distribution.
+    4.	Redistributions, with or without modifications, shall only be licensed under this NonCommercial License.
+    5.	Neither the name of the University of Washington nor the names of its contributors may be used to endorse or promote
+    products derived from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
+    INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE UNIVERSITY OF WASHINGTON OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+    INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
+    OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+    THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+/*********************************************************************/
+
+/*********************************************************************/
+v. NIPS Cell Segmentation Challenge
+
+   https://neurips22-cellseg.grand-challenge.org/dataset/
+
+   CC BY-NC-ND
+   https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en
+
+/*********************************************************************/
+
+/*********************************************************************/
+vi. LiveCell
+
+    https://sartorius-research.github.io/LIVECell/
+
+    CC BY-NC 4.0
+    https://creativecommons.org/licenses/by-nc/4.0/
+
+/*********************************************************************/
+
+/*********************************************************************/
+vii. Deepbacs
+
+    https://github.com/HenriquesLab/DeepBacs/blob/main/LICENSE
+
+    CC0 1.0
+    https://creativecommons.org/publicdomain/zero/1.0/deed.en
+
+/*********************************************************************/
+Data Usage Agreement / Citations
diff --git a/vista2d/download_preprocessor/all_file_downloader.py b/vista2d/download_preprocessor/all_file_downloader.py
new file mode 100644
index 0000000..a04805a
--- /dev/null
+++ b/vista2d/download_preprocessor/all_file_downloader.py
@@ -0,0 +1,73 @@
+import argparse
+import os
+
+import requests
+from tqdm import tqdm
+
+
+def download_files(url_dict, directory):
+    if not os.path.exists(directory):
+        os.makedirs(directory)
+
+    for key, url in url_dict.items():
+        if key == "nips_train.zip" or key == "nips_test.zip":
+            if not os.path.exists(os.path.join(directory, "nips_dataset")):
+                os.mkdir(os.path.join(directory, "nips_dataset"))
+            base_dir = os.path.join(directory, "nips_dataset")
+        elif key == "deepbacs.zip":
+            if not os.path.exists(os.path.join(directory, "deepbacs_dataset")):
+                os.mkdir(os.path.join(directory, "deepbacs_dataset"))
+            base_dir = os.path.join(directory, "deepbacs_dataset")
+        elif key == "livecell":
+            if not os.path.exists(os.path.join(directory, "livecell_dataset")):
+                os.mkdir(os.path.join(directory, "livecell_dataset"))
+            base_dir = os.path.join(directory, "livecell_dataset")
+            print(f"Downloading from {key}: {url}")
+            os.system(url + base_dir)
+            continue
+
+        try:
+            print(f"Downloading from {key}: {url}")
+            response = requests.get(url, stream=True, allow_redirects=True)
+            total_size = int(response.headers.get("content-length", 0))
+
+            # Extract the filename from the URL or use the key as the filename
+            filename = os.path.basename(key)
+            file_path = os.path.join(base_dir, filename)
+
+            # Write the content to a file in the specified directory with progress
+            with open(file_path, "wb") as file, tqdm(
+                desc=filename,
+                total=total_size,
+                unit="iB",
+                unit_scale=True,
+                unit_divisor=1024,
+            ) as bar:
+                for data in response.iter_content(chunk_size=1024):
+                    size = file.write(data)
+                    bar.update(size)
+
+            print(f"Saved to {file_path}")
+        except Exception as e:
+            print(f"Failed to download from {key} ({url}). Reason: {str(e)}")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Process some integers.")
+    parser.add_argument("--dir", type=str, help="Directory to download files to", default="/set/the/path")
+
+    args = parser.parse_args()
+    directory = os.path.normpath(args.dir)
+
+    url_dict = {
+        "deepbacs.zip": "https://zenodo.org/records/5551009/files/DeepBacs_Data_Segmentation_StarDist_MIXED_dataset.zip?download=1",
+        "nips_test.zip": "https://zenodo.org/records/10719375/files/Testing.zip?download=1",
+        "nips_train.zip": "https://zenodo.org/records/10719375/files/Training-labeled.zip?download=1",
+        "livecell": "wget --recursive --no-parent --cut-dirs=0 --timestamping -i urls.txt --directory-prefix="
+        # Add URLs with keys here
+    }
+    download_files(url_dict, directory)
+
+
+if __name__ == "__main__":
+    main()
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diff --git a/vista2d/download_preprocessor/generate_json.py b/vista2d/download_preprocessor/generate_json.py
new file mode 100644
index 0000000..67c495b
--- /dev/null
+++ b/vista2d/download_preprocessor/generate_json.py
@@ -0,0 +1,988 @@
+import argparse
+import gc
+import json
+import os
+import shutil
+import time
+import warnings
+import zipfile
+
+import imageio.v3 as imageio
+import numpy as np
+from PIL import Image
+from pycocotools.coco import COCO
+from sklearn.model_selection import KFold
+
+# from skimage.io import imsave
+# from skimage.measure import label
+# import imageio
+
+
+def min_label_precision(label):
+    lm = label.max()
+
+    if lm <= 255:
+        label = label.astype(np.uint8)
+    elif lm <= 65535:
+        label = label.astype(np.uint16)
+    else:
+        label = label.astype(np.uint32)
+
+    return label
+
+
+def guess_convert_to_uint16(img, margin=30):
+    """
+    Guess a multiplier that makes all pixels integers.
+    The input img (each channel) is already in the range 0..1, they must have been converted from uint16 integers as image / scale,
+    where scale was the unknown max intensity. We could guess the scale by looking at unique values: 1/np.min(np.diff(np.unique(im)).
+    the hypothesis is that it will be more accurate recovery of the original image, instead of doing a simple (img*65535).astype(np.uint16)
+    """
+
+    for i in range(img.shape[0]):
+        im = img[i]
+
+        if im.any():
+            start = time.time()
+            imsmall = im[::4, ::4]  # subsample
+            # imsmall = im
+
+            scale = int(np.round(1 / np.min(np.diff(np.unique(imsmall)))))  # guessing scale
+            test = [
+                (np.sum((imsmall * k) % 1)) for k in range(scale - margin, scale + margin)
+            ]  # finetune, guess a multiplier that makes all pixels integers
+            sid = np.argmin(test)  # fine tune scale
+            # print('guessing scale', scale, test[margin], 'fine tuning scale', scale - margin + sid, 'dif', test[sid], 'time', time.time()-start)
+
+            if scale < 16000 or scale > 16400:
+                # print(imsmall.shape)
+                # print(np.unique(imsmall))
+                # print(np.diff(np.unique(imsmall)))
+                # print(np.min(np.diff(np.unique(imsmall))))
+                warnings.warn("scale not in expected range")
+                print(
+                    "guessing scale",
+                    scale,
+                    test[margin],
+                    "fine tuning scale",
+                    scale - margin + sid,
+                    "dif",
+                    test[sid],
+                    "time",
+                    time.time() - start,
+                )
+
+                scale = 16384
+            else:
+                scale = scale - margin + sid
+
+            scale = min(
+                65535, scale * 4
+            )  # all the recovered scale values seems to be up to 16384, we can stretch to 65535 (for better visualization, most tiff viewers expect that range)
+            img[i] = im * scale
+
+    img = img.astype(np.uint16)
+    return img
+
+
+def concatenate_masks(mask_dir):
+    labeled_mask = None
+    i = 0
+    for filename in sorted(os.listdir(mask_dir)):
+        if filename.endswith(".png"):
+            mask = imageio.imread(os.path.join(mask_dir, filename))
+            if labeled_mask is None:
+                labeled_mask = np.zeros(shape=mask.shape, dtype=np.uint16)
+            labeled_mask[mask > 0] = i
+            i = i + 1
+
+    if i <= 255:
+        labeled_mask = labeled_mask.astype(np.uint8)
+
+    return labeled_mask
+
+
+# def concatenate_masks(mask_dir):
+#     masks = []
+#     for filename in sorted(os.listdir(mask_dir)):
+#         if filename.endswith('.png'):
+#             mask = imageio.imread(os.path.join(mask_dir, filename))
+#             masks.append(mask)
+#     concatenated_mask = np.any(masks, axis=0).astype(np.uint8)
+#     labeled_mask = label(concatenated_mask)
+#     return labeled_mask
+
+# def normalize_image(image):
+#     # Convert to float and normalize each channel
+#     image = image.astype(np.float32)
+#     for i in range(3):
+#         channel = image[..., i]
+#         channel_min = np.min(channel)
+#         channel_max = np.max(channel)
+#         if channel_max - channel_min != 0:
+#             image[..., i] = (channel - channel_min) / (channel_max - channel_min)
+#     return image
+
+
+def get_filenames_exclude_masks(dir1, target_string):
+    filenames = []
+    # Combine lists of files from both directories
+    files = os.listdir(dir1)
+    # Filter files that contain the target string but exclude 'masks'
+    filenames = [f for f in files if target_string in f and "masks" not in f]
+
+    return filenames
+
+
+def remove_overlaps(masks, medians, overlap_threshold=0.75):
+    """replace overlapping mask pixels with mask id of closest mask
+    if mask fully within another mask, remove it
+    masks = Nmasks x Ly x Lx
+    """
+    cellpix = masks.sum(axis=0)
+    igood = np.ones(masks.shape[0], "bool")
+    for i in masks.sum(axis=(1, 2)).argsort():
+        npix = float(masks[i].sum())
+        noverlap = float(masks[i][cellpix > 1].sum())
+        if noverlap / npix >= overlap_threshold:
+            igood[i] = False
+            cellpix[masks[i] > 0] -= 1
+            # print(cellpix.min())
+    print(f"removing {(~igood).sum()} masks")
+    masks = masks[igood]
+    medians = medians[igood]
+    cellpix = masks.sum(axis=0)
+    overlaps = np.array(np.nonzero(cellpix > 1.0)).T
+    dists = ((overlaps[:, :, np.newaxis] - medians.T) ** 2).sum(axis=1)
+    tocell = np.argmin(dists, axis=1)
+    masks[:, overlaps[:, 0], overlaps[:, 1]] = 0
+    masks[tocell, overlaps[:, 0], overlaps[:, 1]] = 1
+
+    # labels should be 1 to mask.shape[0]
+    masks = masks.astype(int) * np.arange(1, masks.shape[0] + 1, 1, int)[:, np.newaxis, np.newaxis]
+    masks = masks.sum(axis=0)
+    gc.collect()
+    return masks
+
+
+def livecell_json_files(dataset_dir, json_f_path):
+    """
+    This function takes in the directory of livecell extracted dataset as input and
+    creates 7 json lists with 5 folds. Separate testing set is recorded in the json list.
+    Please note that there are some hard-coded directory names as per the original dataset.
+    At the time of creation, the livecell zipfile had 'images' and 'LIVECell_dataset_2021' directories
+    """
+
+    # "A172", "BT474", "Huh7", "MCF7", "SHSY5Y", "SkBr3", "SKOV3"
+    # TODO "BV2" is being skipped
+    cell_type_list = ["A172", "BT474", "Huh7", "MCF7", "SHSY5Y", "SkBr3", "SKOV3"]
+    for each_cell_tp in cell_type_list:
+        for split in ["train", "val", "test"]:
+            print(f"Working on split: {split}")
+
+            if split == "test":
+                img_path = os.path.join(dataset_dir, "images", "livecell_test_images", each_cell_tp)
+                msk_path = os.path.join(dataset_dir, "images", "livecell_test_images", each_cell_tp + "_masks")
+            else:
+                img_path = os.path.join(dataset_dir, "images", "livecell_train_val_images", each_cell_tp)
+                msk_path = os.path.join(dataset_dir, "images", "livecell_train_val_images", each_cell_tp + "_masks")
+            if not os.path.exists(msk_path):
+                os.makedirs(msk_path)
+
+            # annotation path
+            path = os.path.join(
+                dataset_dir,
+                "livecell-dataset.s3.eu-central-1.amazonaws.com",
+                "LIVECell_dataset_2021",
+                "annotations",
+                "LIVECell_single_cells",
+                each_cell_tp.lower(),
+                split + ".json",
+            )
+            annotation = COCO(path)
+            # Convert COCO format segmentation to binary mask
+            images = annotation.loadImgs(annotation.getImgIds())
+            height = []
+            width = []
+            for index, im in enumerate(images):
+                print("Status: {}/{}, Process image: {}".format(index, len(images), im["file_name"]))
+                if (
+                    im["file_name"] == "BV2_Phase_C4_2_03d00h00m_1.tif"
+                    or im["file_name"] == "BV2_Phase_C4_2_03d00h00m_3.tif"
+                ):
+                    print("Skipping the file: BV2_Phase_C4_2_03d00h00m_1.tif, as it is troublesome")
+                    continue
+                # load image
+                img = Image.open(os.path.join(img_path, im["file_name"])).convert("L")
+                height.append(img.size[0])
+                width.append(img.size[1])
+                # arr = np.asarray(img) #? not used
+                # msk = np.zeros(arr.shape)
+                # load and display instance annotations
+                annIds = annotation.getAnnIds(imgIds=im["id"], iscrowd=None)
+                anns = annotation.loadAnns(annIds)
+                idx = 1
+                medians = []
+                masks = []
+                k = 0
+                for ann in anns:
+                    # convert segmentation to binary mask
+                    mask = annotation.annToMask(ann)
+                    masks.append(mask)
+                    ypix, xpix = mask.nonzero()
+                    medians.append(np.array([ypix.mean().astype(np.float32), xpix.mean().astype(np.float32)]))
+                    k += 1
+                    # add instance mask to image mask
+                    # msk = np.add(msk, mask*idx)
+                    # idx += 1
+
+                masks = np.array(masks).astype(np.int8)
+                medians = np.array(medians)
+                masks = remove_overlaps(masks, medians, overlap_threshold=0.75)
+                gc.collect()
+
+                # ## Create new name for the image and also for the mask and save them as .tif format
+                # masks_int32 = masks.astype(np.int32)
+                # mask_pil = Image.fromarray(masks_int32, 'I')
+
+                t_filename = im["file_name"]
+                # cell_type = t_filename.split('_')[0] #? not used
+                new_mask_name = t_filename[:-4] + "_masks.tif"
+                # mask_pil.save(os.path.join(msk_path, new_mask_name))
+                imageio.imwrite(os.path.join(msk_path, new_mask_name), min_label_precision(masks))
+                gc.collect()
+
+            print(f"In total {len(images)} images")
+
+        # The directory containing your files
+        # cell_type = 'BV2'
+        json_save_path = os.path.join(json_f_path, f"lc_{each_cell_tp}.json")
+        directory = os.path.join(dataset_dir, "images", "livecell_train_val_images", each_cell_tp)
+        mask_directory = os.path.join(dataset_dir, "images", "livecell_train_val_images", each_cell_tp + "_masks")
+        test_directory = os.path.join(dataset_dir, "images", "livecell_test_images", each_cell_tp)
+        mask_test_directory = os.path.join(dataset_dir, "images", "livecell_test_images", each_cell_tp + "_masks")
+        # List to hold all image-mask pairs
+        data_pairs = []
+        test_data_pairs = []
+        all_data = {}
+
+        # Scan the directory for image files and create pairs
+        for filename in os.listdir(directory):
+            if filename.endswith(".tif"):
+                # Construct the corresponding mask filename
+                mask_filename = filename.replace(".tif", "_masks.tif")
+
+                # Check if the corresponding mask file exists
+                if os.path.exists(os.path.join(mask_directory, mask_filename)):
+                    # Add the pair to the list
+                    data_pairs.append(
+                        {
+                            "image": os.path.join(
+                                "livecell_dataset", "images", "livecell_train_val_images", each_cell_tp, filename
+                            ),
+                            "label": os.path.join(
+                                "livecell_dataset",
+                                "images",
+                                "livecell_train_val_images",
+                                f"{each_cell_tp}_masks",
+                                mask_filename,
+                            ),
+                        }
+                    )
+
+        # Convert data_pairs to a numpy array for easy indexing by KFold
+        data_pairs_array = np.array(data_pairs)
+
+        # Initialize KFold
+        kf = KFold(n_splits=5, shuffle=True, random_state=42)
+
+        # Assign fold numbers
+        for fold, (train_index, val_index) in enumerate(kf.split(data_pairs_array)):
+            for idx in val_index:
+                data_pairs_array[idx]["fold"] = fold
+
+        # Convert the array back to a list and sort by fold
+        sorted_data_pairs = sorted(data_pairs_array.tolist(), key=lambda x: x["fold"])
+
+        print(sorted_data_pairs)
+
+        # Scan the directory for image files and create pairs
+        for filename in os.listdir(test_directory):
+            if filename.endswith(".tif"):
+                # Construct the corresponding mask filename
+                mask_filename = filename.replace(".tif", "_masks.tif")
+
+                # Check if the corresponding mask file exists
+                if os.path.exists(os.path.join(mask_test_directory, mask_filename)):
+                    # Add the pair to the list
+                    test_data_pairs.append(
+                        {
+                            "image": os.path.join(
+                                "livecell_dataset", "images", "livecell_test_images", each_cell_tp, filename
+                            ),
+                            "label": os.path.join(
+                                "livecell_dataset",
+                                "images",
+                                "livecell_test_images",
+                                f"{each_cell_tp}_masks",
+                                mask_filename,
+                            ),
+                        }
+                    )
+
+        all_data["training"] = sorted_data_pairs
+        all_data["testing"] = test_data_pairs
+
+        with open(json_save_path, "w") as j_file:
+            json.dump(all_data, j_file, indent=4)
+        j_file.close()
+
+
+def tissuenet_json_files(dataset_dir, json_f_path):
+    """
+    This function takes in the directory of TissueNet extracted dataset as input and
+    creates 13 json lists with 5 folds each. Separate testing set is recorded in the json list per subset.
+    Please note that there are some hard-coded directory names as per the original dataset.
+    At the time of creation, the tissuenet 1.0 zipfile had 'train', 'val' and 'test' directories that
+    images with paired labels.
+    """
+
+    for folder in ["train", "val", "test"]:
+        if not os.path.exists(os.path.join(dataset_dir, "tissuenet_1.0", folder)):
+            os.mkdir(os.path.join(dataset_dir, "tissuenet_1.0", folder))
+
+    for folder in ["train", "val", "test"]:
+        print(f"Working on {folder} directory of tissuenet")
+        f_name = f"tissuenet_1.0/tissuenet_v1.0_{folder}.npz"
+        dat = np.load(os.path.join(dataset_dir, f_name))
+        data = dat["X"]
+        labels = dat["y"]
+        tissues = dat["tissue_list"]
+        platforms = dat["platform_list"]
+        tlabels = np.unique(tissues)
+        plabels = np.unique(platforms)
+        tp = 0
+        for t in tlabels:
+            for p in plabels:
+                ix = ((tissues == t) * (platforms == p)).nonzero()[0]
+                tp += 1
+                if len(ix) > 0:
+                    print(f"Working on {t} {p}")
+
+                    for k, i in enumerate(ix):
+                        print(f"Status: {k}/{len(ix)} {tp}/{len(tlabels) * len(plabels)} {t} {p}")
+                        img = data[i].transpose(2, 0, 1)
+                        label = labels[i][:, :, 0]
+
+                        img = guess_convert_to_uint16(img)  # guess inverse scale and convert to uint16
+                        label = min_label_precision(label)
+
+                        if folder == "train":
+                            img = img.reshape(2, 2, 256, 2, 256).transpose(0, 1, 3, 2, 4).reshape(2, 4, 256, 256)
+                            label = label.reshape(2, 256, 2, 256).transpose(0, 2, 1, 3).reshape(4, 256, 256)
+
+                            zero_channel = np.zeros((1, img.shape[1], img.shape[2], img.shape[3]), dtype=img.dtype)
+
+                            # Concatenate the zero channel with the original array along the first dimension
+                            new_array = np.concatenate([img, zero_channel], axis=0)
+                            # reshaped_array = np.transpose(new_array, (1, 2, 3, 0))
+                            for j in range(4):
+                                # imwrite(f'/scratch_2/cell_imaging_2023/tissuenet/tissuenet_v1.0/tissuenet_1_cellpose_way/{folder}/{t}_{p}_{k}_{j}.tif', img[:,j])
+                                # imwrite(f'/scratch_2/cell_imaging_2023/tissuenet/tissuenet_v1.0/tissuenet_1_cellpose_way/{folder}/{t}_{p}_{k}_{j}_masks.tif', label[j])
+                                img_name = f"{folder}/{t}_{p}_{k}_{j}.tif"
+                                mask_name = f"{folder}/{t}_{p}_{k}_{j}_masks.tif"
+                                imageio.imwrite(os.path.join(dataset_dir, "tissuenet_1.0", img_name), new_array[:, j])
+                                imageio.imwrite(os.path.join(dataset_dir, "tissuenet_1.0", mask_name), label[j])
+                        else:
+                            zero_channel = np.zeros((1, img.shape[1], img.shape[2]), dtype=img.dtype)
+                            new_array = np.concatenate([img, zero_channel], axis=0)
+                            # reshaped_array = np.transpose(new_array, (1, 2, 0))
+                            img_name = f"{folder}/{t}_{p}_{k}.tif"
+                            mask_name = f"{folder}/{t}_{p}_{k}_masks.tif"
+                            imageio.imwrite(os.path.join(dataset_dir, "tissuenet_1.0", img_name), new_array)
+                            imageio.imwrite(os.path.join(dataset_dir, "tissuenet_1.0", mask_name), label)
+
+    t_p_combos = [
+        ["breast", "imc"],
+        ["breast", "mibi"],
+        ["breast", "vectra"],
+        ["gi", "codex"],
+        ["gi", "mibi"],
+        ["gi", "mxif"],
+        ["immune", "cycif"],
+        ["immune", "mibi"],
+        ["immune", "vectra"],
+        ["lung", "cycif"],
+        ["lung", "mibi"],
+        ["pancreas", "codex"],
+        ["pancreas", "vectra"],
+        ["skin", "mibi"],
+    ]
+
+    for each_t_p in t_p_combos:
+        json_f_name = "tn_" + each_t_p[0] + "_" + each_t_p[1] + ".json"
+        json_f_subset_path = os.path.join(json_f_path, json_f_name)
+
+        tp_match = each_t_p[0] + "_" + each_t_p[1]
+        train_filenames = get_filenames_exclude_masks(os.path.join(dataset_dir, "tissuenet_1.0", "train"), tp_match)
+        val_filenames = get_filenames_exclude_masks(os.path.join(dataset_dir, "tissuenet_1.0", "val"), tp_match)
+        test_filenames = get_filenames_exclude_masks(os.path.join(dataset_dir, "tissuenet_1.0", "test"), tp_match)
+
+        train_data_list = []
+        test_data_list = []
+
+        for each_tf in train_filenames:
+            t_dict = {
+                "image": os.path.join("tissuenet_dataset", "tissuenet_1.0", "train", each_tf),
+                "label": os.path.join("tissuenet_dataset", "tissuenet_1.0", "train", each_tf[:-4] + "_masks.tif"),
+            }
+            train_data_list.append(t_dict)
+
+        for each_vf in val_filenames:
+            t_dict = {
+                "image": os.path.join("tissuenet_dataset", "tissuenet_1.0", "val", each_vf),
+                "label": os.path.join("tissuenet_dataset", "tissuenet_1.0", "val", each_vf[:-4] + "_masks.tif"),
+            }
+            train_data_list.append(t_dict)
+
+        for each_tf in test_filenames:
+            t_dict = {
+                "image": os.path.join("tissuenet_dataset", "tissuenet_1.0", "test", each_tf),
+                "label": os.path.join("tissuenet_dataset", "tissuenet_1.0", "test", each_tf[:-4] + "_masks.tif"),
+            }
+            test_data_list.append(t_dict)
+
+        # print(train_data_list)
+        # print(test_data_list)
+
+        # Convert data_pairs to a numpy array for easy indexing by KFold
+        data_pairs_array = np.array(train_data_list)
+
+        # Initialize KFold
+        kf = KFold(n_splits=5, shuffle=True, random_state=42)
+
+        # Assign fold numbers
+        for fold, (train_index, val_index) in enumerate(kf.split(data_pairs_array)):
+            for idx in val_index:
+                data_pairs_array[idx]["fold"] = fold
+
+        # Convert the array back to a list and sort by fold
+        sorted_data_pairs = sorted(data_pairs_array.tolist(), key=lambda x: x["fold"])
+
+        print(sorted_data_pairs)
+
+        all_data = {}
+        all_data["training"] = sorted_data_pairs
+        all_data["testing"] = test_data_list
+
+        with open(json_f_subset_path, "w") as j_file:
+            json.dump(all_data, j_file, indent=4)
+        j_file.close()
+
+
+def omnipose_json_file(dataset_dir, json_path):
+    """
+    This function takes in the directory of extracted Omnipose dataset as input
+    and creates a json list with 5 folds. Please note that only 'bact_phase' and 'bact_fluor' were
+    used for creating datasets as they have bacteria the other directiories are worms. Each directory
+    has 'train_sorted' and 'test_sorted'.Separate testing set is recorded in the json list.
+    Please note that there are some hard-coded directory names as per the original dataset.
+    """
+    # Define the folders
+    op_list = ["bact_fluor", "bact_phase"]
+    for each_op in op_list:
+        print(f"Working on {each_op} ...")
+        images_folder = os.path.join(dataset_dir, each_op, "train_sorted")
+        test_images_folder = os.path.join(dataset_dir, each_op, "test_sorted")
+        json_f_path = os.path.join(json_path, f"op_{each_op}.json")
+
+        # Initialize the list for training data
+        training_data = []
+
+        # Loop through each image file to find its corresponding label file
+        sub_dirs = os.listdir(images_folder)
+        # Likely Omnipose dataset was created using a Mac and hence the spare filename
+        sub_dirs.remove(".DS_Store")
+        for each_sub in sub_dirs:
+            # List files in the images folder
+            image_files = os.listdir(os.path.join(images_folder, each_sub))
+            for image_file in image_files:
+                # Extract the name without the extension
+                base_name = os.path.splitext(image_file)[0]
+
+                # Construct the label file name by adding '_label' before the extension
+                label_file = base_name + "_masks.tif"  # + os.path.splitext(image_file)[1]
+                flows_file = base_name + "_flows.tif"
+                # Check if the corresponding label file exists in the labels folder
+                if label_file in os.listdir(os.path.join(images_folder, each_sub)):
+                    # Add the file names to the training data list
+                    training_data.append(
+                        {
+                            "image": os.path.join("omnipose_dataset", each_op, "train_sorted", each_sub, image_file),
+                            "label": os.path.join("omnipose_dataset", each_op, "train_sorted", each_sub, label_file),
+                            "flows": os.path.join("omnipose_dataset", each_op, "train_sorted", each_sub, flows_file),
+                        }
+                    )
+
+        # Convert data_pairs to a numpy array for easy indexing by KFold
+        data_pairs_array = np.array(training_data)
+
+        # Initialize KFold
+        kf = KFold(n_splits=5, shuffle=True, random_state=42)
+
+        # Assign fold numbers
+        for fold, (train_index, val_index) in enumerate(kf.split(data_pairs_array)):
+            for idx in val_index:
+                data_pairs_array[idx]["fold"] = fold
+
+        # Convert the array back to a list and sort by fold
+        sorted_data_pairs = sorted(data_pairs_array.tolist(), key=lambda x: x["fold"])
+
+        # Initialize the list for testing data
+        testing_data = []
+
+        test_sub_dirs = os.listdir(test_images_folder)
+        # Likely Omnipose dataset was created using a Mac and hence the spare filename
+        test_sub_dirs.remove(".DS_Store")
+        # Loop through each image file to find its corresponding label file
+        for each_test_sub in test_sub_dirs:
+            # List files in the images folder
+            test_image_files = os.listdir(os.path.join(test_images_folder, each_test_sub))
+            for image_file in test_image_files:
+                # Extract the name without the extension
+                base_name = os.path.splitext(image_file)[0]
+
+                # Construct the label file name by adding '_label' before the extension
+                label_file = base_name + "_masks.tif"  # + os.path.splitext(image_file)[1]
+
+                # Check if the corresponding label file exists in the labels folder
+                if label_file in os.listdir(os.path.join(test_images_folder, each_test_sub)):
+                    # Add the file names to the training data list
+                    testing_data.append(
+                        {
+                            "image": os.path.join(
+                                "omnipose_dataset", each_op, "test_sorted", each_test_sub, image_file
+                            ),
+                            "label": os.path.join(
+                                "omnipose_dataset", each_op, "test_sorted", each_test_sub, label_file
+                            ),
+                        }
+                    )
+
+        all_data = {}
+        all_data["training"] = sorted_data_pairs
+        all_data["testing"] = testing_data
+
+        # Save the training data list to a JSON file
+        with open(json_f_path, "w") as json_file:
+            json.dump(all_data, json_file, indent=4)
+
+
+def nips_json_file(dataset_dir, json_f_path):
+    """
+    This function takes in the directory of extracted NIPS cell segmentation challenge as input
+    and creates a json list with 5 folds. Separate testing set is recorded in the json list.
+    Please note that there are some hard-coded directory names as per the original dataset.
+    At the time of creation, the NIPS zipfile had 'Training-labeled' and 'Testing' directories that
+    both contained 'images' and 'labels' directories
+    """
+    # The directory containing your files
+    json_save_path = os.path.normpath(json_f_path)
+    directory = os.path.join(dataset_dir, "Training-labeled")
+    test_directory = os.path.join(dataset_dir, "Testing", "Public")
+    # List to hold all image-mask pairs
+    data_pairs = []
+    test_data_pairs = []
+    all_data = {}
+
+    # Scan the directory for image files and create pairs
+    for filename in os.listdir(os.path.join(directory, "images")):
+        if os.path.exists(os.path.join(directory, "images", filename)):
+            # Extract the name without the extension
+            base_name = os.path.splitext(filename)[0]
+
+            # Construct the label file name by adding '_label' before the extension
+            label_file = base_name + "_label.tiff"  # + os.path.splitext(image_file)[1]
+
+            # Check if the corresponding label file exists in the labels folder
+            if label_file in os.listdir(os.path.join(directory, "labels")):
+                # Add the file names to the training data list
+                data_pairs.append(
+                    {
+                        "image": os.path.join("nips_dataset", "Training-labeled", "images", filename),
+                        "label": os.path.join("nips_dataset", "Training-labeled", "labels", label_file),
+                    }
+                )
+
+    # Convert data_pairs to a numpy array for easy indexing by KFold
+    data_pairs_array = np.array(data_pairs)
+
+    # Initialize KFold
+    kf = KFold(n_splits=5, shuffle=True, random_state=42)
+
+    # Assign fold numbers
+    for fold, (train_index, val_index) in enumerate(kf.split(data_pairs_array)):
+        for idx in val_index:
+            data_pairs_array[idx]["fold"] = fold
+
+    # Convert the array back to a list and sort by fold
+    sorted_data_pairs = sorted(data_pairs_array.tolist(), key=lambda x: x["fold"])
+
+    print(sorted_data_pairs)
+
+    # Scan the directory for image files and create pairs
+    for filename in os.listdir(os.path.join(test_directory, "images")):
+        if os.path.exists(os.path.join(test_directory, "images", filename)):
+            # Extract the name without the extension
+            base_name = os.path.splitext(filename)[0]
+
+            # Construct the label file name by adding '_label' before the extension
+            label_file = base_name + "_label.tiff"  # + os.path.splitext(image_file)[1]
+
+            # Check if the corresponding label file exists in the labels folder
+            if label_file in os.listdir(os.path.join(test_directory, "labels")):
+                # Add the file names to the training data list
+                test_data_pairs.append(
+                    {
+                        "image": os.path.join("nips_dataset", "Testing", "Public", "images", filename),
+                        "label": os.path.join("nips_dataset", "Testing", "Public", "labels", label_file),
+                    }
+                )
+
+    all_data["training"] = sorted_data_pairs
+    all_data["testing"] = test_data_pairs
+
+    with open(json_save_path, "w") as j_file:
+        json.dump(all_data, j_file, indent=4)
+    j_file.close()
+
+
+def kaggle_json_file(dataset_dir, json_f_path):
+    """
+    This function takes in the directory of kaggle nuclei extracted dataset as input and
+    creates a json list with 5 folds.
+    Please note that there are some hard-coded directory names as per the original dataset.
+    The function creates an instance processed dataset and then a 5 fold json file based on
+    the instance processed dataset
+    """
+    data_dir = os.path.join(dataset_dir, "stage1_train")
+    saving_path = os.path.join(dataset_dir, "instance_processed_data")
+    if not os.path.exists(saving_path):
+        os.mkdir(saving_path)
+
+    # Process the images and create instance masks first
+    for idx, subdir in enumerate(os.listdir(data_dir)):
+        subdir_path = os.path.join(data_dir, subdir)
+        if os.path.isdir(subdir_path):
+            images_dir = os.path.join(subdir_path, "images")
+            masks_dir = os.path.join(subdir_path, "masks")
+            if os.path.isdir(images_dir) and os.path.isdir(masks_dir):
+                image_file = os.path.join(images_dir, os.listdir(images_dir)[0])
+                filename_prefix = f"kg_bowl_{idx}_"
+
+                mask_data = concatenate_masks(masks_dir)
+
+                # ## Apply channel-wise normalization and use only the first three channels
+                # image_data = imageio.imread(image_file)
+                # normalized_image = normalize_image(image_data[..., :3])
+                # imageio.imwrite(os.path.join(saving_path, f"{filename_prefix}img.tiff"), normalized_image)
+                shutil.copyfile(image_file, os.path.join(saving_path, f"{filename_prefix}img.png"))
+                imageio.imwrite(os.path.join(saving_path, f"{filename_prefix}img_masks.tiff"), mask_data)
+
+    directory = saving_path
+
+    # List to hold all image-mask pairs
+    data_pairs = []
+    all_data = {}
+
+    # Scan the directory for image files and create pairs
+    for filename in os.listdir(directory):
+        if filename.endswith("_img.png"):
+            # Construct the corresponding mask filename
+            mask_filename = filename.replace("_img.png", "_img_masks.tiff")
+
+            # Check if the corresponding mask file exists
+            if os.path.exists(os.path.join(directory, mask_filename)):
+                # Add the pair to the list
+                data_pairs.append(
+                    {
+                        "image": os.path.join("kaggle_dataset", "instance_processed_data", filename),
+                        "label": os.path.join("kaggle_dataset", "instance_processed_data", mask_filename),
+                    }
+                )
+
+    # Convert data_pairs to a numpy array for easy indexing by KFold
+    data_pairs_array = np.array(data_pairs)
+
+    # Initialize KFold
+    kf = KFold(n_splits=5, shuffle=True, random_state=42)
+
+    # Assign fold numbers
+    for fold, (train_index, val_index) in enumerate(kf.split(data_pairs_array)):
+        for idx in val_index:
+            data_pairs_array[idx]["fold"] = fold
+
+    # Convert the array back to a list and sort by fold
+    sorted_data_pairs = sorted(data_pairs_array.tolist(), key=lambda x: x["fold"])
+
+    print(sorted_data_pairs)
+
+    all_data["training"] = sorted_data_pairs
+
+    with open(json_f_path, "w") as j_file:
+        json.dump(all_data, j_file, indent=4)
+    j_file.close()
+
+
+def deepbacs_json_file(dataset_dir, json_f_path):
+    """
+    This function takes in the directory of deepbacs extracted dataset as input and
+    creates a json list with 5 folds. Separate testing set is recorded in the json list.
+    Please note that there are some hard-coded directory names as per the original dataset.
+    At the time of creation, the deepbacs zipfile had 'training' and 'test' directories that
+    both contained 'source' and 'target' directories
+    """
+    # The directory containing your files
+    json_save_path = os.path.normpath(json_f_path)
+    directory = os.path.join(dataset_dir, "training")
+    test_directory = os.path.join(dataset_dir, "test")
+    # List to hold all image-mask pairs
+    data_pairs = []
+    test_data_pairs = []
+    all_data = {}
+
+    # Scan the directory for image files and create pairs
+    for filename in os.listdir(os.path.join(directory, "source")):
+        if os.path.exists(os.path.join(directory, "source", filename)):
+            # Construct the corresponding mask filename
+            mask_filename = filename
+
+            # Check if the corresponding mask file exists
+            if os.path.exists(os.path.join(directory, "target", mask_filename)):
+                # Add the pair to the list
+                data_pairs.append(
+                    {
+                        "image": os.path.join("deepbacs_dataset", "training", "source", filename),
+                        "label": os.path.join("deepbacs_dataset", "training", "target", mask_filename),
+                    }
+                )
+
+    # Convert data_pairs to a numpy array for easy indexing by KFold
+    data_pairs_array = np.array(data_pairs)
+
+    # Initialize KFold
+    kf = KFold(n_splits=5, shuffle=True, random_state=42)
+
+    # Assign fold numbers
+    for fold, (train_index, val_index) in enumerate(kf.split(data_pairs_array)):
+        for idx in val_index:
+            data_pairs_array[idx]["fold"] = fold
+
+    # Convert the array back to a list and sort by fold
+    sorted_data_pairs = sorted(data_pairs_array.tolist(), key=lambda x: x["fold"])
+
+    print(sorted_data_pairs)
+
+    # Scan the directory for image files and create pairs
+    for filename in os.listdir(os.path.join(test_directory, "source")):
+        if os.path.exists(os.path.join(test_directory, "source", filename)):
+            # Construct the corresponding mask filename
+            mask_filename = filename
+
+            # Check if the corresponding mask file exists
+            if os.path.exists(os.path.join(test_directory, "target", mask_filename)):
+                # Add the pair to the list
+                test_data_pairs.append(
+                    {
+                        "image": os.path.join("deepbacs_dataset", "test", "source", filename),
+                        "label": os.path.join("deepbacs_dataset", "test", "target", filename),
+                    }
+                )
+
+    all_data["training"] = sorted_data_pairs
+    all_data["testing"] = test_data_pairs
+
+    with open(json_save_path, "w") as j_file:
+        json.dump(all_data, j_file, indent=4)
+    j_file.close()
+
+
+def cellpose_json_file(dataset_dir, json_f_path):
+    """
+    This function takes in the directory of cellpose extracted dataset as input and
+    creates a json list with 5 folds. Separate testing set is recorded in the json list.
+    Please note that there are some hard-coded directory names as per the original dataset.
+    At the time of creation, the cellpose dataset had 'train.zip' and 'test.zip' that
+    extracted as 'train' and 'test' directories
+    """
+    # The directory containing your files
+    json_save_path = os.path.normpath(json_f_path)
+    directory = os.path.join(dataset_dir, "train")
+    test_directory = os.path.join(dataset_dir, "test")
+
+    # List to hold all image-mask pairs
+    data_pairs = []
+    test_data_pairs = []
+    all_data = {}
+
+    # Scan the directory for image files and create pairs
+    for filename in os.listdir(directory):
+        if filename.endswith("_img.png"):
+            # Construct the corresponding mask filename
+            mask_filename = filename.replace("_img.png", "_masks.png")
+
+            # Check if the corresponding mask file exists
+            if os.path.exists(os.path.normpath(os.path.join(directory, mask_filename))):
+                # Add the pair to the list
+                data_pairs.append(
+                    {
+                        "image": os.path.join("cellpose_dataset", "train", filename),
+                        "label": os.path.join("cellpose_dataset", "train", mask_filename),
+                    }
+                )
+
+    # Convert data_pairs to a numpy array for easy indexing by KFold
+    data_pairs_array = np.array(data_pairs)
+
+    # Initialize KFold
+    kf = KFold(n_splits=5, shuffle=True, random_state=42)
+
+    # Assign fold numbers
+    for fold, (train_index, val_index) in enumerate(kf.split(data_pairs_array)):
+        for idx in val_index:
+            data_pairs_array[idx]["fold"] = fold
+
+    # Convert the array back to a list and sort by fold
+    sorted_data_pairs = sorted(data_pairs_array.tolist(), key=lambda x: x["fold"])
+
+    print(sorted_data_pairs)
+
+    # Scan the directory for image files and create pairs
+    for filename in os.listdir(test_directory):
+        if filename.endswith("_img.png"):
+            # Construct the corresponding mask filename
+            mask_filename = filename.replace("_img.png", "_masks.png")
+
+            # Check if the corresponding mask file exists
+            if os.path.exists(os.path.join(directory, mask_filename)):
+                # Add the pair to the list
+                test_data_pairs.append(
+                    {
+                        "image": os.path.join("cellpose_dataset", "test", filename),
+                        "label": os.path.join("cellpose_dataset", "test", mask_filename),
+                    }
+                )
+
+    all_data["training"] = sorted_data_pairs
+    all_data["testing"] = test_data_pairs
+
+    with open(json_save_path, "w") as j_file:
+        json.dump(all_data, j_file, indent=4)
+    j_file.close()
+
+
+def extract_zip(zip_path, extract_to):
+    # Ensure the target directory exists
+    print(f"Extracting from: {zip_path}")
+    print(f"Extracting to: {extract_to}")
+
+    if not os.path.exists(extract_to):
+        os.makedirs(extract_to)
+
+    # Extract all contents of the zip file to the specified directory
+    with zipfile.ZipFile(zip_path, "r") as zip_ref:
+        zip_ref.extractall(extract_to)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Process some integers.")
+    parser.add_argument("--dir", type=str, help="Directory of datasets to generate json", default="/set/the/path")
+
+    args = parser.parse_args()
+    data_root_path = os.path.normpath(args.dir)
+
+    if not os.path.exists(os.path.join(data_root_path, "json_files")):
+        os.mkdir(os.path.join(data_root_path, "json_files"))
+
+    dataset_dict = {
+        "cellpose_dataset": ["train.zip", "test.zip"],
+        "deepbacs_dataset": ["deepbacs.zip"],
+        "kaggle_dataset": ["data-science-bowl-2018.zip"],
+        "nips_dataset": ["nips_train.zip", "nips_test.zip"],
+        "omnipose_dataset": ["datasets.zip"],
+        "tissuenet_dataset": ["tissuenet_v1.0.zip"],
+        "livecell_dataset": [
+            "livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/images_per_celltype.zip"
+        ],
+    }
+
+    for key, value in dataset_dict.items():
+        dataset_path = os.path.join(data_root_path, key)
+
+        for each_zipped in value:
+            in_path = os.path.join(dataset_path, each_zipped)
+            try:
+                if os.path.exists(in_path):
+                    print(f"File exists at: {in_path}")
+            except:
+                print(f"File: {in_path} was not found")
+            out_path = os.path.join(dataset_path)
+            extract_zip(in_path, out_path)
+
+    print(
+        "If we reached here, that means all zip files got extracted ... Working on pre-processing and generating json files"
+    )
+
+    # Looping over all datasets again, Cellpose & Deepbacs have a similar directory structure
+    for key, value in dataset_dict.items():
+        if key == "cellpose_dataset":
+            print("Creating Cellpose Dataset Json file ...")
+            dataset_path = os.path.join(data_root_path, key)
+            json_path = os.path.join(data_root_path, "json_files", "cellpose.json")
+            cellpose_json_file(dataset_dir=dataset_path, json_f_path=json_path)
+
+        elif key == "nips_dataset":
+            print("Creating NIPS Dataset Json file ...")
+            dataset_path = os.path.join(data_root_path, key)
+            json_path = os.path.join(data_root_path, "json_files", "nips.json")
+            nips_json_file(dataset_dir=dataset_path, json_f_path=json_path)
+
+        elif key == "omnipose_dataset":
+            print("Creating Omnipose Dataset Json files ...")
+            dataset_path = os.path.join(data_root_path, key)
+            json_path = os.path.join(data_root_path, "json_files")
+            omnipose_json_file(dataset_dir=dataset_path, json_path=json_path)
+
+        elif key == "kaggle_dataset":
+            print("Needs additional extraction")
+            train_zip_path = os.path.join(data_root_path, key, "stage1_train.zip")
+            zip_out_path = os.path.join(data_root_path, key, "stage1_train")
+            extract_zip(train_zip_path, zip_out_path)
+            print("Creating Kaggle Dataset Json files ...")
+            dataset_path = os.path.join(data_root_path, key)
+            json_f_path = os.path.join(data_root_path, "json_files", "kaggle.json")
+            kaggle_json_file(dataset_dir=dataset_path, json_f_path=json_f_path)
+
+        elif key == "livecell_dataset":
+            print("Creating LiveCell Dataset Json files ... Please note that 7 files will be created from livecell")
+            dataset_path = os.path.join(data_root_path, key)
+            json_base_name = os.path.join(data_root_path, "json_files")
+            livecell_json_files(dataset_dir=dataset_path, json_f_path=json_base_name)
+
+        elif key == "deepbacs_dataset":
+            print("Creating Deepbacs Dataset Json file ...")
+            dataset_path = os.path.join(data_root_path, key)
+            json_path = os.path.join(data_root_path, "json_files", "deepbacs.json")
+            deepbacs_json_file(dataset_dir=dataset_path, json_f_path=json_path)
+
+        elif key == "tissuenet_dataset":
+            print("Creating TissueNet Dataset Json files ... Please note that 13 files will be created from tissuenet")
+            dataset_path = os.path.join(data_root_path, key)
+            json_base_name = os.path.join(data_root_path, "json_files")
+            tissuenet_json_files(dataset_dir=dataset_path, json_f_path=json_base_name)
+
+    return None
+
+
+if __name__ == "__main__":
+    main()
diff --git a/vista2d/download_preprocessor/kaggle_download.png b/vista2d/download_preprocessor/kaggle_download.png
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diff --git a/vista2d/download_preprocessor/process_data.py b/vista2d/download_preprocessor/process_data.py
new file mode 100644
index 0000000..0d5c8f2
--- /dev/null
+++ b/vista2d/download_preprocessor/process_data.py
@@ -0,0 +1,398 @@
+import argparse
+import gc
+import os
+import shutil
+import time
+import warnings
+import zipfile
+
+import imageio.v3 as imageio
+import numpy as np
+from PIL import Image
+from pycocotools.coco import COCO
+
+
+def min_label_precision(label):
+    lm = label.max()
+
+    if lm <= 255:
+        label = label.astype(np.uint8)
+    elif lm <= 65535:
+        label = label.astype(np.uint16)
+    else:
+        label = label.astype(np.uint32)
+
+    return label
+
+
+def guess_convert_to_uint16(img, margin=30):
+    """
+    Guess a multiplier that makes all pixels integers.
+    The input img (each channel) is already in the range 0..1, they must have been converted from uint16 integers as image / scale,
+    where scale was the unknown max intensity. We could guess the scale by looking at unique values: 1/np.min(np.diff(np.unique(im)).
+    the hypothesis is that it will be more accurate recovery of the original image, instead of doing a simple (img*65535).astype(np.uint16)
+    """
+
+    for i in range(img.shape[0]):
+        im = img[i]
+
+        if im.any():
+            start = time.time()
+            imsmall = im[::4, ::4]  # subsample
+            # imsmall = im
+
+            scale = int(np.round(1 / np.min(np.diff(np.unique(imsmall)))))  # guessing scale
+            test = [
+                (np.sum((imsmall * k) % 1)) for k in range(scale - margin, scale + margin)
+            ]  # finetune, guess a multiplier that makes all pixels integers
+            sid = np.argmin(test)  # fine tune scale
+            # print('guessing scale', scale, test[margin], 'fine tuning scale', scale - margin + sid, 'dif', test[sid], 'time', time.time()-start)
+
+            if scale < 16000 or scale > 16400:
+                # print(imsmall.shape)
+                # print(np.unique(imsmall))
+                # print(np.diff(np.unique(imsmall)))
+                # print(np.min(np.diff(np.unique(imsmall))))
+                warnings.warn("scale not in expected range")
+                print(
+                    "guessing scale",
+                    scale,
+                    test[margin],
+                    "fine tuning scale",
+                    scale - margin + sid,
+                    "dif",
+                    test[sid],
+                    "time",
+                    time.time() - start,
+                )
+
+                scale = 16384
+            else:
+                scale = scale - margin + sid
+
+            scale = min(
+                65535, scale * 4
+            )  # all the recovered scale values seems to be up to 16384, we can stretch to 65535 (for better visualization, most tiff viewers expect that range)
+            img[i] = im * scale
+
+    img = img.astype(np.uint16)
+    return img
+
+
+def concatenate_masks(mask_dir):
+    labeled_mask = None
+    i = 0
+    for filename in sorted(os.listdir(mask_dir)):
+        if filename.endswith(".png"):
+            mask = imageio.imread(os.path.join(mask_dir, filename))
+            if labeled_mask is None:
+                labeled_mask = np.zeros(shape=mask.shape, dtype=np.uint16)
+            labeled_mask[mask > 0] = i
+            i = i + 1
+
+    if i <= 255:
+        labeled_mask = labeled_mask.astype(np.uint8)
+
+    return labeled_mask
+
+
+def get_filenames_exclude_masks(dir1, target_string):
+    filenames = []
+    # Combine lists of files from both directories
+    files = os.listdir(dir1)
+    # Filter files that contain the target string but exclude 'masks'
+    filenames = [f for f in files if target_string in f and "masks" not in f]
+
+    return filenames
+
+
+def remove_overlaps(masks, medians, overlap_threshold=0.75):
+    """replace overlapping mask pixels with mask id of closest mask
+    if mask fully within another mask, remove it
+    masks = Nmasks x Ly x Lx
+    """
+    cellpix = masks.sum(axis=0)
+    igood = np.ones(masks.shape[0], "bool")
+    for i in masks.sum(axis=(1, 2)).argsort():
+        npix = float(masks[i].sum())
+        noverlap = float(masks[i][cellpix > 1].sum())
+        if noverlap / npix >= overlap_threshold:
+            igood[i] = False
+            cellpix[masks[i] > 0] -= 1
+            # print(cellpix.min())
+    print(f"removing {(~igood).sum()} masks")
+    masks = masks[igood]
+    medians = medians[igood]
+    cellpix = masks.sum(axis=0)
+    overlaps = np.array(np.nonzero(cellpix > 1.0)).T
+    dists = ((overlaps[:, :, np.newaxis] - medians.T) ** 2).sum(axis=1)
+    tocell = np.argmin(dists, axis=1)
+    masks[:, overlaps[:, 0], overlaps[:, 1]] = 0
+    masks[tocell, overlaps[:, 0], overlaps[:, 1]] = 1
+
+    # labels should be 1 to mask.shape[0]
+    masks = masks.astype(int) * np.arange(1, masks.shape[0] + 1, 1, int)[:, np.newaxis, np.newaxis]
+    masks = masks.sum(axis=0)
+    gc.collect()
+    return masks
+
+
+def livecell_process_files(dataset_dir):
+    """
+    This function takes in the directory of livecell extracted dataset as input and
+    extracts labels from the coco format.
+    """
+
+    # "A172", "BT474", "Huh7", "MCF7", "SHSY5Y", "SkBr3", "SKOV3"
+    # "BV2" is being skipped, runs into memory constraints
+    cell_type_list = ["A172", "BT474", "Huh7", "MCF7", "SHSY5Y", "SkBr3", "SKOV3"]
+    for each_cell_tp in cell_type_list:
+        for split in ["train", "val", "test"]:
+            print(f"Working on split: {split}")
+
+            if split == "test":
+                img_path = os.path.join(dataset_dir, "images", "livecell_test_images", each_cell_tp)
+                msk_path = os.path.join(dataset_dir, "images", "livecell_test_images", each_cell_tp + "_masks")
+            else:
+                img_path = os.path.join(dataset_dir, "images", "livecell_train_val_images", each_cell_tp)
+                msk_path = os.path.join(dataset_dir, "images", "livecell_train_val_images", each_cell_tp + "_masks")
+            if not os.path.exists(msk_path):
+                os.makedirs(msk_path)
+
+            # annotation path
+            path = os.path.join(
+                dataset_dir,
+                "livecell-dataset.s3.eu-central-1.amazonaws.com",
+                "LIVECell_dataset_2021",
+                "annotations",
+                "LIVECell_single_cells",
+                each_cell_tp.lower(),
+                split + ".json",
+            )
+            annotation = COCO(path)
+            # Convert COCO format segmentation to binary mask
+            images = annotation.loadImgs(annotation.getImgIds())
+            height = []
+            width = []
+            for index, im in enumerate(images):
+                print("Status: {}/{}, Process image: {}".format(index, len(images), im["file_name"]))
+                if (
+                    im["file_name"] == "BV2_Phase_C4_2_03d00h00m_1.tif"
+                    or im["file_name"] == "BV2_Phase_C4_2_03d00h00m_3.tif"
+                ):
+                    print("Skipping the file: BV2_Phase_C4_2_03d00h00m_1.tif, as it is troublesome")
+                    continue
+                # load image
+                img = Image.open(os.path.join(img_path, im["file_name"])).convert("L")
+                height.append(img.size[0])
+                width.append(img.size[1])
+                # arr = np.asarray(img) #? not used
+                # msk = np.zeros(arr.shape)
+                # load and display instance annotations
+                annIds = annotation.getAnnIds(imgIds=im["id"], iscrowd=None)
+                anns = annotation.loadAnns(annIds)
+                idx = 1
+                medians = []
+                masks = []
+                k = 0
+                for ann in anns:
+                    # convert segmentation to binary mask
+                    mask = annotation.annToMask(ann)
+                    masks.append(mask)
+                    ypix, xpix = mask.nonzero()
+                    medians.append(np.array([ypix.mean().astype(np.float32), xpix.mean().astype(np.float32)]))
+                    k += 1
+                    # add instance mask to image mask
+                    # msk = np.add(msk, mask*idx)
+                    # idx += 1
+
+                masks = np.array(masks).astype(np.int8)
+                medians = np.array(medians)
+                masks = remove_overlaps(masks, medians, overlap_threshold=0.75)
+                gc.collect()
+
+                # ## Create new name for the image and also for the mask and save them as .tif format
+                # masks_int32 = masks.astype(np.int32)
+                # mask_pil = Image.fromarray(masks_int32, 'I')
+
+                t_filename = im["file_name"]
+                # cell_type = t_filename.split('_')[0] #? not used
+                new_mask_name = t_filename[:-4] + "_masks.tif"
+                # mask_pil.save(os.path.join(msk_path, new_mask_name))
+                imageio.imwrite(os.path.join(msk_path, new_mask_name), min_label_precision(masks))
+                gc.collect()
+
+            print(f"In total {len(images)} images")
+
+
+def tissuenet_process_files(dataset_dir):
+    """
+    This function takes in the directory of TissueNet extracted dataset as input and
+    creates tiled images into 4 from each image
+    """
+
+    for folder in ["train", "val", "test"]:
+        if not os.path.exists(os.path.join(dataset_dir, "tissuenet_1.0", folder)):
+            os.mkdir(os.path.join(dataset_dir, "tissuenet_1.0", folder))
+
+    for folder in ["train", "val", "test"]:
+        print(f"Working on {folder} directory of tissuenet")
+        f_name = f"tissuenet_1.0/tissuenet_v1.0_{folder}.npz"
+        dat = np.load(os.path.join(dataset_dir, f_name))
+        data = dat["X"]
+        labels = dat["y"]
+        tissues = dat["tissue_list"]
+        platforms = dat["platform_list"]
+        tlabels = np.unique(tissues)
+        plabels = np.unique(platforms)
+        tp = 0
+        for t in tlabels:
+            for p in plabels:
+                ix = ((tissues == t) * (platforms == p)).nonzero()[0]
+                tp += 1
+                if len(ix) > 0:
+                    print(f"Working on {t} {p}")
+
+                    for k, i in enumerate(ix):
+                        print(f"Status: {k}/{len(ix)} {tp}/{len(tlabels) * len(plabels)} {t} {p}")
+                        img = data[i].transpose(2, 0, 1)
+                        label = labels[i][:, :, 0]
+
+                        img = guess_convert_to_uint16(img)  # guess inverse scale and convert to uint16
+                        label = min_label_precision(label)
+
+                        if folder == "train":
+                            img = img.reshape(2, 2, 256, 2, 256).transpose(0, 1, 3, 2, 4).reshape(2, 4, 256, 256)
+                            label = label.reshape(2, 256, 2, 256).transpose(0, 2, 1, 3).reshape(4, 256, 256)
+
+                            zero_channel = np.zeros((1, img.shape[1], img.shape[2], img.shape[3]), dtype=img.dtype)
+
+                            # Concatenate the zero channel with the original array along the first dimension
+                            new_array = np.concatenate([img, zero_channel], axis=0)
+                            # reshaped_array = np.transpose(new_array, (1, 2, 3, 0))
+                            for j in range(4):
+                                # imwrite(f'/scratch_2/cell_imaging_2023/tissuenet/tissuenet_v1.0/tissuenet_1_cellpose_way/{folder}/{t}_{p}_{k}_{j}.tif', img[:,j])
+                                # imwrite(f'/scratch_2/cell_imaging_2023/tissuenet/tissuenet_v1.0/tissuenet_1_cellpose_way/{folder}/{t}_{p}_{k}_{j}_masks.tif', label[j])
+                                img_name = f"{folder}/{t}_{p}_{k}_{j}.tif"
+                                mask_name = f"{folder}/{t}_{p}_{k}_{j}_masks.tif"
+                                imageio.imwrite(os.path.join(dataset_dir, "tissuenet_1.0", img_name), new_array[:, j])
+                                imageio.imwrite(os.path.join(dataset_dir, "tissuenet_1.0", mask_name), label[j])
+                        else:
+                            zero_channel = np.zeros((1, img.shape[1], img.shape[2]), dtype=img.dtype)
+                            new_array = np.concatenate([img, zero_channel], axis=0)
+                            # reshaped_array = np.transpose(new_array, (1, 2, 0))
+                            img_name = f"{folder}/{t}_{p}_{k}.tif"
+                            mask_name = f"{folder}/{t}_{p}_{k}_masks.tif"
+                            imageio.imwrite(os.path.join(dataset_dir, "tissuenet_1.0", img_name), new_array)
+                            imageio.imwrite(os.path.join(dataset_dir, "tissuenet_1.0", mask_name), label)
+
+
+def kaggle_process_files(dataset_dir):
+    """
+    This function takes in the directory of kaggle nuclei extracted dataset as input and
+    creates a json list with 5 folds.
+    Please note that there are some hard-coded directory names as per the original dataset.
+    The function creates an instance processed dataset and then a 5 fold json file based on
+    the instance processed dataset
+    """
+    data_dir = os.path.join(dataset_dir, "stage1_train")
+    saving_path = os.path.join(dataset_dir, "instance_processed_data")
+    if not os.path.exists(saving_path):
+        os.mkdir(saving_path)
+
+    # Process the images and create instance masks first
+    for idx, subdir in enumerate(os.listdir(data_dir)):
+        subdir_path = os.path.join(data_dir, subdir)
+        if os.path.isdir(subdir_path):
+            images_dir = os.path.join(subdir_path, "images")
+            masks_dir = os.path.join(subdir_path, "masks")
+            if os.path.isdir(images_dir) and os.path.isdir(masks_dir):
+                image_file = os.path.join(images_dir, os.listdir(images_dir)[0])
+                filename_prefix = f"kg_bowl_{idx}_"
+
+                mask_data = concatenate_masks(masks_dir)
+
+                # ## Apply channel-wise normalization and use only the first three channels
+                # image_data = imageio.imread(image_file)
+                # normalized_image = normalize_image(image_data[..., :3])
+                # imageio.imwrite(os.path.join(saving_path, f"{filename_prefix}img.tiff"), normalized_image)
+                shutil.copyfile(image_file, os.path.join(saving_path, f"{filename_prefix}img.png"))
+                imageio.imwrite(os.path.join(saving_path, f"{filename_prefix}img_masks.tiff"), mask_data)
+
+
+def extract_zip(zip_path, extract_to):
+    # Ensure the target directory exists
+    print(f"Extracting from: {zip_path}")
+    print(f"Extracting to: {extract_to}")
+
+    if not os.path.exists(extract_to):
+        os.makedirs(extract_to)
+
+    # Extract all contents of the zip file to the specified directory
+    with zipfile.ZipFile(zip_path, "r") as zip_ref:
+        zip_ref.extractall(extract_to)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Script to process the cell imaging datasets")
+    parser.add_argument("--dir", type=str, help="Directory of datasets to process it ...", default="/set/the/path")
+
+    args = parser.parse_args()
+    data_root_path = os.path.normpath(args.dir)
+
+    dataset_dict = {
+        "cellpose_dataset": ["train.zip", "test.zip"],
+        "deepbacs_dataset": ["deepbacs.zip"],
+        "kaggle_dataset": ["data-science-bowl-2018.zip"],
+        "nips_dataset": ["nips_train.zip", "nips_test.zip"],
+        "omnipose_dataset": ["datasets.zip"],
+        "tissuenet_dataset": ["tissuenet_v1.0.zip"],
+        "livecell_dataset": [
+            "livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/images_per_celltype.zip"
+        ],
+    }
+
+    for key, value in dataset_dict.items():
+        dataset_path = os.path.join(data_root_path, key)
+
+        for each_zipped in value:
+            in_path = os.path.join(dataset_path, each_zipped)
+            try:
+                if os.path.exists(in_path):
+                    print(f"File exists at: {in_path}")
+            except:
+                print(f"File: {in_path} was not found")
+            out_path = os.path.join(dataset_path)
+            extract_zip(in_path, out_path)
+
+    print("If we reached here, that means all zip files got extracted ... Working on pre-processing")
+
+    # Looping over all datasets again, Cellpose & Deepbacs have a similar directory structure
+    for key, value in dataset_dict.items():
+        if key == "kaggle_dataset":
+            print("Needs additional extraction")
+            train_zip_path = os.path.join(data_root_path, key, "stage1_train.zip")
+            zip_out_path = os.path.join(data_root_path, key, "stage1_train")
+            extract_zip(train_zip_path, zip_out_path)
+            print("Processing Kaggle Dataset ...")
+            dataset_path = os.path.join(data_root_path, key)
+            kaggle_process_files(dataset_dir=dataset_path)
+
+        elif key == "livecell_dataset":
+            print("Processing LiveCell Dataset ...")
+            print(
+                "Fyi, this processing might take upto an hour, coffee break might be more fruitful in the meanwhile ..."
+            )
+            dataset_path = os.path.join(data_root_path, key)
+            livecell_process_files(dataset_dir=dataset_path)
+
+        elif key == "tissuenet_dataset":
+            print("Processing TissueNet Dataset ...")
+            dataset_path = os.path.join(data_root_path, key)
+            tissuenet_process_files(dataset_dir=dataset_path)
+
+    return None
+
+
+if __name__ == "__main__":
+    main()
diff --git a/vista2d/download_preprocessor/readme.md b/vista2d/download_preprocessor/readme.md
new file mode 100644
index 0000000..975f4b3
--- /dev/null
+++ b/vista2d/download_preprocessor/readme.md
@@ -0,0 +1,73 @@
+## Tutorial: VISTA2D Model Creation
+
+This tutorial will guide the users to setting up all the datasets, running pre-processing, creation of organized json file lists which can be provided to VISTA-2D training pipeline.
+Some dataset need to be manually downloaded, others will be downloaded by a provided script. Please do not manually unzip any of the downloaded files, it will be automatically handled in the final step.
+
+### List of Datasets
+1.) [Cellpose](https://www.cellpose.org/dataset)
+
+2.) [TissueNet](https://datasets.deepcell.org/login)
+
+3.) [Kaggle Nuclei Segmentation](https://www.kaggle.com/c/data-science-bowl-2018/data)
+
+4.) [Omnipose - OSF repository](https://osf.io/xmury/)
+
+5.) [NIPS Cell Segmentation Challenge](https://neurips22-cellseg.grand-challenge.org/)
+
+6.) [LiveCell](https://sartorius-research.github.io/LIVECell/)
+
+7.) [Deepbacs](https://github.com/HenriquesLab/DeepBacs/wiki/Segmentation)
+
+Datasets 1-4 need to be manually downloaded, instructions to download them have been provided below.
+
+### Manual Dataset Download Instructions
+#### 1.) Cellpose:
+The dataset can be downloaded from this [link](https://www.cellpose.org/dataset). Please see below screenshots to assist in downloading it
+![cellpose_agreement.png](cellpose_agreement.png)
+Please enter your email and accept terms and conditions to download the dataset.
+
+![cellpose_links.png](cellpose_links.png)
+Click on train.zip and test.zip to download both directories independently. They both need to be placed in a `cellpose_dataset` directory. The `cellpose_dataset` will have to be created by the user in the root data directory.
+
+#### 2.) TissueNet
+Login credentials have to be created at below provided link. Please see below screenshots for further assistance.
+
+![tissuenet_login.png](tissuenet_login.png)
+Please create an account at the provided [link](https://datasets.deepcell.org/login).
+
+![tissuenet_download.png](tissuenet_download.png)
+After logging in, the above page will be visible, please make sure that version 1.0 is selected for TissueNet before clicking on download button.
+All the downloaded files need to be placed in a `tissuenet_dataset` directory, this directory has to be created by the user.
+
+#### 3.) Kaggle Nuclei Segmentation
+Kaggle credentials are required in order to access this dataset at this [link](https://www.kaggle.com/c/data-science-bowl-2018/data), the user will have to register for the challenge to access and download the dataset.
+Please refer below screenshots for additional help.
+
+![kaggle_download.png](kaggle_download.png)
+The `Download All` button needs to be used so all files are downloaded, the files need to be placed in a directory created by the user `kaggle_dataset`.
+
+#### 4.) Omnipose
+The Omnipose dataset is hosted on an [OSF repository](https://osf.io/xmury/) and the dataset part needs to be downloaded from it. Please refer below screenshots for further assistance.
+
+![omnipose_download.png](omnipose_download.png)
+The `datasets` directory needs to be selected as highlighted in the screenshot, then `download as zip` needs to be pressed for downloading the dataset. The user will have to place all the files in
+a user created directory named `omnipose_dataset`.
+
+### The remaining datasets will be downloaded by a python script.
+To run the script use the following example command `python all_file_downloader.py --download_path provide_the_same_root_data_path`
+
+After completion of downloading of all datasets, below is how the data root directory should look:
+
+![data_tree.png](data_tree.png)
+
+### Process the downloaded data
+To execute VISTA-2D training pipeline, some datasets require label conversion. Please use the `root_data_path` as the input to the script, example command to execute the script is given below:
+
+`python generate_json.py --data_root provide_the_same_root_data_path`
+
+### Generation of Json data lists (Optional)
+If one desires to generate JSON files from scratch, `generate_json.py` script performs both processing and creation of JSON files.
+To execute VISTA-2D training pipeline, some datasets require label conversion and then a json file list which the VISTA-2D training uses a format.
+Creating the json lists from the raw dataset sources, please use the `root_data_path` as the input to the script, example command to execute the script is given below:
+
+`python generate_json.py --data_root provide_the_same_root_data_path`
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diff --git a/vista2d/download_preprocessor/urls.txt b/vista2d/download_preprocessor/urls.txt
new file mode 100644
index 0000000..b85707f
--- /dev/null
+++ b/vista2d/download_preprocessor/urls.txt
@@ -0,0 +1,39 @@
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LICENSE
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell/
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/a172/
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/a172/test.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/a172/train.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/a172/val.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/bt474/
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/bt474/test.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/bt474/train.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/bt474/val.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/bv2/
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/bv2/test.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/bv2/train.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/bv2/val.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/huh7/
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/huh7/test.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/huh7/train.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/huh7/val.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/mcf7/
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/mcf7/test.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/mcf7/train.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/mcf7/val.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/shsy5y/
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/shsy5y/test.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/shsy5y/train.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/shsy5y/val.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/skbr3/
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/skbr3/test.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/skbr3/train.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/skbr3/val.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/skov3/
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/skov3/test.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/skov3/train.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell_single_cells/skov3/val.json
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/images_per_celltype.zip
+http://livecell-dataset.s3.eu-central-1.amazonaws.com/README.md
diff --git a/vista2d/large_files.yml b/vista2d/large_files.yml
new file mode 100644
index 0000000..a8b9337
--- /dev/null
+++ b/vista2d/large_files.yml
@@ -0,0 +1,3 @@
+large_files:
+- path: "models/sam_vit_b_01ec64.pth"
+  url: "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth"
diff --git a/vista2d/license.code b/vista2d/license.code
new file mode 100644
index 0000000..f49a4e1
--- /dev/null
+++ b/vista2d/license.code
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
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+
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+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
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+
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+      transformation or translation of a Source form, including but
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+
+      "Work" shall mean the work of authorship, whether in Source or
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+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
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+      "Contributor" shall mean Licensor and any individual or Legal Entity
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+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
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+   3. Grant of Patent License. Subject to the terms and conditions of
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\ No newline at end of file
diff --git a/vista2d/license.weights b/vista2d/license.weights
new file mode 100644
index 0000000..7cdbe0b
--- /dev/null
+++ b/vista2d/license.weights
@@ -0,0 +1,438 @@
+Attribution-NonCommercial-ShareAlike 4.0 International
+
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+
diff --git a/vista2d/scripts/__init__.py b/vista2d/scripts/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/vista2d/scripts/cell_distributed_weighted_sampler.py b/vista2d/scripts/cell_distributed_weighted_sampler.py
new file mode 100644
index 0000000..6e690b9
--- /dev/null
+++ b/vista2d/scripts/cell_distributed_weighted_sampler.py
@@ -0,0 +1,119 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# based on Pytorch DistributedSampler and WeightedRandomSampler combined
+
+import math
+from typing import Iterator, Optional, Sequence, TypeVar
+
+import torch
+import torch.distributed as dist
+from torch.utils.data import Dataset, Sampler
+
+__all__ = ["DistributedWeightedSampler"]
+
+T_co = TypeVar("T_co", covariant=True)
+
+
+class DistributedWeightedSampler(Sampler[T_co]):
+    def __init__(
+        self,
+        dataset: Dataset,
+        weights: Sequence[float],
+        num_samples: int,
+        num_replicas: Optional[int] = None,
+        rank: Optional[int] = None,
+        shuffle: bool = True,
+        seed: int = 0,
+        drop_last: bool = False,
+    ) -> None:
+        if not isinstance(num_samples, int) or isinstance(num_samples, bool) or num_samples <= 0:
+            raise ValueError(f"num_samples should be a positive integer value, but got num_samples={num_samples}")
+
+        weights_tensor = torch.as_tensor(weights, dtype=torch.float)
+        if len(weights_tensor.shape) != 1:
+            raise ValueError(
+                "weights should be a 1d sequence but given " f"weights have shape {tuple(weights_tensor.shape)}"
+            )
+
+        self.weights = weights_tensor
+        self.num_samples = num_samples
+
+        if num_replicas is None:
+            if not dist.is_available():
+                raise RuntimeError("Requires distributed package to be available")
+            num_replicas = dist.get_world_size()
+        if rank is None:
+            if not dist.is_available():
+                raise RuntimeError("Requires distributed package to be available")
+            rank = dist.get_rank()
+        if rank >= num_replicas or rank < 0:
+            raise ValueError(f"Invalid rank {rank}, rank should be in the interval [0, {num_replicas - 1}]")
+        self.dataset = dataset
+        self.num_replicas = num_replicas
+        self.rank = rank
+        self.epoch = 0
+        self.drop_last = drop_last
+        self.shuffle = shuffle
+
+        if self.shuffle:
+            self.num_samples = int(math.ceil(self.num_samples / self.num_replicas))
+        else:
+            # this is not used, as we always shuffle, the only reason to use this class
+
+            # If the dataset length is evenly divisible by # of replicas, then there
+            # is no need to drop any data, since the dataset will be split equally.
+            if self.drop_last and len(self.dataset) % self.num_replicas != 0:  # type: ignore[arg-type]
+                # Split to nearest available length that is evenly divisible.
+                # This is to ensure each rank receives the same amount of data when
+                # using this Sampler.
+                self.num_samples = math.ceil(
+                    (len(self.dataset) - self.num_replicas) / self.num_replicas  # type: ignore[arg-type]
+                )
+            else:
+                self.num_samples = math.ceil(len(self.dataset) / self.num_replicas)  # type: ignore[arg-type]
+
+        self.total_size = self.num_samples * self.num_replicas
+        self.shuffle = shuffle
+        self.seed = seed
+
+    def __iter__(self) -> Iterator[T_co]:
+        if self.shuffle:
+            # deterministically shuffle based on epoch and seed
+            g = torch.Generator()
+            g.manual_seed(self.seed + self.epoch)
+            indices = torch.multinomial(input=self.weights, num_samples=self.total_size, replacement=True, generator=g).tolist()  # type: ignore[arg-type]
+        else:
+            # this is not used, as we always shuffle, the only reason to use this class
+            indices = list(range(len(self.dataset)))  # type: ignore[arg-type]
+            if not self.drop_last:
+                # add extra samples to make it evenly divisible
+                padding_size = self.total_size - len(indices)
+                if padding_size <= len(indices):
+                    indices += indices[:padding_size]
+                else:
+                    indices += (indices * math.ceil(padding_size / len(indices)))[:padding_size]
+            else:
+                # remove tail of data to make it evenly divisible.
+                indices = indices[: self.total_size]
+            assert len(indices) == self.total_size
+
+        # subsample
+        indices = indices[self.rank : self.total_size : self.num_replicas]
+        assert len(indices) == self.num_samples
+
+        return iter(indices)
+
+    def __len__(self) -> int:
+        return self.num_samples
+
+    def set_epoch(self, epoch: int) -> None:
+        self.epoch = epoch
diff --git a/vista2d/scripts/cell_sam_wrapper.py b/vista2d/scripts/cell_sam_wrapper.py
new file mode 100644
index 0000000..a2f08be
--- /dev/null
+++ b/vista2d/scripts/cell_sam_wrapper.py
@@ -0,0 +1,70 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+from segment_anything.build_sam import build_sam_vit_b
+from torch import nn
+from torch.nn import functional as F
+
+
+class CellSamWrapper(torch.nn.Module):
+    def __init__(
+        self,
+        auto_resize_inputs=True,
+        network_resize_roi=[1024, 1024],
+        checkpoint="sam_vit_b_01ec64.pth",
+        return_features=False,
+        *args,
+        **kwargs,
+    ) -> None:
+        super().__init__(*args, **kwargs)
+
+        print(
+            f"CellSamWrapper auto_resize_inputs {auto_resize_inputs} network_resize_roi {network_resize_roi} checkpoint {checkpoint}"
+        )
+        self.network_resize_roi = network_resize_roi
+        self.auto_resize_inputs = auto_resize_inputs
+        self.return_features = return_features
+
+        model = build_sam_vit_b(checkpoint=checkpoint)
+
+        model.prompt_encoder = None
+        model.mask_decoder = None
+
+        model.mask_decoder = nn.Sequential(
+            nn.BatchNorm2d(num_features=256),
+            nn.ReLU(inplace=True),
+            nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False),
+            nn.BatchNorm2d(num_features=128),
+            nn.ReLU(inplace=True),
+            nn.ConvTranspose2d(128, 3, kernel_size=3, stride=2, padding=1, output_padding=1, bias=True),
+        )
+
+        self.model = model
+
+    def forward(self, x):
+        # print("CellSamWrapper x0", x.shape)
+        sh = x.shape[2:]
+
+        if self.auto_resize_inputs:
+            x = F.interpolate(x, size=self.network_resize_roi, mode="bilinear")
+
+        # print("CellSamWrapper x1", x.shape)
+        x = self.model.image_encoder(x)  # shape: (1, 256, 64, 64)
+        # print("CellSamWrapper image_embeddings", x.shape)
+
+        if not self.return_features:
+            x = self.model.mask_decoder(x)
+            if self.auto_resize_inputs:
+                x = F.interpolate(x, size=sh, mode="bilinear")
+
+        # print("CellSamWrapper x final", x.shape)
+        return x
diff --git a/vista2d/scripts/components.py b/vista2d/scripts/components.py
new file mode 100644
index 0000000..c59f640
--- /dev/null
+++ b/vista2d/scripts/components.py
@@ -0,0 +1,309 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import json
+import os
+
+import cv2
+import fastremap
+import numpy as np
+import PIL
+import tifffile
+import torch
+import torch.nn.functional as F
+from cellpose.dynamics import compute_masks, masks_to_flows
+from cellpose.metrics import _intersection_over_union, _true_positive
+from monai.apps import get_logger
+from monai.data import MetaTensor
+from monai.transforms import MapTransform
+from monai.utils import ImageMetaKey, convert_to_dst_type
+
+logger = get_logger("VistaCell")
+
+
+class LoadTiffd(MapTransform):
+    def __call__(self, data):
+        d = dict(data)
+        for key in self.key_iterator(d):
+            filename = d[key]
+
+            extension = os.path.splitext(filename)[1][1:]
+            image_size = None
+
+            if extension in ["tif", "tiff"]:
+                img_array = tifffile.imread(filename)  # use tifffile for tif images
+                image_size = img_array.shape
+                if len(img_array.shape) == 3 and img_array.shape[-1] <= 3:
+                    img_array = np.transpose(img_array, (2, 0, 1))  # channels first without transpose
+            else:
+                img_array = np.array(PIL.Image.open(filename))  # PIL for all other images (png, jpeg)
+                image_size = img_array.shape
+                if len(img_array.shape) == 3:
+                    img_array = np.transpose(img_array, (2, 0, 1))  # channels first
+
+            if len(img_array.shape) not in [2, 3]:
+                raise ValueError(
+                    "Unsupported image dimensions, filename " + str(filename) + " shape " + str(img_array.shape)
+                )
+
+            if len(img_array.shape) == 2:
+                img_array = img_array[np.newaxis]  # add channels_first if no channel
+
+            if key == "label":
+                if img_array.shape[0] > 1:
+                    print(
+                        f"Strange case, label with several channels {filename} shape {img_array.shape}, keeping only first"
+                    )
+                    img_array = img_array[[0]]
+
+            elif key == "image":
+                if img_array.shape[0] == 1:
+                    img_array = np.repeat(img_array, repeats=3, axis=0)  # if grayscale, repeat as 3 channels
+                elif img_array.shape[0] == 2:
+                    print(
+                        f"Strange case, image with 2 channels {filename} shape {img_array.shape}, appending first channel to make 3"
+                    )
+                    img_array = np.stack(
+                        (img_array[0], img_array[1], img_array[0]), axis=0
+                    )  # this should not happen, we got 2 channel input image
+                elif img_array.shape[0] > 3:
+                    print(f"Strange case, image with >3 channels,  {filename} shape {img_array.shape}, keeping first 3")
+                    img_array = img_array[:3]
+
+            meta_data = {ImageMetaKey.FILENAME_OR_OBJ: filename, ImageMetaKey.SPATIAL_SHAPE: image_size}
+            d[key] = MetaTensor.ensure_torch_and_prune_meta(img_array, meta_data)
+
+        return d
+
+
+class SaveTiffd(MapTransform):
+    def __init__(self, output_dir, data_root_dir="/", nested_folder=False, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        self.output_dir = output_dir
+        self.data_root_dir = data_root_dir
+        self.nested_folder = nested_folder
+
+    def set_data_root_dir(self, data_root_dir):
+        self.data_root_dir = data_root_dir
+
+    def __call__(self, data):
+        d = dict(data)
+        os.makedirs(self.output_dir, exist_ok=True)
+
+        for key in self.key_iterator(d):
+            seg = d[key]
+            filename = seg.meta[ImageMetaKey.FILENAME_OR_OBJ]
+
+            basename = os.path.splitext(os.path.basename(filename))[0]
+
+            if self.nested_folder:
+                reldir = os.path.relpath(os.path.dirname(filename), self.data_root_dir)
+                outdir = os.path.join(self.output_dir, reldir)
+                os.makedirs(outdir, exist_ok=True)
+            else:
+                outdir = self.output_dir
+
+            outname = os.path.join(outdir, basename + ".tif")
+
+            label = seg.cpu().numpy()
+            lm = label.max()
+            if lm <= 255:
+                label = label.astype(np.uint8)
+            elif lm <= 65535:
+                label = label.astype(np.uint16)
+            else:
+                label = label.astype(np.uint32)
+
+            tifffile.imwrite(outname, label)
+
+            print(f"Saving {outname} shape {label.shape} max {label.max()} dtype {label.dtype}")
+
+        return d
+
+
+class LabelsToFlows(MapTransform):
+    # based on dynamics labels_to_flows()
+    # created a 3 channel output (foreground, flowx, flowy) and saves under flow (new) key
+
+    def __init__(self, flow_key, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        self.flow_key = flow_key
+
+    def __call__(self, data):
+        d = dict(data)
+        for key in self.key_iterator(d):
+            label = d[key].int().numpy()
+
+            label = fastremap.renumber(label, in_place=True)[0]
+            veci = masks_to_flows(label[0], device=None)
+
+            flows = np.concatenate((label > 0.5, veci), axis=0).astype(np.float32)
+            flows = convert_to_dst_type(flows, d[key], dtype=torch.float, device=d[key].device)[0]
+            d[self.flow_key] = flows
+            # meta_data = {ImageMetaKey.FILENAME_OR_OBJ : filename}
+            # d[key] = MetaTensor.ensure_torch_and_prune_meta(img_array, meta_data)
+        return d
+
+
+class LogitsToLabels:
+    def __call__(self, logits, filename=None):
+        device = logits.device
+        logits = logits.float().cpu().numpy()
+        dP = logits[1:]  # vectors
+        cellprob = logits[0]  # foreground prob (logit)
+
+        try:
+            pred_mask, p = compute_masks(
+                dP,
+                cellprob,
+                niter=200,
+                cellprob_threshold=0.4,
+                flow_threshold=0.4,
+                interp=True,
+                device=device,
+            )
+        except RuntimeError as e:
+            logger.warning(f"compute_masks failed on GPU retrying on CPU {logits.shape} file {filename} {e}")
+            pred_mask, p = compute_masks(
+                dP,
+                cellprob,
+                niter=200,
+                cellprob_threshold=0.4,
+                flow_threshold=0.4,
+                interp=True,
+                device=None,
+            )
+
+        return pred_mask, p
+
+
+class LogitsToLabelsd(MapTransform):
+    def __call__(self, data):
+        d = dict(data)
+        f = LogitsToLabels()
+        for key in self.key_iterator(d):
+            pred_mask, p = f(d[key])
+            d[key] = pred_mask
+            d[f"{key}_centroids"] = p
+        return d
+
+
+class SaveTiffExd(MapTransform):
+    def __init__(self, output_dir, output_ext=".png", output_postfix="seg", image_key="image", *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        self.output_dir = output_dir
+        self.output_ext = output_ext
+        self.output_postfix = output_postfix
+        self.image_key = image_key
+
+    def to_polygons(self, contours):
+        polygons = []
+        for contour in contours:
+            if len(contour) < 3:
+                continue
+            polygons.append(np.squeeze(contour).astype(int).tolist())
+        return polygons
+
+    def __call__(self, data):
+        d = dict(data)
+
+        output_dir = d.get("output_dir", self.output_dir)
+        output_ext = d.get("output_ext", self.output_ext)
+        overlayed_masks = d.get("overlayed_masks", False)
+        output_contours = d.get("output_contours", False)
+
+        os.makedirs(self.output_dir, exist_ok=True)
+
+        img = d.get(self.image_key, None)
+        filename = img.meta.get(ImageMetaKey.FILENAME_OR_OBJ) if img is not None else None
+        image_size = img.meta.get(ImageMetaKey.SPATIAL_SHAPE) if img is not None else None
+        basename = os.path.splitext(os.path.basename(filename))[0] if filename else "mask"
+        logger.info(f"File: {filename}; Base: {basename}")
+
+        for key in self.key_iterator(d):
+            label = d[key]
+            output_filename = f"{basename}{'_' + self.output_postfix if self.output_postfix else ''}{output_ext}"
+            output_filepath = os.path.join(output_dir, output_filename)
+            lm = label.max()
+            logger.info(f"Mask Shape: {label.shape}; Instances: {lm}")
+
+            if lm <= 255:
+                label = label.astype(np.uint8)
+            elif lm <= 65535:
+                label = label.astype(np.uint16)
+            else:
+                label = label.astype(np.uint32)
+
+            tifffile.imwrite(output_filepath, label)
+            logger.info(f"Saving {output_filepath}")
+
+            polygons = []
+            if overlayed_masks:
+                logger.info(f"Overlay Masks: Reading original Image: {filename}")
+                image = cv2.imread(filename)
+                mask = cv2.imread(output_filepath, 0)
+
+                for i in range(1, np.max(mask)):
+                    m = np.zeros_like(mask)
+                    m[mask == i] = 1
+                    color = np.random.choice(range(256), size=3).tolist()
+                    contours, _ = cv2.findContours(m, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+                    polygons.extend(self.to_polygons(contours))
+                    cv2.drawContours(image, contours, -1, color, 1)
+                cv2.imwrite(output_filepath, image)
+                logger.info(f"Overlay Masks: Saving {output_filepath}")
+            else:
+                contours, _ = cv2.findContours(label, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+                polygons.extend(self.to_polygons(contours))
+
+            meta_json = {"image_size": image_size, "contours": len(polygons)}
+            with open(os.path.join(output_dir, "meta.json"), "w") as fp:
+                json.dump(meta_json, fp, indent=2)
+
+            if output_contours:
+                logger.info(f"Total Polygons: {len(polygons)}")
+                with open(os.path.join(output_dir, "contours.json"), "w") as fp:
+                    json.dump({"count": len(polygons), "contours": polygons}, fp, indent=2)
+
+        return d
+
+
+# Loss (adopted from Cellpose)
+class CellLoss:
+    def __call__(self, y_pred, y):
+        loss = 0.5 * F.mse_loss(y_pred[:, 1:], 5 * y[:, 1:]) + F.binary_cross_entropy_with_logits(
+            y_pred[:, [0]], y[:, [0]]
+        )
+        return loss
+
+
+# Accuracy (adopted from Cellpose)
+class CellAcc:
+    def __call__(self, mask_pred, mask_true):
+        if isinstance(mask_true, torch.Tensor):
+            mask_true = mask_true.cpu().numpy()
+
+        if isinstance(mask_pred, torch.Tensor):
+            mask_pred = mask_pred.cpu().numpy()
+
+        # print("CellAcc mask_true", mask_true.shape, 'max', np.max(mask_true), ",
+        #       "'mask_pred', mask_pred.shape,  'max', np.max(mask_pred) )
+
+        iou = _intersection_over_union(mask_true, mask_pred)[1:, 1:]
+        tp = _true_positive(iou, th=0.5)
+
+        fp = np.max(mask_pred) - tp
+        fn = np.max(mask_true) - tp
+        ap = tp / (tp + fp + fn)
+
+        # print("CellAcc ap", ap, 'tp', tp, 'fp', fp,  'fn', fn)
+        return ap
diff --git a/vista2d/scripts/utils.py b/vista2d/scripts/utils.py
new file mode 100644
index 0000000..843bc49
--- /dev/null
+++ b/vista2d/scripts/utils.py
@@ -0,0 +1,88 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import os
+import warnings
+from logging.config import fileConfig
+from pathlib import Path
+
+import numpy as np
+from monai.apps import get_logger
+from monai.apps.utils import DEFAULT_FMT
+from monai.bundle import ConfigParser
+from monai.utils import RankFilter, ensure_tuple
+
+logger = get_logger("VistaCell")
+
+np.set_printoptions(formatter={"float": "{: 0.3f}".format}, suppress=True)
+logging.getLogger("torch.nn.parallel.distributed").setLevel(logging.WARNING)
+warnings.filterwarnings("ignore", message=".*Divide by zero.*")  # intensity transform divide by zero warning
+
+LOGGING_CONFIG = {
+    "version": 1,
+    "disable_existing_loggers": False,
+    "formatters": {"monai_default": {"format": DEFAULT_FMT}},
+    "loggers": {
+        "VistaCell": {"handlers": ["file", "console"], "level": "DEBUG", "propagate": False},
+    },
+    "filters": {"rank_filter": {"()": RankFilter}},
+    "handlers": {
+        "file": {
+            "class": "logging.FileHandler",
+            "filename": "default.log",
+            "mode": "a",  # append or overwrite
+            "level": "DEBUG",
+            "formatter": "monai_default",
+            "filters": ["rank_filter"],
+        },
+        "console": {
+            "class": "logging.StreamHandler",
+            "level": "INFO",
+            "formatter": "monai_default",
+            "filters": ["rank_filter"],
+        },
+    },
+}
+
+
+def parsing_bundle_config(config_file, logging_file=None, meta_file=None):
+    if config_file is not None:
+        _config_files = ensure_tuple(config_file)
+        config_root_path = Path(_config_files[0]).parent
+        for _config_file in _config_files:
+            _config_file = Path(_config_file)
+            if _config_file.parent != config_root_path:
+                logger.warning(
+                    f"Not all config files are in '{config_root_path}'. If logging_file and meta_file are"
+                    f"not specified, '{config_root_path}' will be used as the default config root directory."
+                )
+            if not _config_file.is_file():
+                raise FileNotFoundError(f"Cannot find the config file: {_config_file}.")
+    else:
+        config_root_path = Path("configs")
+
+    logging_file = str(config_root_path / "logging.conf") if logging_file is None else logging_file
+    if os.path.exists(logging_file):
+        fileConfig(logging_file, disable_existing_loggers=False)
+
+    parser = ConfigParser()
+    parser.read_config(config_file)
+    meta_file = str(config_root_path / "metadata.json") if meta_file is None else meta_file
+    if isinstance(meta_file, str) and not os.path.exists(meta_file):
+        logger.error(
+            f"Cannot find the metadata config file: {meta_file}. "
+            "Please see: https://docs.monai.io/en/stable/mb_specification.html"
+        )
+    else:
+        parser.read_meta(f=meta_file)
+
+    return parser
diff --git a/vista2d/scripts/workflow.py b/vista2d/scripts/workflow.py
new file mode 100644
index 0000000..26fa402
--- /dev/null
+++ b/vista2d/scripts/workflow.py
@@ -0,0 +1,1214 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import csv
+import gc
+import logging
+import os
+import shutil
+import sys
+import time
+from collections import OrderedDict
+from datetime import datetime
+
+import monai.transforms as mt
+import numpy as np
+import torch
+import torch.distributed as dist
+import yaml
+from monai.apps import get_logger
+from monai.auto3dseg.utils import datafold_read
+from monai.bundle import BundleWorkflow, ConfigParser
+from monai.config import print_config
+from monai.data import DataLoader, Dataset, decollate_batch
+from monai.metrics import CumulativeAverage
+from monai.utils import (
+    BundleProperty,
+    ImageMetaKey,
+    convert_to_dst_type,
+    ensure_tuple,
+    look_up_option,
+    optional_import,
+    set_determinism,
+)
+from torch.cuda.amp import GradScaler, autocast
+from torch.utils.data import WeightedRandomSampler
+from torch.utils.data.distributed import DistributedSampler
+from torch.utils.tensorboard import SummaryWriter
+
+mlflow, mlflow_is_imported = optional_import("mlflow")
+
+
+if __package__ in (None, ""):
+    from cell_distributed_weighted_sampler import DistributedWeightedSampler
+    from components import LabelsToFlows, LoadTiffd, LogitsToLabels
+    from utils import LOGGING_CONFIG, parsing_bundle_config  # type: ignore
+else:
+    from .cell_distributed_weighted_sampler import DistributedWeightedSampler
+    from .components import LabelsToFlows, LoadTiffd, LogitsToLabels
+    from .utils import LOGGING_CONFIG, parsing_bundle_config
+
+
+logger = get_logger("VistaCell")
+
+
+class VistaCell(BundleWorkflow):
+    """
+    Primary vista model training workflow that extends
+    monai.bundle.BundleWorkflow for cell segmentation.
+    """
+
+    def __init__(
+        self,
+        config_file=None,
+        meta_file=None,
+        logging_file=None,
+        workflow_type="train",
+        **override,
+    ):
+        """
+        config_file can be one or a list of config files.
+        the rest key-values in the `override` are to override config content.
+        """
+
+        parser = parsing_bundle_config(config_file, logging_file=logging_file, meta_file=meta_file)
+        parser.update(pairs=override)
+
+        mode = parser.get("mode", None)
+        if mode is not None:  # if user specified a `mode` it'll override the workflow_type arg
+            workflow_type = mode
+        else:
+            mode = workflow_type  # if user didn't specify mode, the workflow_type will be used
+        super().__init__(workflow_type=workflow_type)
+        self._props = {}
+        self._set_props = {}
+        self.parser = parser
+
+        self.rank = int(os.getenv("LOCAL_RANK", "0"))
+        self.global_rank = int(os.getenv("RANK", "0"))
+        self.is_distributed = dist.is_available() and dist.is_initialized()
+
+        # check if torchrun or bcprun started it
+        if dist.is_torchelastic_launched() or (
+            os.getenv("NGC_ARRAY_SIZE") is not None and int(os.getenv("NGC_ARRAY_SIZE")) > 1
+        ):
+            if dist.is_available():
+                dist.init_process_group(backend="nccl", init_method="env://")
+
+            self.is_distributed = dist.is_available() and dist.is_initialized()
+
+            torch.cuda.set_device(self.config("device"))
+            dist.barrier()
+
+        else:
+            self.is_distributed = False
+
+        if self.global_rank == 0 and self.config("ckpt_path") and not os.path.exists(self.config("ckpt_path")):
+            os.makedirs(self.config("ckpt_path"), exist_ok=True)
+
+        if self.rank == 0:
+            # make sure the log file exists, as a workaround for mult-gpu logging race condition
+            _log_file = self.config("log_output_file", "vista_cell.log")
+            if _log_file is not None:
+                open(_log_file, "a").close()
+
+            print_config()
+
+        if self.is_distributed:
+            dist.barrier()
+
+        seed = self.config("seed", None)
+        if seed is not None:
+            set_determinism(seed)
+            logger.info(f"set determinism seed: {self.config('seed', None)}")
+        elif torch.cuda.is_available():
+            torch.backends.cudnn.benchmark = True
+            logger.info("No seed provided, using cudnn.benchmark for performance.")
+
+        if os.path.exists(self.config("ckpt_path")):
+            self.parser.export_config_file(
+                self.parser.config,
+                os.path.join(self.config("ckpt_path"), "working.yaml"),
+                fmt="yaml",
+                default_flow_style=None,
+            )
+
+        self.add_property("network", required=True)
+        self.add_property("train_loader", required=True)
+        self.add_property("val_dataset", required=False)
+        self.add_property("val_loader", required=False)
+        self.add_property("val_preprocessing", required=False)
+        self.add_property("train_sampler", required=True)
+        self.add_property("val_sampler", required=True)
+        self.add_property("mode", required=False)
+        # set evaluator as required when mode is infer or eval
+        # will change after we enhance the bundle properties
+        self.evaluator = None
+
+    def _set_property(self, name, property, value):
+        # stores user-reset initialized objects that should not be re-initialized.
+        self._set_props[name] = value
+
+    def _get_property(self, name, property):
+        """
+        The customized bundle workflow must implement required properties in:
+        https://github.com/Project-MONAI/MONAI/blob/dev/monai/bundle/properties.py.
+        """
+        if name in self._set_props:
+            self._props[name] = self._set_props[name]
+            return self._props[name]
+        if name in self._props:
+            return self._props[name]
+        try:
+            value = getattr(self, f"get_{name}")()
+        except AttributeError:
+            if property[BundleProperty.REQUIRED]:
+                raise ValueError(
+                    f"Property '{name}' is required by the bundle format,"
+                    f"but the method 'get_{name}' is not implemented."
+                )
+            raise AttributeError
+        self._props[name] = value
+        return value
+
+    def config(self, name, default="null", **kwargs):
+        """read the parsed content (evaluate the expression) from the config file."""
+        if default != "null":
+            return self.parser.get_parsed_content(name, default=default, **kwargs)
+        return self.parser.get_parsed_content(name, **kwargs)
+
+    def initialize(self):
+        _log_file = self.config("log_output_file", "vista_cell.log")
+        if _log_file is None:
+            LOGGING_CONFIG["loggers"]["VistaCell"]["handlers"].remove("file")
+            LOGGING_CONFIG["handlers"].pop("file", None)
+        else:
+            LOGGING_CONFIG["handlers"]["file"]["filename"] = _log_file
+        logging.config.dictConfig(LOGGING_CONFIG)
+
+    def get_mode(self):
+        mode_str = self.config("mode", self.workflow_type)
+        return look_up_option(mode_str, ("train", "training", "infer", "inference", "eval", "evaluation"))
+
+    def run(self):
+        if str(self.mode).startswith("train"):
+            return self.train()
+        if str(self.mode).startswith("infer"):
+            return self.infer()
+        return self.validate()
+
+    def finalize(self):
+        if self.is_distributed:
+            dist.destroy_process_group()
+        set_determinism(None)
+
+    def get_network_def(self):
+        return self.config("network_def")
+
+    def get_network(self):
+        pretrained_ckpt_name = self.config("pretrained_ckpt_name", None)
+        pretrained_ckpt_path = self.config("pretrained_ckpt_path", None)
+        if pretrained_ckpt_name is not None and pretrained_ckpt_path is None:
+            # if relative name specified, append to default ckpt_path dir
+            pretrained_ckpt_path = os.path.join(self.config("ckpt_path"), pretrained_ckpt_name)
+
+        if pretrained_ckpt_path is not None and not os.path.exists(pretrained_ckpt_path):
+            logger.info(f"Pretrained checkpoint {pretrained_ckpt_path} not found.")
+            raise ValueError(f"Pretrained checkpoint {pretrained_ckpt_path} not found.")
+
+        if pretrained_ckpt_path is not None and os.path.exists(pretrained_ckpt_path):
+            # not loading sam weights, if we're using our own checkpoint
+            if "checkpoint" in self.parser.config["network_def"]:
+                self.parser.config["network_def"]["checkpoint"] = None
+            model = self.config("network")
+            self.checkpoint_load(ckpt=pretrained_ckpt_path, model=model)
+        else:
+            model = self.config("network")
+
+        if self.config("channels_last", False):
+            model = model.to(memory_format=torch.channels_last)
+
+        if self.is_distributed:
+            model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
+
+        if self.config("compile", False):
+            model = torch.compile(model)
+
+        if self.is_distributed:
+            model = torch.nn.parallel.DistributedDataParallel(
+                module=model,
+                device_ids=[self.rank],
+                output_device=self.rank,
+                find_unused_parameters=self.config("find_unused_parameters", False),
+            )
+
+        pytorch_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+        logger.info(f"total parameters count {pytorch_params} distributed {self.is_distributed}")
+        return model
+
+    def get_train_dataset_data(self):
+        train_files, valid_files = [], []
+        dataset_data = self.config("train#dataset#data")
+        val_key = None
+        if isinstance(dataset_data, dict):
+            val_key = dataset_data.get("key", None)
+        data_list_files = dataset_data["data_list_files"]
+
+        if isinstance(data_list_files, str):
+            data_list_files = ConfigParser.load_config_file(
+                data_list_files
+            )  # if it's a path to a separate file with a list of datasets
+        else:
+            data_list_files = ensure_tuple(data_list_files)
+
+        if self.global_rank == 0:
+            print("Using data_list_files ", data_list_files)
+
+        for idx, d in enumerate(data_list_files):
+            logger.info(f"adding datalist ({idx}): {d['datalist']}")
+            t, v = datafold_read(datalist=d["datalist"], basedir=d["basedir"], fold=self.config("fold"))
+
+            if val_key is not None:
+                v, _ = datafold_read(datalist=d["datalist"], basedir=d["basedir"], fold=-1, key=val_key)  # e.g. testing
+
+            for item in t:
+                item["datalist_id"] = idx
+                item["datalist_count"] = len(t)
+            for item in v:
+                item["datalist_id"] = idx
+                item["datalist_count"] = len(v)
+            train_files.extend(t)
+            valid_files.extend(v)
+
+        if self.config("quick", False):
+            logger.info("quick_data")
+            train_files = train_files[:8]
+            valid_files = valid_files[:7]
+        if not valid_files:
+            logger.warning("No validation data found.")
+        return train_files, valid_files
+
+    def read_val_datalists(self, section="validate", data_list_files=None, val_key=None, merge=True):
+        """read the corresponding folds of the datalist for validation or inference"""
+        dataset_data = self.config(f"{section}#dataset#data")
+
+        if isinstance(dataset_data, list):
+            return dataset_data
+
+        if data_list_files is None:
+            data_list_files = dataset_data["data_list_files"]
+
+        if isinstance(data_list_files, str):
+            data_list_files = ConfigParser.load_config_file(
+                data_list_files
+            )  # if it's a path to a separate file with a list of datasets
+        else:
+            data_list_files = ensure_tuple(data_list_files)
+
+        if val_key is None:
+            val_key = dataset_data.get("key", None)
+
+        val_files, idx = [], 0
+        for d in data_list_files:
+            if val_key is not None:
+                v_files, _ = datafold_read(datalist=d["datalist"], basedir=d["basedir"], fold=-1, key=val_key)
+            else:
+                _, v_files = datafold_read(datalist=d["datalist"], basedir=d["basedir"], fold=self.config("fold"))
+            logger.info(f"adding datalist ({idx} -- {val_key}): {d['datalist']} {len(v_files)}")
+            if merge:
+                val_files.extend(v_files)
+            else:
+                val_files.append(v_files)
+            idx += 1
+
+        if self.config("quick", False):
+            logger.info("quick_data")
+            val_files = val_files[:8] if merge else [val_files[0][:8]]
+        return val_files
+
+    def get_train_preprocessing(self):
+        roi_size = self.config("train#dataset#preprocessing#roi_size")
+
+        train_xforms = []
+        train_xforms.append(LoadTiffd(keys=["image", "label"]))
+        train_xforms.append(mt.EnsureTyped(keys=["image", "label"], data_type="tensor", dtype=torch.float))
+        if self.config("prescale", True):
+            print("Prescaling images to 0..1")
+            train_xforms.append(mt.ScaleIntensityd(keys="image", minv=0, maxv=1, channel_wise=True))
+        train_xforms.append(mt.ScaleIntensityd(keys="image", minv=0, maxv=1, channel_wise=True))
+        train_xforms.append(
+            mt.ScaleIntensityRangePercentilesd(
+                keys="image", lower=1, upper=99, b_min=0.0, b_max=1.0, channel_wise=True, clip=True
+            )
+        )
+        train_xforms.append(mt.SpatialPadd(keys=["image", "label"], spatial_size=roi_size))
+        train_xforms.append(
+            mt.RandSpatialCropd(keys=["image", "label"], roi_size=roi_size)
+        )  # crop roi_size (if image is large)
+
+        # # add augmentations
+        train_xforms.extend(
+            [
+                mt.RandAffined(
+                    keys=["image", "label"],
+                    prob=0.5,
+                    rotate_range=np.pi,  # from -pi to pi
+                    scale_range=[-0.5, 0.5],  # from 0.5 to 1.5
+                    mode=["bilinear", "nearest"],
+                    spatial_size=roi_size,
+                    cache_grid=True,
+                    padding_mode="border",
+                ),
+                mt.RandAxisFlipd(keys=["image", "label"], prob=0.5),
+                mt.RandGaussianNoised(keys=["image"], prob=0.25, mean=0, std=0.1),
+                mt.RandAdjustContrastd(keys=["image"], prob=0.25, gamma=(1, 2)),
+                mt.RandGaussianSmoothd(keys=["image"], prob=0.25, sigma_x=(1, 2)),
+                mt.RandHistogramShiftd(keys=["image"], prob=0.25, num_control_points=3),
+                mt.RandGaussianSharpend(keys=["image"], prob=0.25),
+            ]
+        )
+
+        train_xforms.append(
+            LabelsToFlows(keys="label", flow_key="flow")
+        )  # finally create new key "flows" with 3 channels 1) foreground 2) dx flow 3) dy flow
+
+        return train_xforms
+
+    def get_val_preprocessing(self):
+        val_xforms = []
+        val_xforms.append(LoadTiffd(keys=["image", "label"], allow_missing_keys=True))
+        val_xforms.append(
+            mt.EnsureTyped(keys=["image", "label"], data_type="tensor", dtype=torch.float, allow_missing_keys=True)
+        )
+
+        if self.config("prescale", True):
+            print("Prescaling val images to 0..1")
+            val_xforms.append(mt.ScaleIntensityd(keys="image", minv=0, maxv=1, channel_wise=True))
+
+        val_xforms.append(
+            mt.ScaleIntensityRangePercentilesd(
+                keys="image", lower=1, upper=99, b_min=0.0, b_max=1.0, channel_wise=True, clip=True
+            )
+        )
+        val_xforms.append(LabelsToFlows(keys="label", flow_key="flow", allow_missing_keys=True))
+
+        return val_xforms
+
+    def get_train_dataset(self):
+        train_dataset_data = self.config("train#dataset#data")
+        if isinstance(train_dataset_data, list):  # FIXME, why check
+            train_files = train_dataset_data
+        else:
+            train_files, _ = self.train_dataset_data
+        logger.info(f"train files {len(train_files)}")
+        return Dataset(data=train_files, transform=mt.Compose(self.train_preprocessing))
+
+    def get_val_dataset(self):
+        """this is to be used for validation during training"""
+        val_dataset_data = self.config("validate#dataset#data")
+        if isinstance(val_dataset_data, list):  # FIXME, why check
+            valid_files = val_dataset_data
+        else:
+            _, valid_files = self.train_dataset_data
+        return Dataset(data=valid_files, transform=mt.Compose(self.val_preprocessing))
+
+    def set_val_datalist(self, datalist_py):
+        self.parser["validate#dataset#data"] = datalist_py
+        self._props.pop("val_loader", None)
+        self._props.pop("val_dataset", None)
+        self._props.pop("val_sampler", None)
+
+    def get_train_sampler(self):
+        if self.config("use_weighted_sampler", False):
+            data = self.train_dataset.data
+            logger.info(f"Using weighted sampler, first item {data[0]}")
+            sample_weights = 1.0 / torch.as_tensor(
+                [item.get("datalist_count", 1.0) for item in data], dtype=torch.float
+            )  # inverse proportional to sub-datalist count
+            # if we are using weighed sampling, the number of iterations epoch must be provided
+            # (cant use a dataset length anymore)
+            num_samples_per_epoch = self.config("num_samples_per_epoch", None)
+            if num_samples_per_epoch is None:
+                num_samples_per_epoch = len(data)  # a workaround if not provided
+                logger.warning(
+                    "We are using weighted random sampler, but num_samples_per_epoch is not provided, "
+                    f"so using {num_samples_per_epoch} full data length as a workaround!"
+                )
+
+            if self.is_distributed:
+                return DistributedWeightedSampler(
+                    self.train_dataset, shuffle=True, weights=sample_weights, num_samples=num_samples_per_epoch
+                )  # custom implementation, as Pytorch does not have one
+            return WeightedRandomSampler(weights=sample_weights, num_samples=num_samples_per_epoch)
+
+        if self.is_distributed:
+            return DistributedSampler(self.train_dataset, shuffle=True)
+        return None
+
+    def get_val_sampler(self):
+        if self.is_distributed:
+            return DistributedSampler(self.val_dataset, shuffle=False)
+        return None
+
+    def get_train_loader(self):
+        sampler = self.train_sampler
+        return DataLoader(
+            self.train_dataset,
+            batch_size=self.config("train#batch_size"),
+            shuffle=(sampler is None),
+            sampler=sampler,
+            pin_memory=True,
+            num_workers=self.config("train#num_workers"),
+        )
+
+    def get_val_loader(self):
+        sampler = self.val_sampler
+        return DataLoader(
+            self.val_dataset,
+            batch_size=self.config("validate#batch_size"),
+            shuffle=False,
+            sampler=sampler,
+            pin_memory=True,
+            num_workers=self.config("validate#num_workers"),
+        )
+
+    def train(self):
+        config = self.config
+        distributed = self.is_distributed
+        sliding_inferrer = config("inferer#sliding_inferer")
+        use_amp = config("amp")
+
+        amp_dtype = {"float32": torch.float32, "bfloat16": torch.bfloat16, "float16": torch.float16}[
+            config("amp_dtype")
+        ]
+        if amp_dtype == torch.bfloat16 and not torch.cuda.is_bf16_supported():
+            amp_dtype = torch.float16
+            logger.warning(
+                "bfloat16 dtype is not support on your device, changing to float16, use  --amp_dtype=float16 to set manually"
+            )
+
+        use_gradscaler = use_amp and amp_dtype == torch.float16
+        logger.info(f"Using grad scaler {use_gradscaler} amp_dtype {amp_dtype} use_amp {use_amp}")
+        grad_scaler = GradScaler(enabled=use_gradscaler)  # using GradScaler only for AMP float16 (not bfloat16)
+
+        loss_function = config("loss_function")
+        acc_function = config("key_metric")
+
+        ckpt_path = config("ckpt_path")
+        channels_last = config("channels_last")
+
+        num_epochs_per_saving = config("train#trainer#num_epochs_per_saving")
+        num_epochs_per_validation = config("train#trainer#num_epochs_per_validation")
+        num_epochs = config("train#trainer#max_epochs")
+        val_schedule_list = self.schedule_validation_epochs(
+            num_epochs=num_epochs, num_epochs_per_validation=num_epochs_per_validation
+        )
+        logger.info(f"Scheduling validation loops at epochs: {val_schedule_list}")
+
+        train_loader = self.train_loader
+        val_loader = self.val_loader
+        optimizer = config("optimizer")
+        model = self.network
+
+        tb_writer = None
+        csv_path = progress_path = None
+
+        if self.global_rank == 0 and ckpt_path is not None:
+            # rank 0 is responsible for heavy lifting of logging/saving
+            progress_path = os.path.join(ckpt_path, "progress.yaml")
+
+            tb_writer = SummaryWriter(log_dir=ckpt_path)
+            logger.info(f"Writing Tensorboard logs to {tb_writer.log_dir}")
+
+            if mlflow_is_imported:
+                if config("mlflow_tracking_uri", None) is not None:
+                    mlflow.set_tracking_uri(config("mlflow_tracking_uri"))
+                    mlflow.set_experiment("vista2d")
+
+                    mlflow_run_name = config("mlflow_run_name", f'vista2d train fold{config("fold")}')
+                    mlflow.start_run(
+                        run_name=mlflow_run_name, log_system_metrics=config("mlflow_log_system_metrics", False)
+                    )
+                    mlflow.log_params(self.parser.config)
+                    mlflow.log_dict(self.parser.config, "hyper_parameters.yaml")  # experimental
+
+            csv_path = os.path.join(ckpt_path, "accuracy_history.csv")
+            self.save_history_csv(
+                csv_path=csv_path,
+                header=["epoch", "metric", "loss", "iter", "time", "train_time", "validation_time", "epoch_time"],
+            )
+
+        do_torch_save = (
+            (self.global_rank == 0) and ckpt_path and config("ckpt_save") and not config("train#skip", False)
+        )
+        best_ckpt_path = os.path.join(ckpt_path, "model.pt")
+        intermediate_ckpt_path = os.path.join(ckpt_path, "model_final.pt")
+
+        best_metric = float(config("best_metric", -1))
+        start_epoch = config("start_epoch", 0)
+        best_metric_epoch = -1
+        pre_loop_time = time.time()
+        report_num_epochs = num_epochs
+        train_time = validation_time = 0
+        val_acc_history = []
+
+        if start_epoch > 0:
+            val_schedule_list = [v for v in val_schedule_list if v >= start_epoch]
+            if len(val_schedule_list) == 0:
+                val_schedule_list = [start_epoch]
+            print(f"adjusted schedule_list {val_schedule_list}")
+
+        logger.info(
+            f"Using num_epochs => {num_epochs}\n "
+            f"Using start_epoch => {start_epoch}\n "
+            f"batch_size => {config('train#batch_size')} \n "
+            f"num_warmup_epochs => {config('train#trainer#num_warmup_epochs')} \n "
+        )
+
+        lr_scheduler = config("lr_scheduler")
+        if lr_scheduler is not None and start_epoch > 0:
+            lr_scheduler.last_epoch = start_epoch
+
+        range_num_epochs = range(start_epoch, num_epochs)
+
+        if distributed:
+            dist.barrier()
+
+        if self.global_rank == 0 and tb_writer is not None and mlflow_is_imported and mlflow.is_tracking_uri_set():
+            mlflow.log_param("len_train_set", len(train_loader.dataset))
+            mlflow.log_param("len_val_set", len(val_loader.dataset))
+
+        for epoch in range_num_epochs:
+            report_epoch = epoch
+
+            if distributed:
+                if isinstance(train_loader.sampler, DistributedSampler):
+                    train_loader.sampler.set_epoch(epoch)
+                dist.barrier()
+
+            epoch_time = start_time = time.time()
+
+            train_loss, train_acc = 0, 0
+
+            if not config("train#skip", False):
+                train_loss, train_acc = self.train_epoch(
+                    model=model,
+                    train_loader=train_loader,
+                    optimizer=optimizer,
+                    loss_function=loss_function,
+                    acc_function=acc_function,
+                    grad_scaler=grad_scaler,
+                    epoch=report_epoch,
+                    rank=self.rank,
+                    global_rank=self.global_rank,
+                    num_epochs=report_num_epochs,
+                    use_amp=use_amp,
+                    amp_dtype=amp_dtype,
+                    channels_last=channels_last,
+                    device=config("device"),
+                )
+
+            train_time = time.time() - start_time
+            logger.info(
+                f"Latest training  {report_epoch}/{report_num_epochs - 1} "
+                f"loss: {train_loss:.4f} time {train_time:.2f}s  "
+                f"lr: {optimizer.param_groups[0]['lr']:.4e}"
+            )
+
+            if self.global_rank == 0 and tb_writer is not None:
+                tb_writer.add_scalar("train/loss", train_loss, report_epoch)
+
+                if mlflow_is_imported and mlflow.is_tracking_uri_set():
+                    mlflow.log_metric("train/loss", train_loss, step=report_epoch)
+                    mlflow.log_metric("train/epoch_time", train_time, step=report_epoch)
+
+            # validate every num_epochs_per_validation epochs (defaults to 1, every epoch)
+            val_acc_mean = -1
+            if (
+                len(val_schedule_list) > 0
+                and epoch + 1 >= val_schedule_list[0]
+                and val_loader is not None
+                and len(val_loader) > 0
+            ):
+                val_schedule_list.pop(0)
+
+                start_time = time.time()
+                torch.cuda.empty_cache()
+
+                val_loss, val_acc = self.val_epoch(
+                    model=model,
+                    val_loader=val_loader,
+                    sliding_inferrer=sliding_inferrer,
+                    loss_function=loss_function,
+                    acc_function=acc_function,
+                    epoch=report_epoch,
+                    rank=self.rank,
+                    global_rank=self.global_rank,
+                    num_epochs=report_num_epochs,
+                    use_amp=use_amp,
+                    amp_dtype=amp_dtype,
+                    channels_last=channels_last,
+                    device=config("device"),
+                )
+
+                torch.cuda.empty_cache()
+                validation_time = time.time() - start_time
+
+                val_acc_mean = float(np.mean(val_acc))
+                val_acc_history.append((report_epoch, val_acc_mean))
+
+                if self.global_rank == 0:
+                    logger.info(
+                        f"Latest validation {report_epoch}/{report_num_epochs - 1} "
+                        f"loss: {val_loss:.4f} acc_avg: {val_acc_mean:.4f} acc: {val_acc} time: {validation_time:.2f}s"
+                    )
+
+                    if tb_writer is not None:
+                        tb_writer.add_scalar("val/acc", val_acc_mean, report_epoch)
+                        tb_writer.add_scalar("val/loss", val_loss, report_epoch)
+                        if mlflow_is_imported and mlflow.is_tracking_uri_set():
+                            mlflow.log_metric("val/acc", val_acc_mean, step=report_epoch)
+                            mlflow.log_metric("val/epoch_time", validation_time, step=report_epoch)
+
+                    timing_dict = {
+                        "time": f"{(time.time() - pre_loop_time) / 3600:.2f} hr",
+                        "train_time": f"{train_time:.2f}s",
+                        "validation_time": f"{validation_time:.2f}s",
+                        "epoch_time": f"{time.time() - epoch_time:.2f}s",
+                    }
+
+                    if val_acc_mean > best_metric:
+                        logger.info(f"New best metric ({best_metric:.6f} --> {val_acc_mean:.6f}). ")
+                        best_metric, best_metric_epoch = val_acc_mean, report_epoch
+                        save_time = 0
+                        if do_torch_save:
+                            save_time = self.checkpoint_save(
+                                ckpt=best_ckpt_path, model=model, epoch=best_metric_epoch, best_metric=best_metric
+                            )
+
+                        if progress_path is not None:
+                            self.save_progress_yaml(
+                                progress_path=progress_path,
+                                ckpt=best_ckpt_path if do_torch_save else None,
+                                best_avg_score_epoch=best_metric_epoch,
+                                best_avg_score=best_metric,
+                                save_time=save_time,
+                                **timing_dict,
+                            )
+                    if csv_path is not None:
+                        self.save_history_csv(
+                            csv_path=csv_path,
+                            epoch=report_epoch,
+                            metric=f"{val_acc_mean:.4f}",
+                            loss=f"{train_loss:.4f}",
+                            iter=report_epoch * len(train_loader.dataset),
+                            **timing_dict,
+                        )
+
+                # sanity check
+                if epoch > max(20, num_epochs / 4) and 0 <= val_acc_mean < 0.01 and config("stop_on_lowacc", True):
+                    logger.info(
+                        f"Accuracy seems very low at epoch {report_epoch}, acc {val_acc_mean}. "
+                        "Most likely optimization diverged, try setting a smaller learning_rate"
+                        f" than {config('learning_rate')}"
+                    )
+                    raise ValueError(
+                        f"Accuracy seems very low at epoch {report_epoch}, acc {val_acc_mean}. "
+                        "Most likely optimization diverged, try setting a smaller learning_rate"
+                        f" than {config('learning_rate')}"
+                    )
+
+            # save intermediate checkpoint every num_epochs_per_saving epochs
+            if do_torch_save and ((epoch + 1) % num_epochs_per_saving == 0 or (epoch + 1) >= num_epochs):
+                if report_epoch != best_metric_epoch:
+                    self.checkpoint_save(
+                        ckpt=intermediate_ckpt_path, model=model, epoch=report_epoch, best_metric=val_acc_mean
+                    )
+                else:
+                    try:
+                        shutil.copyfile(best_ckpt_path, intermediate_ckpt_path)  # if already saved once
+                    except Exception as err:
+                        logger.warning(f"error copying {best_ckpt_path} {intermediate_ckpt_path} {err}")
+                        pass
+
+            if lr_scheduler is not None:
+                lr_scheduler.step()
+
+            if self.global_rank == 0:
+                # report time estimate
+                time_remaining_estimate = train_time * (num_epochs - epoch)
+                if val_loader is not None and len(val_loader) > 0:
+                    if validation_time == 0:
+                        validation_time = train_time
+                    time_remaining_estimate += validation_time * len(val_schedule_list)
+
+                logger.info(
+                    f"Estimated remaining training time for the current model fold {config('fold')} is "
+                    f"{time_remaining_estimate/3600:.2f} hr, "
+                    f"running time {(time.time() - pre_loop_time)/3600:.2f} hr, "
+                    f"est total time {(time.time() - pre_loop_time + time_remaining_estimate)/3600:.2f} hr \n"
+                )
+
+        # end of main epoch loop
+        train_loader = val_loader = optimizer = None
+
+        # optionally validate best checkpoint
+        logger.info(f"Checking to run final testing {config('run_final_testing')}")
+        if config("run_final_testing"):
+            if distributed:
+                dist.barrier()
+            _ckpt_name = best_ckpt_path if os.path.exists(best_ckpt_path) else intermediate_ckpt_path
+            if not os.path.exists(_ckpt_name):
+                logger.info(f"Unable to validate final no checkpoints found {best_ckpt_path}, {intermediate_ckpt_path}")
+            else:
+                # self._props.pop("network", None)
+                # self._set_props.pop("network", None)
+                gc.collect()
+                torch.cuda.empty_cache()
+                best_metric = self.run_final_testing(
+                    pretrained_ckpt_path=_ckpt_name,
+                    progress_path=progress_path,
+                    best_metric_epoch=best_metric_epoch,
+                    pre_loop_time=pre_loop_time,
+                )
+
+                if (
+                    self.global_rank == 0
+                    and tb_writer is not None
+                    and mlflow_is_imported
+                    and mlflow.is_tracking_uri_set()
+                ):
+                    mlflow.log_param("acc_testing", val_acc_mean)
+                    mlflow.log_metric("acc_testing", val_acc_mean)
+
+        if tb_writer is not None:
+            tb_writer.flush()
+            tb_writer.close()
+
+            if mlflow_is_imported and mlflow.is_tracking_uri_set():
+                mlflow.end_run()
+
+        logger.info(
+            f"=== DONE: best_metric: {best_metric:.4f} at epoch: {best_metric_epoch} of {report_num_epochs}."
+            f"Training time {(time.time() - pre_loop_time)/3600:.2f} hr."
+        )
+        return best_metric
+
+    def run_final_testing(self, pretrained_ckpt_path, progress_path, best_metric_epoch, pre_loop_time):
+        logger.info("Running final best model testing set!")
+
+        # validate
+        start_time = time.time()
+
+        self._props.pop("network", None)
+        self.parser["pretrained_ckpt_path"] = pretrained_ckpt_path
+        self.parser["validate#evaluator#postprocessing"] = None  # not saving images
+
+        val_acc_mean, val_loss, val_acc = self.validate(val_key="testing")
+        validation_time = f"{time.time() - start_time:.2f}s"
+        val_acc_mean = float(np.mean(val_acc))
+        logger.info(f"Testing: loss: {val_loss:.4f} acc_avg: {val_acc_mean:.4f} acc {val_acc} time {validation_time}")
+
+        if self.global_rank == 0 and progress_path is not None:
+            self.save_progress_yaml(
+                progress_path=progress_path,
+                ckpt=pretrained_ckpt_path,
+                best_avg_score_epoch=best_metric_epoch,
+                best_avg_score=val_acc_mean,
+                validation_time=validation_time,
+                run_final_testing=True,
+                time=f"{(time.time() - pre_loop_time) / 3600:.2f} hr",
+            )
+        return val_acc_mean
+
+    def validate(self, validation_files=None, val_key=None, datalist=None):
+        if self.config("pretrained_ckpt_name", None) is None and self.config("pretrained_ckpt_path", None) is None:
+            self.parser["pretrained_ckpt_name"] = "model.pt"
+            logger.info("Using default model.pt checkpoint for validation.")
+
+        grouping = self.config("validate#grouping", False)  # whether to computer average per datalist
+        if validation_files is None:
+            validation_files = self.read_val_datalists("validate", datalist, val_key=val_key, merge=not grouping)
+        if len(validation_files) == 0:
+            logger.warning(f"No validation files found {datalist} {val_key}!")
+            return 0, 0, 0
+        if not grouping or not isinstance(validation_files[0], (list, tuple)):
+            validation_files = [validation_files]
+        logger.info(f"validation file groups {len(validation_files)} grouping {grouping}")
+        val_acc_dict = {}
+
+        amp_dtype = {"float32": torch.float32, "bfloat16": torch.bfloat16, "float16": torch.float16}[
+            self.config("amp_dtype")
+        ]
+        if amp_dtype == torch.bfloat16 and not torch.cuda.is_bf16_supported():
+            amp_dtype = torch.float16
+            logger.warning(
+                "bfloat16 dtype is not support on your device, changing to float16, use  --amp_dtype=float16 to set manually"
+            )
+
+        for datalist_id, group_files in enumerate(validation_files):
+            self.set_val_datalist(group_files)
+            val_loader = self.val_loader
+
+            start_time = time.time()
+            val_loss, val_acc = self.val_epoch(
+                model=self.network,
+                val_loader=val_loader,
+                sliding_inferrer=self.config("inferer#sliding_inferer"),
+                loss_function=self.config("loss_function"),
+                acc_function=self.config("key_metric"),
+                rank=self.rank,
+                global_rank=self.global_rank,
+                use_amp=self.config("amp"),
+                amp_dtype=amp_dtype,
+                post_transforms=self.config("validate#evaluator#postprocessing"),
+                channels_last=self.config("channels_last"),
+                device=self.config("device"),
+            )
+            val_acc_mean = float(np.mean(val_acc))
+            logger.info(
+                f"Validation {datalist_id} complete, loss_avg: {val_loss:.4f} "
+                f"acc_avg: {val_acc_mean:.4f} acc {val_acc} time {time.time() - start_time:.2f}s"
+            )
+            val_acc_dict[datalist_id] = val_acc_mean
+        for k, v in val_acc_dict.items():
+            logger.info(f"group: {k} => {v:.4f}")
+        val_acc_mean = sum(val_acc_dict.values()) / len(val_acc_dict.values())
+        logger.info(f"Testing group score average: {val_acc_mean:.4f}")
+        return val_acc_mean, val_loss, val_acc
+
+    def infer(self, infer_files=None, infer_key=None, datalist=None):
+        if self.config("pretrained_ckpt_name", None) is None and self.config("pretrained_ckpt_path", None) is None:
+            self.parser["pretrained_ckpt_name"] = "model.pt"
+            logger.info("Using default model.pt checkpoint for inference.")
+
+        if infer_files is None:
+            infer_files = self.read_val_datalists("infer", datalist, val_key=infer_key, merge=True)
+        if len(infer_files) == 0:
+            logger.warning(f"no file to infer {datalist} {infer_key}.")
+            return
+        logger.info(f"inference files {len(infer_files)}")
+        self.set_val_datalist(infer_files)
+        val_loader = self.val_loader
+
+        amp_dtype = {"float32": torch.float32, "bfloat16": torch.bfloat16, "float16": torch.float16}[
+            self.config("amp_dtype")
+        ]
+        if amp_dtype == torch.bfloat16 and not torch.cuda.is_bf16_supported():
+            amp_dtype = torch.bfloat16
+            logger.warning(
+                "bfloat16 dtype is not support on your device, changing to float16, use  --amp_dtype=float16 to set manually"
+            )
+
+        start_time = time.time()
+        self.val_epoch(
+            model=self.network,
+            val_loader=val_loader,
+            sliding_inferrer=self.config("inferer#sliding_inferer"),
+            loss_function=None,
+            acc_function=None,
+            rank=self.rank,
+            global_rank=self.global_rank,
+            use_amp=self.config("amp"),
+            amp_dtype=amp_dtype,
+            post_transforms=self.config("infer#evaluator#postprocessing"),
+            channels_last=self.config("channels_last"),
+            device=self.config("device"),
+        )
+        logger.info(f"Inference complete time {time.time() - start_time:.2f}s")
+        return
+
+    @torch.no_grad()
+    def val_epoch(
+        self,
+        model,
+        val_loader,
+        sliding_inferrer,
+        loss_function=None,
+        acc_function=None,
+        epoch=0,
+        rank=0,
+        global_rank=0,
+        num_epochs=0,
+        use_amp=True,
+        amp_dtype=torch.float16,
+        post_transforms=None,
+        channels_last=False,
+        device=None,
+    ):
+        model.eval()
+        distributed = dist.is_available() and dist.is_initialized()
+        memory_format = torch.channels_last if channels_last else torch.preserve_format
+
+        run_loss = CumulativeAverage()
+        run_acc = CumulativeAverage()
+        run_loss.append(torch.tensor(0, device=device), count=0)
+
+        avg_loss = avg_acc = 0
+        start_time = time.time()
+
+        # In DDP, each replica has a subset of data, but if total data length is not evenly divisible by num_replicas,
+        # then some replicas has 1 extra repeated item.
+        # For proper validation with batch of 1, we only want to collect metrics for non-repeated items,
+        # hence let's compute a proper subset length
+        nonrepeated_data_length = len(val_loader.dataset)
+        sampler = val_loader.sampler
+        if distributed and isinstance(sampler, DistributedSampler) and not sampler.drop_last:
+            nonrepeated_data_length = len(range(sampler.rank, len(sampler.dataset), sampler.num_replicas))
+
+        for idx, batch_data in enumerate(val_loader):
+            data = batch_data["image"].as_subclass(torch.Tensor).to(memory_format=memory_format, device=device)
+            filename = batch_data["image"].meta[ImageMetaKey.FILENAME_OR_OBJ]
+            batch_size = data.shape[0]
+            loss = acc = None
+
+            with autocast(enabled=use_amp, dtype=amp_dtype):
+                logits = sliding_inferrer(inputs=data, network=model)
+            data = None
+
+            # calc loss
+            if loss_function is not None:
+                target = batch_data["flow"].as_subclass(torch.Tensor).to(device=logits.device)
+                loss = loss_function(logits, target)
+                run_loss.append(loss.to(device=device), count=batch_size)
+                target = None
+
+            pred_mask_all = []
+
+            for b_ind in range(logits.shape[0]):  # go over batch dim
+                pred_mask, p = LogitsToLabels()(logits=logits[b_ind], filename=filename)
+                pred_mask_all.append(pred_mask)
+
+            if acc_function is not None:
+                label = batch_data["label"].as_subclass(torch.Tensor)
+
+                for b_ind in range(label.shape[0]):
+                    acc = acc_function(pred_mask_all[b_ind], label[b_ind, 0].long())
+                    acc = acc.detach().clone() if isinstance(acc, torch.Tensor) else torch.tensor(acc)
+
+                    if idx < nonrepeated_data_length:
+                        run_acc.append(acc.to(device=device), count=1)
+                    else:
+                        run_acc.append(torch.zeros_like(acc, device=device), count=0)
+                label = None
+
+            avg_loss = loss.cpu() if loss is not None else 0
+            avg_acc = acc.cpu().numpy() if acc is not None else 0
+
+            logger.info(
+                f"Val {epoch}/{num_epochs} {idx}/{len(val_loader)} "
+                f"loss: {avg_loss:.4f} acc {avg_acc}  time {time.time() - start_time:.2f}s"
+            )
+
+            if post_transforms:
+                seg = torch.from_numpy(np.stack(pred_mask_all, axis=0).astype(np.int32)).unsqueeze(1)
+                batch_data["seg"] = convert_to_dst_type(
+                    seg, batch_data["image"], dtype=torch.int32, device=torch.device("cpu")
+                )[0]
+                for bd in decollate_batch(batch_data):
+                    post_transforms(bd)  # (currently only to save output mask)
+
+            start_time = time.time()
+
+        label = target = data = batch_data = None
+
+        if distributed:
+            dist.barrier()
+
+        avg_loss = run_loss.aggregate()
+        avg_acc = run_acc.aggregate()
+
+        if np.any(avg_acc < 0):
+            dist.barrier()
+            logger.warning(f"Avg accuracy is negative ({avg_acc}), something went wrong!!!!!")
+
+        return avg_loss, avg_acc
+
+    def train_epoch(
+        self,
+        model,
+        train_loader,
+        optimizer,
+        loss_function,
+        acc_function,
+        grad_scaler,
+        epoch,
+        rank,
+        global_rank=0,
+        num_epochs=0,
+        use_amp=True,
+        amp_dtype=torch.float16,
+        channels_last=False,
+        device=None,
+    ):
+        model.train()
+        memory_format = torch.channels_last if channels_last else torch.preserve_format
+
+        run_loss = CumulativeAverage()
+
+        start_time = time.time()
+        avg_loss = avg_acc = 0
+        for idx, batch_data in enumerate(train_loader):
+            data = batch_data["image"].as_subclass(torch.Tensor).to(memory_format=memory_format, device=device)
+            target = batch_data["flow"].as_subclass(torch.Tensor).to(memory_format=memory_format, device=device)
+
+            optimizer.zero_grad(set_to_none=True)
+
+            with autocast(enabled=use_amp, dtype=amp_dtype):
+                logits = model(data)
+
+            # print('logits', logits.shape, logits.dtype)
+            loss = loss_function(logits.float(), target)
+
+            grad_scaler.scale(loss).backward()
+            grad_scaler.step(optimizer)
+            grad_scaler.update()
+
+            batch_size = data.shape[0]
+
+            run_loss.append(loss, count=batch_size)
+            avg_loss = run_loss.aggregate()
+
+            logger.info(
+                f"Epoch {epoch}/{num_epochs} {idx}/{len(train_loader)} "
+                f"loss: {avg_loss:.4f} time {time.time() - start_time:.2f}s "
+            )
+            start_time = time.time()
+
+        optimizer.zero_grad(set_to_none=True)
+
+        data = None
+        target = None
+        batch_data = None
+
+        return avg_loss, avg_acc
+
+    def save_history_csv(self, csv_path=None, header=None, **kwargs):
+        if csv_path is not None:
+            if header is not None:
+                with open(csv_path, "a") as myfile:
+                    wrtr = csv.writer(myfile, delimiter="\t")
+                    wrtr.writerow(header)
+            if len(kwargs):
+                with open(csv_path, "a") as myfile:
+                    wrtr = csv.writer(myfile, delimiter="\t")
+                    wrtr.writerow(list(kwargs.values()))
+
+    def save_progress_yaml(self, progress_path=None, ckpt=None, **report):
+        if ckpt is not None:
+            report["model"] = ckpt
+
+        report["date"] = str(datetime.now())[:19]
+
+        if progress_path is not None:
+            yaml.add_representer(
+                float, lambda dumper, value: dumper.represent_scalar("tag:yaml.org,2002:float", f"{value:.4f}")
+            )
+            with open(progress_path, "a") as progress_file:
+                yaml.dump([report], stream=progress_file, allow_unicode=True, default_flow_style=None, sort_keys=False)
+
+        logger.info("Progress:" + ",".join(f" {k}: {v}" for k, v in report.items()))
+
+    def checkpoint_save(self, ckpt: str, model: torch.nn.Module, **kwargs):
+        # save checkpoint and config
+        save_time = time.time()
+        if isinstance(model, torch.nn.parallel.DistributedDataParallel):
+            state_dict = model.module.state_dict()
+        else:
+            state_dict = model.state_dict()
+
+        if self.config("compile", False):
+            # remove key prefix of compiled models
+            state_dict = OrderedDict(
+                (k[len("_orig_mod.") :] if k.startswith("_orig_mod.") else k, v) for k, v in state_dict.items()
+            )
+
+        torch.save({"state_dict": state_dict, "config": self.parser.config, **kwargs}, ckpt)
+
+        save_time = time.time() - save_time
+        logger.info(f"Saving checkpoint process: {ckpt}, {kwargs}, save_time {save_time:.2f}s")
+
+        return save_time
+
+    def checkpoint_load(self, ckpt: str, model: torch.nn.Module, **kwargs):
+        # load checkpoint
+        if not os.path.isfile(ckpt):
+            logger.warning("Invalid checkpoint file: " + str(ckpt))
+            return
+        checkpoint = torch.load(ckpt, map_location="cpu")
+
+        # if self.config("compile", False):
+        #     checkpoint["state_dict"] = OrderedDict((k[len("_orig_mod."):] if k.startswith("_orig_mod.") else k, v) for k, v in checkpoint["state_dict"].items())
+
+        model.load_state_dict(checkpoint["state_dict"], strict=True)
+        epoch = checkpoint.get("epoch", 0)
+        best_metric = checkpoint.get("best_metric", 0)
+
+        if self.config("continue", False):
+            if "epoch" in checkpoint:
+                self.parser["start_epoch"] = checkpoint["epoch"]
+            if "best_metric" in checkpoint:
+                self.parser["best_metric"] = checkpoint["best_metric"]
+
+        logger.info(
+            f"=> loaded checkpoint {ckpt} (epoch {epoch}) "
+            f"(best_metric {best_metric}) setting start_epoch {self.config('start_epoch')}"
+        )
+        self.parser["start_epoch"] = int(self.config("start_epoch")) + 1
+        return
+
+    def schedule_validation_epochs(self, num_epochs, num_epochs_per_validation=None, fraction=0.16) -> list:
+        """
+        Schedule of epochs to validate (progressively more frequently)
+            num_epochs - total number of epochs
+            num_epochs_per_validation - if provided use a linear schedule with this step
+            init_step
+        """
+
+        if num_epochs_per_validation is None:
+            x = (np.sin(np.linspace(0, np.pi / 2, max(10, int(fraction * num_epochs)))) * num_epochs).astype(int)
+            x = np.cumsum(np.sort(np.diff(np.unique(x)))[::-1])
+            x[-1] = num_epochs
+            x = x.tolist()
+        else:
+            if num_epochs_per_validation >= num_epochs:
+                x = [num_epochs_per_validation]
+            else:
+                x = list(range(num_epochs_per_validation, num_epochs, num_epochs_per_validation))
+
+        if len(x) == 0:
+            x = [0]
+
+        return x
+
+
+def main(**kwargs) -> None:
+    workflow = VistaCell(**kwargs)
+    workflow.initialize()
+    workflow.run()
+    workflow.finalize()
+
+
+if __name__ == "__main__":
+    # to be able to run directly as python scripts/workflow.py --config_file=...
+    # for debugging and development
+
+    from pathlib import Path
+
+    sys.path.append(str(Path(__file__).parent.parent))
+
+    # from scripts import *
+
+    fire, fire_is_imported = optional_import("fire")
+    if fire_is_imported:
+        fire.Fire(main)
+    else:
+        print("Missing package: fire")
diff --git a/vista2d/setup.cfg b/vista2d/setup.cfg
new file mode 100644
index 0000000..d4d6afe
--- /dev/null
+++ b/vista2d/setup.cfg
@@ -0,0 +1,17 @@
+[flake8]
+select = B,C,E,F,N,P,T4,W,B9
+max_line_length = 120
+# F403 'from module import *' used; unable to detect undefined names
+# F405 Name may be undefined, or defined from star import
+# E203 whitespace before ':' # incompatible with black style
+# E402 module level import not at top of file
+# E501 is not flexible enough, we're using B950 instead
+# E722 do not use bare 'except'
+# C408 ignored because we like the dict keyword argument syntax
+# W503 line break before binary operator
+ignore =
+    E203,E302,E303,E305,E402,E501,E721,E722,E741,F403,F405,F821,F841,F999,C408,W291,W503,W504,B008,
+    # N812 lowercase 'torch.nn.functional' imported as non lowercase 'F'
+    N812,N818
+per_file_ignores = __init__.py: F401
+exclude = *.pyi,.git,.eggs,_version.py,versioneer.py,venv,.venv,_version.py
diff --git a/vista2d/unit_tests/test_vista2d.py b/vista2d/unit_tests/test_vista2d.py
new file mode 100644
index 0000000..8d3c39b
--- /dev/null
+++ b/vista2d/unit_tests/test_vista2d.py
@@ -0,0 +1,123 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import shutil
+import sys
+import tempfile
+import unittest
+
+import matplotlib.pyplot as plt
+import numpy as np
+from monai.bundle import create_workflow
+from parameterized import parameterized
+from utils import check_workflow
+
+TEST_CASE_TRAIN = [
+    {
+        "bundle_root": ".",
+        "mode": "train",
+        "train#trainer#max_epochs": 1,
+    }
+]
+
+TEST_CASE_INFER = [
+    {
+        "bundle_root": ".",
+        "mode": "infer",
+    }
+]
+
+
+class TestVista2d(unittest.TestCase):
+    def setUp(self):
+        self.dataset_dir = tempfile.mkdtemp()
+        self.tmp_output_dir = os.path.join(self.dataset_dir, "output")
+        os.makedirs(self.tmp_output_dir, exist_ok=True)
+        self.dataset_size = 5
+        input_shape = (256, 256)
+        for s in range(self.dataset_size):
+            test_image = np.random.randint(low=0, high=2, size=input_shape).astype(np.int8)
+            test_label = np.random.randint(low=0, high=2, size=input_shape).astype(np.int8)
+            image_filename = os.path.join(self.dataset_dir, f"image_{s}.png")
+            label_filename = os.path.join(self.dataset_dir, f"label_{s}.png")
+            plt.imsave(image_filename, test_image, cmap="gray")
+            plt.imsave(label_filename, test_label, cmap="gray")
+
+        self.bundle_root = "."
+        sys.path = [self.bundle_root] + sys.path
+        from scripts.workflow import VistaCell
+
+        self.workflow = VistaCell
+
+    def tearDown(self):
+        shutil.rmtree(self.dataset_dir)
+
+    @parameterized.expand([TEST_CASE_INFER])
+    def test_infer_config(self, override):
+        # update override with dataset dir
+        override["dataset#data"] = [
+            {
+                "image": os.path.join(self.dataset_dir, f"image_{s}.png"),
+                "label": os.path.join(self.dataset_dir, f"label_{s}.png"),
+            }
+            for s in range(self.dataset_size)
+        ]
+        override["output_dir"] = self.tmp_output_dir
+        workflow = create_workflow(
+            workflow_name=self.workflow,
+            config_file=os.path.join(self.bundle_root, "configs/hyper_parameters.yaml"),
+            meta_file=os.path.join(self.bundle_root, "configs/metadata.json"),
+            **override,
+        )
+
+        # check_properties=False, need to add monai service properties later
+        check_workflow(workflow, check_properties=False)
+
+        expected_output_file = os.path.join(self.tmp_output_dir, f"image_{self.dataset_size-1}.tif")
+        self.assertTrue(os.path.isfile(expected_output_file))
+
+    @parameterized.expand([TEST_CASE_TRAIN])
+    def test_train_config(self, override):
+        # update override with dataset dir
+        override["train#dataset#data"] = [
+            {
+                "image": os.path.join(self.dataset_dir, f"image_{s}.png"),
+                "label": os.path.join(self.dataset_dir, f"label_{s}.png"),
+            }
+            for s in range(self.dataset_size)
+        ]
+        override["dataset#data"] = override["train#dataset#data"]
+
+        workflow = create_workflow(
+            workflow_name=self.workflow,
+            config_file=os.path.join(self.bundle_root, "configs/hyper_parameters.yaml"),
+            meta_file=os.path.join(self.bundle_root, "configs/metadata.json"),
+            **override,
+        )
+
+        # check_properties=False, need to add monai service properties later
+        check_workflow(workflow, check_properties=False)
+
+        # follow up to use trained weights and test eval
+        override["mode"] = "eval"
+        override["pretrained_ckpt_name"] = "model.pt"
+        workflow = create_workflow(
+            workflow_name=self.workflow,
+            config_file=os.path.join(self.bundle_root, "configs/hyper_parameters.yaml"),
+            meta_file=os.path.join(self.bundle_root, "configs/metadata.json"),
+            **override,
+        )
+        check_workflow(workflow, check_properties=False)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/vista2d/unit_tests/test_vista2d_mgpu.py b/vista2d/unit_tests/test_vista2d_mgpu.py
new file mode 100644
index 0000000..af27f2f
--- /dev/null
+++ b/vista2d/unit_tests/test_vista2d_mgpu.py
@@ -0,0 +1,80 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import shutil
+import sys
+import tempfile
+import unittest
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from parameterized import parameterized
+from utils import export_config_and_run_mgpu_cmd
+
+TEST_CASE_TRAIN_MGPU = [
+    {
+        "bundle_root": ".",
+        "workflow_type": "train",
+        "train#trainer#max_epochs": 2,
+    }
+]
+
+
+class TestVista2d(unittest.TestCase):
+    def setUp(self):
+        self.dataset_dir = tempfile.mkdtemp()
+        self.dataset_size = 5
+        input_shape = (256, 256)
+        for s in range(self.dataset_size):
+            test_image = np.random.randint(low=0, high=2, size=input_shape).astype(np.int8)
+            test_label = np.random.randint(low=0, high=2, size=input_shape).astype(np.int8)
+            image_filename = os.path.join(self.dataset_dir, f"image_{s}.png")
+            label_filename = os.path.join(self.dataset_dir, f"label_{s}.png")
+            plt.imsave(image_filename, test_image, cmap="gray")
+            plt.imsave(label_filename, test_label, cmap="gray")
+
+        self.bundle_root = "."
+        sys.path = [self.bundle_root] + sys.path
+        from scripts.workflow import VistaCell
+
+        self.workflow = VistaCell
+
+    def tearDown(self):
+        shutil.rmtree(self.dataset_dir)
+
+    @parameterized.expand([TEST_CASE_TRAIN_MGPU])
+    def test_train_mgpu_config(self, override):
+        override["train#dataset#data"] = [
+            {
+                "image": os.path.join(self.dataset_dir, f"image_{s}.png"),
+                "label": os.path.join(self.dataset_dir, f"label_{s}.png"),
+            }
+            for s in range(self.dataset_size)
+        ]
+        override["dataset#data"] = override["train#dataset#data"]
+
+        output_path = os.path.join(self.bundle_root, "configs/train_override.json")
+        n_gpu = torch.cuda.device_count()
+        export_config_and_run_mgpu_cmd(
+            workflow_name=self.workflow,
+            bundle_root=self.bundle_root,
+            config_file=os.path.join(self.bundle_root, "configs/hyper_parameters.yaml"),
+            meta_file=os.path.join(self.bundle_root, "configs/metadata.json"),
+            override_dict=override,
+            output_path=output_path,
+            ngpu=n_gpu,
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/vista2d/unit_tests/utils.py b/vista2d/unit_tests/utils.py
new file mode 100644
index 0000000..67ecf5e
--- /dev/null
+++ b/vista2d/unit_tests/utils.py
@@ -0,0 +1,86 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import os
+import subprocess
+
+from monai.bundle import ConfigParser, create_workflow
+
+
+def export_overrided_config(config_file, override_dict, output_path):
+    parser = ConfigParser()
+    parser.read_config(config_file)
+    parser.update(pairs=override_dict)
+    ConfigParser.export_config_file(parser.config, output_path, indent=4)
+
+
+def produce_mgpu_cmd(config_file, meta_file, nnodes=1, nproc_per_node=2):
+    cmd = [
+        "torchrun",
+        # "--standalone",
+        f"--nnodes={nnodes}",
+        f"--nproc_per_node={nproc_per_node}",
+        "-m",
+        "monai.bundle",
+        "run_workflow",
+        "scripts.workflow.VistaCell",
+        "--config_file",
+        config_file,
+        "--meta_file",
+        meta_file,
+    ]
+    return cmd
+
+
+def export_config_and_run_mgpu_cmd(
+    config_file,
+    meta_file,
+    override_dict,
+    output_path,
+    workflow_name,
+    bundle_root,
+    workflow_type="train",
+    nnode=1,
+    ngpu=2,
+    check_config=False,
+):
+    """
+    step 1: override the config file and export it
+    step 2: (optional) check the exported config file
+    step 3: produce multi-gpu running command
+    step 4: run produced command
+    """
+    export_overrided_config(config_file=config_file, override_dict=override_dict, output_path=output_path)
+    if check_config is True:
+        workflow = create_workflow(
+            workflow_name=workflow_name,
+            config_file=os.path.join(bundle_root, "configs/hyper_parameters.yaml"),
+            meta_file=os.path.join(bundle_root, "configs/metadata.json"),
+            **override_dict,
+        )
+        check_result = workflow.check_properties()
+        if check_result is not None and len(check_result) > 0:
+            raise ValueError(f"check properties for overrided mgpu configs failed: {check_result}")
+    cmd = produce_mgpu_cmd(config_file=output_path, meta_file=meta_file, nnodes=nnode, nproc_per_node=ngpu)
+    env = os.environ.copy()
+    # ensure customized library can be loaded in subprocess
+    env["PYTHONPATH"] = override_dict.get("bundle_root", ".")
+    subprocess.check_call(cmd, env=env)
+
+
+def check_workflow(workflow, check_properties: bool = False):
+    if check_properties is True:
+        check_result = workflow.check_properties()
+        if check_result is not None and len(check_result) > 0:
+            raise ValueError(f"check properties for workflow failed: {check_result}")
+    workflow.run()
+    workflow.finalize()

From abdf8e28301c108787378622142d116f2f5c72a4 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 1 Aug 2024 23:49:56 +0000
Subject: [PATCH 2/4] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 vista2d/README.md                             |   6 +-
 .../all_file_downloader.py                    |   9 +-
 .../download_preprocessor/generate_json.py    | 309 ++++++++++++---
 vista2d/download_preprocessor/process_data.py | 136 +++++--
 vista2d/license.code                          |   2 +-
 vista2d/license.weights                       |   1 -
 .../cell_distributed_weighted_sampler.py      |  21 +-
 vista2d/scripts/cell_sam_wrapper.py           |  14 +-
 vista2d/scripts/components.py                 |  82 +++-
 vista2d/scripts/utils.py                      |  18 +-
 vista2d/scripts/workflow.py                   | 359 ++++++++++++++----
 vista2d/unit_tests/test_vista2d.py            |  12 +-
 vista2d/unit_tests/test_vista2d_mgpu.py       |   8 +-
 vista2d/unit_tests/utils.py                   |  12 +-
 14 files changed, 774 insertions(+), 215 deletions(-)

diff --git a/vista2d/README.md b/vista2d/README.md
index 7db2679..56d9038 100644
--- a/vista2d/README.md
+++ b/vista2d/README.md
@@ -25,13 +25,13 @@ A pretrained model was trained on collection of 15K public microscopy images. Th
 - Robust deep learning algorithm based on transformers
 - Generalist model as compared to specialist models
 - Multiple dataset sources and file formats supported
-- Multiple modalities of imaging data collectively supported 
+- Multiple modalities of imaging data collectively supported
 - Multi-GPU and multinode training support
 
 
 ### Generalization performance
 
-Evaluation was performed for the VISTA2D model with multiple public datasets, such as TissueNet, LIVECell, Omnipose, DeepBacs, Cellpose, and [more](./docs/data_license.txt). A total of ~15K annotated cell images were collected to train the generalist VISTA2D model. This ensured broad coverage of many different types of cells, which were acquired by various imaging acquisition types. The benchmark results of the experiment were performed on held-out test sets for each public dataset that were already defined by the dataset contributors. Average precision at an IoU threshold of 0.5 was used for evaluating performance. The benchmark results are reported in comparison with the best numbers found in the literature, in addition to a specialist VISTA2D model trained only on a particular dataset or a subset of data. 
+Evaluation was performed for the VISTA2D model with multiple public datasets, such as TissueNet, LIVECell, Omnipose, DeepBacs, Cellpose, and [more](./docs/data_license.txt). A total of ~15K annotated cell images were collected to train the generalist VISTA2D model. This ensured broad coverage of many different types of cells, which were acquired by various imaging acquisition types. The benchmark results of the experiment were performed on held-out test sets for each public dataset that were already defined by the dataset contributors. Average precision at an IoU threshold of 0.5 was used for evaluating performance. The benchmark results are reported in comparison with the best numbers found in the literature, in addition to a specialist VISTA2D model trained only on a particular dataset or a subset of data.
 
 <div align="center"> <img src="https://developer-blogs.nvidia.com/wp-content/uploads/2024/04/vista-2d-model-precision-versus-specialist-model-baseline-performance.png" width="800"/> </div>
 
@@ -175,4 +175,4 @@ The codebase is under Apache 2.0 Licence. The model weight is released under CC-
 
 ## Acknowledgement
 - [segment-anything](https://github.com/facebookresearch/segment-anything)
-- [Cellpose](https://www.cellpose.org/)
\ No newline at end of file
+- [Cellpose](https://www.cellpose.org/)
diff --git a/vista2d/download_preprocessor/all_file_downloader.py b/vista2d/download_preprocessor/all_file_downloader.py
index a04805a..c8f4370 100644
--- a/vista2d/download_preprocessor/all_file_downloader.py
+++ b/vista2d/download_preprocessor/all_file_downloader.py
@@ -54,7 +54,12 @@ def download_files(url_dict, directory):
 
 def main():
     parser = argparse.ArgumentParser(description="Process some integers.")
-    parser.add_argument("--dir", type=str, help="Directory to download files to", default="/set/the/path")
+    parser.add_argument(
+        "--dir",
+        type=str,
+        help="Directory to download files to",
+        default="/set/the/path",
+    )
 
     args = parser.parse_args()
     directory = os.path.normpath(args.dir)
@@ -63,7 +68,7 @@ def main():
         "deepbacs.zip": "https://zenodo.org/records/5551009/files/DeepBacs_Data_Segmentation_StarDist_MIXED_dataset.zip?download=1",
         "nips_test.zip": "https://zenodo.org/records/10719375/files/Testing.zip?download=1",
         "nips_train.zip": "https://zenodo.org/records/10719375/files/Training-labeled.zip?download=1",
-        "livecell": "wget --recursive --no-parent --cut-dirs=0 --timestamping -i urls.txt --directory-prefix="
+        "livecell": "wget --recursive --no-parent --cut-dirs=0 --timestamping -i urls.txt --directory-prefix=",
         # Add URLs with keys here
     }
     download_files(url_dict, directory)
diff --git a/vista2d/download_preprocessor/generate_json.py b/vista2d/download_preprocessor/generate_json.py
index 67c495b..8aacde2 100644
--- a/vista2d/download_preprocessor/generate_json.py
+++ b/vista2d/download_preprocessor/generate_json.py
@@ -47,9 +47,12 @@ def guess_convert_to_uint16(img, margin=30):
             imsmall = im[::4, ::4]  # subsample
             # imsmall = im
 
-            scale = int(np.round(1 / np.min(np.diff(np.unique(imsmall)))))  # guessing scale
+            scale = int(
+                np.round(1 / np.min(np.diff(np.unique(imsmall))))
+            )  # guessing scale
             test = [
-                (np.sum((imsmall * k) % 1)) for k in range(scale - margin, scale + margin)
+                (np.sum((imsmall * k) % 1))
+                for k in range(scale - margin, scale + margin)
             ]  # finetune, guess a multiplier that makes all pixels integers
             sid = np.argmin(test)  # fine tune scale
             # print('guessing scale', scale, test[margin], 'fine tuning scale', scale - margin + sid, 'dif', test[sid], 'time', time.time()-start)
@@ -159,7 +162,10 @@ def remove_overlaps(masks, medians, overlap_threshold=0.75):
     masks[tocell, overlaps[:, 0], overlaps[:, 1]] = 1
 
     # labels should be 1 to mask.shape[0]
-    masks = masks.astype(int) * np.arange(1, masks.shape[0] + 1, 1, int)[:, np.newaxis, np.newaxis]
+    masks = (
+        masks.astype(int)
+        * np.arange(1, masks.shape[0] + 1, 1, int)[:, np.newaxis, np.newaxis]
+    )
     masks = masks.sum(axis=0)
     gc.collect()
     return masks
@@ -181,11 +187,25 @@ def livecell_json_files(dataset_dir, json_f_path):
             print(f"Working on split: {split}")
 
             if split == "test":
-                img_path = os.path.join(dataset_dir, "images", "livecell_test_images", each_cell_tp)
-                msk_path = os.path.join(dataset_dir, "images", "livecell_test_images", each_cell_tp + "_masks")
+                img_path = os.path.join(
+                    dataset_dir, "images", "livecell_test_images", each_cell_tp
+                )
+                msk_path = os.path.join(
+                    dataset_dir,
+                    "images",
+                    "livecell_test_images",
+                    each_cell_tp + "_masks",
+                )
             else:
-                img_path = os.path.join(dataset_dir, "images", "livecell_train_val_images", each_cell_tp)
-                msk_path = os.path.join(dataset_dir, "images", "livecell_train_val_images", each_cell_tp + "_masks")
+                img_path = os.path.join(
+                    dataset_dir, "images", "livecell_train_val_images", each_cell_tp
+                )
+                msk_path = os.path.join(
+                    dataset_dir,
+                    "images",
+                    "livecell_train_val_images",
+                    each_cell_tp + "_masks",
+                )
             if not os.path.exists(msk_path):
                 os.makedirs(msk_path)
 
@@ -205,12 +225,18 @@ def livecell_json_files(dataset_dir, json_f_path):
             height = []
             width = []
             for index, im in enumerate(images):
-                print("Status: {}/{}, Process image: {}".format(index, len(images), im["file_name"]))
+                print(
+                    "Status: {}/{}, Process image: {}".format(
+                        index, len(images), im["file_name"]
+                    )
+                )
                 if (
                     im["file_name"] == "BV2_Phase_C4_2_03d00h00m_1.tif"
                     or im["file_name"] == "BV2_Phase_C4_2_03d00h00m_3.tif"
                 ):
-                    print("Skipping the file: BV2_Phase_C4_2_03d00h00m_1.tif, as it is troublesome")
+                    print(
+                        "Skipping the file: BV2_Phase_C4_2_03d00h00m_1.tif, as it is troublesome"
+                    )
                     continue
                 # load image
                 img = Image.open(os.path.join(img_path, im["file_name"])).convert("L")
@@ -230,7 +256,14 @@ def livecell_json_files(dataset_dir, json_f_path):
                     mask = annotation.annToMask(ann)
                     masks.append(mask)
                     ypix, xpix = mask.nonzero()
-                    medians.append(np.array([ypix.mean().astype(np.float32), xpix.mean().astype(np.float32)]))
+                    medians.append(
+                        np.array(
+                            [
+                                ypix.mean().astype(np.float32),
+                                xpix.mean().astype(np.float32),
+                            ]
+                        )
+                    )
                     k += 1
                     # add instance mask to image mask
                     # msk = np.add(msk, mask*idx)
@@ -249,7 +282,9 @@ def livecell_json_files(dataset_dir, json_f_path):
                 # cell_type = t_filename.split('_')[0] #? not used
                 new_mask_name = t_filename[:-4] + "_masks.tif"
                 # mask_pil.save(os.path.join(msk_path, new_mask_name))
-                imageio.imwrite(os.path.join(msk_path, new_mask_name), min_label_precision(masks))
+                imageio.imwrite(
+                    os.path.join(msk_path, new_mask_name), min_label_precision(masks)
+                )
                 gc.collect()
 
             print(f"In total {len(images)} images")
@@ -257,10 +292,18 @@ def livecell_json_files(dataset_dir, json_f_path):
         # The directory containing your files
         # cell_type = 'BV2'
         json_save_path = os.path.join(json_f_path, f"lc_{each_cell_tp}.json")
-        directory = os.path.join(dataset_dir, "images", "livecell_train_val_images", each_cell_tp)
-        mask_directory = os.path.join(dataset_dir, "images", "livecell_train_val_images", each_cell_tp + "_masks")
-        test_directory = os.path.join(dataset_dir, "images", "livecell_test_images", each_cell_tp)
-        mask_test_directory = os.path.join(dataset_dir, "images", "livecell_test_images", each_cell_tp + "_masks")
+        directory = os.path.join(
+            dataset_dir, "images", "livecell_train_val_images", each_cell_tp
+        )
+        mask_directory = os.path.join(
+            dataset_dir, "images", "livecell_train_val_images", each_cell_tp + "_masks"
+        )
+        test_directory = os.path.join(
+            dataset_dir, "images", "livecell_test_images", each_cell_tp
+        )
+        mask_test_directory = os.path.join(
+            dataset_dir, "images", "livecell_test_images", each_cell_tp + "_masks"
+        )
         # List to hold all image-mask pairs
         data_pairs = []
         test_data_pairs = []
@@ -278,7 +321,11 @@ def livecell_json_files(dataset_dir, json_f_path):
                     data_pairs.append(
                         {
                             "image": os.path.join(
-                                "livecell_dataset", "images", "livecell_train_val_images", each_cell_tp, filename
+                                "livecell_dataset",
+                                "images",
+                                "livecell_train_val_images",
+                                each_cell_tp,
+                                filename,
                             ),
                             "label": os.path.join(
                                 "livecell_dataset",
@@ -318,7 +365,11 @@ def livecell_json_files(dataset_dir, json_f_path):
                     test_data_pairs.append(
                         {
                             "image": os.path.join(
-                                "livecell_dataset", "images", "livecell_test_images", each_cell_tp, filename
+                                "livecell_dataset",
+                                "images",
+                                "livecell_test_images",
+                                each_cell_tp,
+                                filename,
                             ),
                             "label": os.path.join(
                                 "livecell_dataset",
@@ -370,18 +421,33 @@ def tissuenet_json_files(dataset_dir, json_f_path):
                     print(f"Working on {t} {p}")
 
                     for k, i in enumerate(ix):
-                        print(f"Status: {k}/{len(ix)} {tp}/{len(tlabels) * len(plabels)} {t} {p}")
+                        print(
+                            f"Status: {k}/{len(ix)} {tp}/{len(tlabels) * len(plabels)} {t} {p}"
+                        )
                         img = data[i].transpose(2, 0, 1)
                         label = labels[i][:, :, 0]
 
-                        img = guess_convert_to_uint16(img)  # guess inverse scale and convert to uint16
+                        img = guess_convert_to_uint16(
+                            img
+                        )  # guess inverse scale and convert to uint16
                         label = min_label_precision(label)
 
                         if folder == "train":
-                            img = img.reshape(2, 2, 256, 2, 256).transpose(0, 1, 3, 2, 4).reshape(2, 4, 256, 256)
-                            label = label.reshape(2, 256, 2, 256).transpose(0, 2, 1, 3).reshape(4, 256, 256)
-
-                            zero_channel = np.zeros((1, img.shape[1], img.shape[2], img.shape[3]), dtype=img.dtype)
+                            img = (
+                                img.reshape(2, 2, 256, 2, 256)
+                                .transpose(0, 1, 3, 2, 4)
+                                .reshape(2, 4, 256, 256)
+                            )
+                            label = (
+                                label.reshape(2, 256, 2, 256)
+                                .transpose(0, 2, 1, 3)
+                                .reshape(4, 256, 256)
+                            )
+
+                            zero_channel = np.zeros(
+                                (1, img.shape[1], img.shape[2], img.shape[3]),
+                                dtype=img.dtype,
+                            )
 
                             # Concatenate the zero channel with the original array along the first dimension
                             new_array = np.concatenate([img, zero_channel], axis=0)
@@ -391,16 +457,34 @@ def tissuenet_json_files(dataset_dir, json_f_path):
                                 # imwrite(f'/scratch_2/cell_imaging_2023/tissuenet/tissuenet_v1.0/tissuenet_1_cellpose_way/{folder}/{t}_{p}_{k}_{j}_masks.tif', label[j])
                                 img_name = f"{folder}/{t}_{p}_{k}_{j}.tif"
                                 mask_name = f"{folder}/{t}_{p}_{k}_{j}_masks.tif"
-                                imageio.imwrite(os.path.join(dataset_dir, "tissuenet_1.0", img_name), new_array[:, j])
-                                imageio.imwrite(os.path.join(dataset_dir, "tissuenet_1.0", mask_name), label[j])
+                                imageio.imwrite(
+                                    os.path.join(
+                                        dataset_dir, "tissuenet_1.0", img_name
+                                    ),
+                                    new_array[:, j],
+                                )
+                                imageio.imwrite(
+                                    os.path.join(
+                                        dataset_dir, "tissuenet_1.0", mask_name
+                                    ),
+                                    label[j],
+                                )
                         else:
-                            zero_channel = np.zeros((1, img.shape[1], img.shape[2]), dtype=img.dtype)
+                            zero_channel = np.zeros(
+                                (1, img.shape[1], img.shape[2]), dtype=img.dtype
+                            )
                             new_array = np.concatenate([img, zero_channel], axis=0)
                             # reshaped_array = np.transpose(new_array, (1, 2, 0))
                             img_name = f"{folder}/{t}_{p}_{k}.tif"
                             mask_name = f"{folder}/{t}_{p}_{k}_masks.tif"
-                            imageio.imwrite(os.path.join(dataset_dir, "tissuenet_1.0", img_name), new_array)
-                            imageio.imwrite(os.path.join(dataset_dir, "tissuenet_1.0", mask_name), label)
+                            imageio.imwrite(
+                                os.path.join(dataset_dir, "tissuenet_1.0", img_name),
+                                new_array,
+                            )
+                            imageio.imwrite(
+                                os.path.join(dataset_dir, "tissuenet_1.0", mask_name),
+                                label,
+                            )
 
     t_p_combos = [
         ["breast", "imc"],
@@ -424,31 +508,58 @@ def tissuenet_json_files(dataset_dir, json_f_path):
         json_f_subset_path = os.path.join(json_f_path, json_f_name)
 
         tp_match = each_t_p[0] + "_" + each_t_p[1]
-        train_filenames = get_filenames_exclude_masks(os.path.join(dataset_dir, "tissuenet_1.0", "train"), tp_match)
-        val_filenames = get_filenames_exclude_masks(os.path.join(dataset_dir, "tissuenet_1.0", "val"), tp_match)
-        test_filenames = get_filenames_exclude_masks(os.path.join(dataset_dir, "tissuenet_1.0", "test"), tp_match)
+        train_filenames = get_filenames_exclude_masks(
+            os.path.join(dataset_dir, "tissuenet_1.0", "train"), tp_match
+        )
+        val_filenames = get_filenames_exclude_masks(
+            os.path.join(dataset_dir, "tissuenet_1.0", "val"), tp_match
+        )
+        test_filenames = get_filenames_exclude_masks(
+            os.path.join(dataset_dir, "tissuenet_1.0", "test"), tp_match
+        )
 
         train_data_list = []
         test_data_list = []
 
         for each_tf in train_filenames:
             t_dict = {
-                "image": os.path.join("tissuenet_dataset", "tissuenet_1.0", "train", each_tf),
-                "label": os.path.join("tissuenet_dataset", "tissuenet_1.0", "train", each_tf[:-4] + "_masks.tif"),
+                "image": os.path.join(
+                    "tissuenet_dataset", "tissuenet_1.0", "train", each_tf
+                ),
+                "label": os.path.join(
+                    "tissuenet_dataset",
+                    "tissuenet_1.0",
+                    "train",
+                    each_tf[:-4] + "_masks.tif",
+                ),
             }
             train_data_list.append(t_dict)
 
         for each_vf in val_filenames:
             t_dict = {
-                "image": os.path.join("tissuenet_dataset", "tissuenet_1.0", "val", each_vf),
-                "label": os.path.join("tissuenet_dataset", "tissuenet_1.0", "val", each_vf[:-4] + "_masks.tif"),
+                "image": os.path.join(
+                    "tissuenet_dataset", "tissuenet_1.0", "val", each_vf
+                ),
+                "label": os.path.join(
+                    "tissuenet_dataset",
+                    "tissuenet_1.0",
+                    "val",
+                    each_vf[:-4] + "_masks.tif",
+                ),
             }
             train_data_list.append(t_dict)
 
         for each_tf in test_filenames:
             t_dict = {
-                "image": os.path.join("tissuenet_dataset", "tissuenet_1.0", "test", each_tf),
-                "label": os.path.join("tissuenet_dataset", "tissuenet_1.0", "test", each_tf[:-4] + "_masks.tif"),
+                "image": os.path.join(
+                    "tissuenet_dataset", "tissuenet_1.0", "test", each_tf
+                ),
+                "label": os.path.join(
+                    "tissuenet_dataset",
+                    "tissuenet_1.0",
+                    "test",
+                    each_tf[:-4] + "_masks.tif",
+                ),
             }
             test_data_list.append(t_dict)
 
@@ -511,16 +622,36 @@ def omnipose_json_file(dataset_dir, json_path):
                 base_name = os.path.splitext(image_file)[0]
 
                 # Construct the label file name by adding '_label' before the extension
-                label_file = base_name + "_masks.tif"  # + os.path.splitext(image_file)[1]
+                label_file = (
+                    base_name + "_masks.tif"
+                )  # + os.path.splitext(image_file)[1]
                 flows_file = base_name + "_flows.tif"
                 # Check if the corresponding label file exists in the labels folder
                 if label_file in os.listdir(os.path.join(images_folder, each_sub)):
                     # Add the file names to the training data list
                     training_data.append(
                         {
-                            "image": os.path.join("omnipose_dataset", each_op, "train_sorted", each_sub, image_file),
-                            "label": os.path.join("omnipose_dataset", each_op, "train_sorted", each_sub, label_file),
-                            "flows": os.path.join("omnipose_dataset", each_op, "train_sorted", each_sub, flows_file),
+                            "image": os.path.join(
+                                "omnipose_dataset",
+                                each_op,
+                                "train_sorted",
+                                each_sub,
+                                image_file,
+                            ),
+                            "label": os.path.join(
+                                "omnipose_dataset",
+                                each_op,
+                                "train_sorted",
+                                each_sub,
+                                label_file,
+                            ),
+                            "flows": os.path.join(
+                                "omnipose_dataset",
+                                each_op,
+                                "train_sorted",
+                                each_sub,
+                                flows_file,
+                            ),
                         }
                     )
 
@@ -547,24 +678,38 @@ def omnipose_json_file(dataset_dir, json_path):
         # Loop through each image file to find its corresponding label file
         for each_test_sub in test_sub_dirs:
             # List files in the images folder
-            test_image_files = os.listdir(os.path.join(test_images_folder, each_test_sub))
+            test_image_files = os.listdir(
+                os.path.join(test_images_folder, each_test_sub)
+            )
             for image_file in test_image_files:
                 # Extract the name without the extension
                 base_name = os.path.splitext(image_file)[0]
 
                 # Construct the label file name by adding '_label' before the extension
-                label_file = base_name + "_masks.tif"  # + os.path.splitext(image_file)[1]
+                label_file = (
+                    base_name + "_masks.tif"
+                )  # + os.path.splitext(image_file)[1]
 
                 # Check if the corresponding label file exists in the labels folder
-                if label_file in os.listdir(os.path.join(test_images_folder, each_test_sub)):
+                if label_file in os.listdir(
+                    os.path.join(test_images_folder, each_test_sub)
+                ):
                     # Add the file names to the training data list
                     testing_data.append(
                         {
                             "image": os.path.join(
-                                "omnipose_dataset", each_op, "test_sorted", each_test_sub, image_file
+                                "omnipose_dataset",
+                                each_op,
+                                "test_sorted",
+                                each_test_sub,
+                                image_file,
                             ),
                             "label": os.path.join(
-                                "omnipose_dataset", each_op, "test_sorted", each_test_sub, label_file
+                                "omnipose_dataset",
+                                each_op,
+                                "test_sorted",
+                                each_test_sub,
+                                label_file,
                             ),
                         }
                     )
@@ -609,8 +754,12 @@ def nips_json_file(dataset_dir, json_f_path):
                 # Add the file names to the training data list
                 data_pairs.append(
                     {
-                        "image": os.path.join("nips_dataset", "Training-labeled", "images", filename),
-                        "label": os.path.join("nips_dataset", "Training-labeled", "labels", label_file),
+                        "image": os.path.join(
+                            "nips_dataset", "Training-labeled", "images", filename
+                        ),
+                        "label": os.path.join(
+                            "nips_dataset", "Training-labeled", "labels", label_file
+                        ),
                     }
                 )
 
@@ -644,8 +793,12 @@ def nips_json_file(dataset_dir, json_f_path):
                 # Add the file names to the training data list
                 test_data_pairs.append(
                     {
-                        "image": os.path.join("nips_dataset", "Testing", "Public", "images", filename),
-                        "label": os.path.join("nips_dataset", "Testing", "Public", "labels", label_file),
+                        "image": os.path.join(
+                            "nips_dataset", "Testing", "Public", "images", filename
+                        ),
+                        "label": os.path.join(
+                            "nips_dataset", "Testing", "Public", "labels", label_file
+                        ),
                     }
                 )
 
@@ -686,8 +839,13 @@ def kaggle_json_file(dataset_dir, json_f_path):
                 # image_data = imageio.imread(image_file)
                 # normalized_image = normalize_image(image_data[..., :3])
                 # imageio.imwrite(os.path.join(saving_path, f"{filename_prefix}img.tiff"), normalized_image)
-                shutil.copyfile(image_file, os.path.join(saving_path, f"{filename_prefix}img.png"))
-                imageio.imwrite(os.path.join(saving_path, f"{filename_prefix}img_masks.tiff"), mask_data)
+                shutil.copyfile(
+                    image_file, os.path.join(saving_path, f"{filename_prefix}img.png")
+                )
+                imageio.imwrite(
+                    os.path.join(saving_path, f"{filename_prefix}img_masks.tiff"),
+                    mask_data,
+                )
 
     directory = saving_path
 
@@ -706,8 +864,12 @@ def kaggle_json_file(dataset_dir, json_f_path):
                 # Add the pair to the list
                 data_pairs.append(
                     {
-                        "image": os.path.join("kaggle_dataset", "instance_processed_data", filename),
-                        "label": os.path.join("kaggle_dataset", "instance_processed_data", mask_filename),
+                        "image": os.path.join(
+                            "kaggle_dataset", "instance_processed_data", filename
+                        ),
+                        "label": os.path.join(
+                            "kaggle_dataset", "instance_processed_data", mask_filename
+                        ),
                     }
                 )
 
@@ -762,8 +924,12 @@ def deepbacs_json_file(dataset_dir, json_f_path):
                 # Add the pair to the list
                 data_pairs.append(
                     {
-                        "image": os.path.join("deepbacs_dataset", "training", "source", filename),
-                        "label": os.path.join("deepbacs_dataset", "training", "target", mask_filename),
+                        "image": os.path.join(
+                            "deepbacs_dataset", "training", "source", filename
+                        ),
+                        "label": os.path.join(
+                            "deepbacs_dataset", "training", "target", mask_filename
+                        ),
                     }
                 )
 
@@ -794,8 +960,12 @@ def deepbacs_json_file(dataset_dir, json_f_path):
                 # Add the pair to the list
                 test_data_pairs.append(
                     {
-                        "image": os.path.join("deepbacs_dataset", "test", "source", filename),
-                        "label": os.path.join("deepbacs_dataset", "test", "target", filename),
+                        "image": os.path.join(
+                            "deepbacs_dataset", "test", "source", filename
+                        ),
+                        "label": os.path.join(
+                            "deepbacs_dataset", "test", "target", filename
+                        ),
                     }
                 )
 
@@ -837,7 +1007,9 @@ def cellpose_json_file(dataset_dir, json_f_path):
                 data_pairs.append(
                     {
                         "image": os.path.join("cellpose_dataset", "train", filename),
-                        "label": os.path.join("cellpose_dataset", "train", mask_filename),
+                        "label": os.path.join(
+                            "cellpose_dataset", "train", mask_filename
+                        ),
                     }
                 )
 
@@ -869,7 +1041,9 @@ def cellpose_json_file(dataset_dir, json_f_path):
                 test_data_pairs.append(
                     {
                         "image": os.path.join("cellpose_dataset", "test", filename),
-                        "label": os.path.join("cellpose_dataset", "test", mask_filename),
+                        "label": os.path.join(
+                            "cellpose_dataset", "test", mask_filename
+                        ),
                     }
                 )
 
@@ -896,7 +1070,12 @@ def extract_zip(zip_path, extract_to):
 
 def main():
     parser = argparse.ArgumentParser(description="Process some integers.")
-    parser.add_argument("--dir", type=str, help="Directory of datasets to generate json", default="/set/the/path")
+    parser.add_argument(
+        "--dir",
+        type=str,
+        help="Directory of datasets to generate json",
+        default="/set/the/path",
+    )
 
     args = parser.parse_args()
     data_root_path = os.path.normpath(args.dir)
@@ -964,7 +1143,9 @@ def main():
             kaggle_json_file(dataset_dir=dataset_path, json_f_path=json_f_path)
 
         elif key == "livecell_dataset":
-            print("Creating LiveCell Dataset Json files ... Please note that 7 files will be created from livecell")
+            print(
+                "Creating LiveCell Dataset Json files ... Please note that 7 files will be created from livecell"
+            )
             dataset_path = os.path.join(data_root_path, key)
             json_base_name = os.path.join(data_root_path, "json_files")
             livecell_json_files(dataset_dir=dataset_path, json_f_path=json_base_name)
@@ -976,7 +1157,9 @@ def main():
             deepbacs_json_file(dataset_dir=dataset_path, json_f_path=json_path)
 
         elif key == "tissuenet_dataset":
-            print("Creating TissueNet Dataset Json files ... Please note that 13 files will be created from tissuenet")
+            print(
+                "Creating TissueNet Dataset Json files ... Please note that 13 files will be created from tissuenet"
+            )
             dataset_path = os.path.join(data_root_path, key)
             json_base_name = os.path.join(data_root_path, "json_files")
             tissuenet_json_files(dataset_dir=dataset_path, json_f_path=json_base_name)
diff --git a/vista2d/download_preprocessor/process_data.py b/vista2d/download_preprocessor/process_data.py
index 0d5c8f2..ae216ff 100644
--- a/vista2d/download_preprocessor/process_data.py
+++ b/vista2d/download_preprocessor/process_data.py
@@ -41,9 +41,12 @@ def guess_convert_to_uint16(img, margin=30):
             imsmall = im[::4, ::4]  # subsample
             # imsmall = im
 
-            scale = int(np.round(1 / np.min(np.diff(np.unique(imsmall)))))  # guessing scale
+            scale = int(
+                np.round(1 / np.min(np.diff(np.unique(imsmall))))
+            )  # guessing scale
             test = [
-                (np.sum((imsmall * k) % 1)) for k in range(scale - margin, scale + margin)
+                (np.sum((imsmall * k) % 1))
+                for k in range(scale - margin, scale + margin)
             ]  # finetune, guess a multiplier that makes all pixels integers
             sid = np.argmin(test)  # fine tune scale
             # print('guessing scale', scale, test[margin], 'fine tuning scale', scale - margin + sid, 'dif', test[sid], 'time', time.time()-start)
@@ -131,7 +134,10 @@ def remove_overlaps(masks, medians, overlap_threshold=0.75):
     masks[tocell, overlaps[:, 0], overlaps[:, 1]] = 1
 
     # labels should be 1 to mask.shape[0]
-    masks = masks.astype(int) * np.arange(1, masks.shape[0] + 1, 1, int)[:, np.newaxis, np.newaxis]
+    masks = (
+        masks.astype(int)
+        * np.arange(1, masks.shape[0] + 1, 1, int)[:, np.newaxis, np.newaxis]
+    )
     masks = masks.sum(axis=0)
     gc.collect()
     return masks
@@ -151,11 +157,25 @@ def livecell_process_files(dataset_dir):
             print(f"Working on split: {split}")
 
             if split == "test":
-                img_path = os.path.join(dataset_dir, "images", "livecell_test_images", each_cell_tp)
-                msk_path = os.path.join(dataset_dir, "images", "livecell_test_images", each_cell_tp + "_masks")
+                img_path = os.path.join(
+                    dataset_dir, "images", "livecell_test_images", each_cell_tp
+                )
+                msk_path = os.path.join(
+                    dataset_dir,
+                    "images",
+                    "livecell_test_images",
+                    each_cell_tp + "_masks",
+                )
             else:
-                img_path = os.path.join(dataset_dir, "images", "livecell_train_val_images", each_cell_tp)
-                msk_path = os.path.join(dataset_dir, "images", "livecell_train_val_images", each_cell_tp + "_masks")
+                img_path = os.path.join(
+                    dataset_dir, "images", "livecell_train_val_images", each_cell_tp
+                )
+                msk_path = os.path.join(
+                    dataset_dir,
+                    "images",
+                    "livecell_train_val_images",
+                    each_cell_tp + "_masks",
+                )
             if not os.path.exists(msk_path):
                 os.makedirs(msk_path)
 
@@ -175,12 +195,18 @@ def livecell_process_files(dataset_dir):
             height = []
             width = []
             for index, im in enumerate(images):
-                print("Status: {}/{}, Process image: {}".format(index, len(images), im["file_name"]))
+                print(
+                    "Status: {}/{}, Process image: {}".format(
+                        index, len(images), im["file_name"]
+                    )
+                )
                 if (
                     im["file_name"] == "BV2_Phase_C4_2_03d00h00m_1.tif"
                     or im["file_name"] == "BV2_Phase_C4_2_03d00h00m_3.tif"
                 ):
-                    print("Skipping the file: BV2_Phase_C4_2_03d00h00m_1.tif, as it is troublesome")
+                    print(
+                        "Skipping the file: BV2_Phase_C4_2_03d00h00m_1.tif, as it is troublesome"
+                    )
                     continue
                 # load image
                 img = Image.open(os.path.join(img_path, im["file_name"])).convert("L")
@@ -200,7 +226,14 @@ def livecell_process_files(dataset_dir):
                     mask = annotation.annToMask(ann)
                     masks.append(mask)
                     ypix, xpix = mask.nonzero()
-                    medians.append(np.array([ypix.mean().astype(np.float32), xpix.mean().astype(np.float32)]))
+                    medians.append(
+                        np.array(
+                            [
+                                ypix.mean().astype(np.float32),
+                                xpix.mean().astype(np.float32),
+                            ]
+                        )
+                    )
                     k += 1
                     # add instance mask to image mask
                     # msk = np.add(msk, mask*idx)
@@ -219,7 +252,9 @@ def livecell_process_files(dataset_dir):
                 # cell_type = t_filename.split('_')[0] #? not used
                 new_mask_name = t_filename[:-4] + "_masks.tif"
                 # mask_pil.save(os.path.join(msk_path, new_mask_name))
-                imageio.imwrite(os.path.join(msk_path, new_mask_name), min_label_precision(masks))
+                imageio.imwrite(
+                    os.path.join(msk_path, new_mask_name), min_label_precision(masks)
+                )
                 gc.collect()
 
             print(f"In total {len(images)} images")
@@ -254,18 +289,33 @@ def tissuenet_process_files(dataset_dir):
                     print(f"Working on {t} {p}")
 
                     for k, i in enumerate(ix):
-                        print(f"Status: {k}/{len(ix)} {tp}/{len(tlabels) * len(plabels)} {t} {p}")
+                        print(
+                            f"Status: {k}/{len(ix)} {tp}/{len(tlabels) * len(plabels)} {t} {p}"
+                        )
                         img = data[i].transpose(2, 0, 1)
                         label = labels[i][:, :, 0]
 
-                        img = guess_convert_to_uint16(img)  # guess inverse scale and convert to uint16
+                        img = guess_convert_to_uint16(
+                            img
+                        )  # guess inverse scale and convert to uint16
                         label = min_label_precision(label)
 
                         if folder == "train":
-                            img = img.reshape(2, 2, 256, 2, 256).transpose(0, 1, 3, 2, 4).reshape(2, 4, 256, 256)
-                            label = label.reshape(2, 256, 2, 256).transpose(0, 2, 1, 3).reshape(4, 256, 256)
-
-                            zero_channel = np.zeros((1, img.shape[1], img.shape[2], img.shape[3]), dtype=img.dtype)
+                            img = (
+                                img.reshape(2, 2, 256, 2, 256)
+                                .transpose(0, 1, 3, 2, 4)
+                                .reshape(2, 4, 256, 256)
+                            )
+                            label = (
+                                label.reshape(2, 256, 2, 256)
+                                .transpose(0, 2, 1, 3)
+                                .reshape(4, 256, 256)
+                            )
+
+                            zero_channel = np.zeros(
+                                (1, img.shape[1], img.shape[2], img.shape[3]),
+                                dtype=img.dtype,
+                            )
 
                             # Concatenate the zero channel with the original array along the first dimension
                             new_array = np.concatenate([img, zero_channel], axis=0)
@@ -275,16 +325,34 @@ def tissuenet_process_files(dataset_dir):
                                 # imwrite(f'/scratch_2/cell_imaging_2023/tissuenet/tissuenet_v1.0/tissuenet_1_cellpose_way/{folder}/{t}_{p}_{k}_{j}_masks.tif', label[j])
                                 img_name = f"{folder}/{t}_{p}_{k}_{j}.tif"
                                 mask_name = f"{folder}/{t}_{p}_{k}_{j}_masks.tif"
-                                imageio.imwrite(os.path.join(dataset_dir, "tissuenet_1.0", img_name), new_array[:, j])
-                                imageio.imwrite(os.path.join(dataset_dir, "tissuenet_1.0", mask_name), label[j])
+                                imageio.imwrite(
+                                    os.path.join(
+                                        dataset_dir, "tissuenet_1.0", img_name
+                                    ),
+                                    new_array[:, j],
+                                )
+                                imageio.imwrite(
+                                    os.path.join(
+                                        dataset_dir, "tissuenet_1.0", mask_name
+                                    ),
+                                    label[j],
+                                )
                         else:
-                            zero_channel = np.zeros((1, img.shape[1], img.shape[2]), dtype=img.dtype)
+                            zero_channel = np.zeros(
+                                (1, img.shape[1], img.shape[2]), dtype=img.dtype
+                            )
                             new_array = np.concatenate([img, zero_channel], axis=0)
                             # reshaped_array = np.transpose(new_array, (1, 2, 0))
                             img_name = f"{folder}/{t}_{p}_{k}.tif"
                             mask_name = f"{folder}/{t}_{p}_{k}_masks.tif"
-                            imageio.imwrite(os.path.join(dataset_dir, "tissuenet_1.0", img_name), new_array)
-                            imageio.imwrite(os.path.join(dataset_dir, "tissuenet_1.0", mask_name), label)
+                            imageio.imwrite(
+                                os.path.join(dataset_dir, "tissuenet_1.0", img_name),
+                                new_array,
+                            )
+                            imageio.imwrite(
+                                os.path.join(dataset_dir, "tissuenet_1.0", mask_name),
+                                label,
+                            )
 
 
 def kaggle_process_files(dataset_dir):
@@ -316,8 +384,13 @@ def kaggle_process_files(dataset_dir):
                 # image_data = imageio.imread(image_file)
                 # normalized_image = normalize_image(image_data[..., :3])
                 # imageio.imwrite(os.path.join(saving_path, f"{filename_prefix}img.tiff"), normalized_image)
-                shutil.copyfile(image_file, os.path.join(saving_path, f"{filename_prefix}img.png"))
-                imageio.imwrite(os.path.join(saving_path, f"{filename_prefix}img_masks.tiff"), mask_data)
+                shutil.copyfile(
+                    image_file, os.path.join(saving_path, f"{filename_prefix}img.png")
+                )
+                imageio.imwrite(
+                    os.path.join(saving_path, f"{filename_prefix}img_masks.tiff"),
+                    mask_data,
+                )
 
 
 def extract_zip(zip_path, extract_to):
@@ -334,8 +407,15 @@ def extract_zip(zip_path, extract_to):
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Script to process the cell imaging datasets")
-    parser.add_argument("--dir", type=str, help="Directory of datasets to process it ...", default="/set/the/path")
+    parser = argparse.ArgumentParser(
+        description="Script to process the cell imaging datasets"
+    )
+    parser.add_argument(
+        "--dir",
+        type=str,
+        help="Directory of datasets to process it ...",
+        default="/set/the/path",
+    )
 
     args = parser.parse_args()
     data_root_path = os.path.normpath(args.dir)
@@ -365,7 +445,9 @@ def main():
             out_path = os.path.join(dataset_path)
             extract_zip(in_path, out_path)
 
-    print("If we reached here, that means all zip files got extracted ... Working on pre-processing")
+    print(
+        "If we reached here, that means all zip files got extracted ... Working on pre-processing"
+    )
 
     # Looping over all datasets again, Cellpose & Deepbacs have a similar directory structure
     for key, value in dataset_dict.items():
diff --git a/vista2d/license.code b/vista2d/license.code
index f49a4e1..261eeb9 100644
--- a/vista2d/license.code
+++ b/vista2d/license.code
@@ -198,4 +198,4 @@
    distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
-   limitations under the License.
\ No newline at end of file
+   limitations under the License.
diff --git a/vista2d/license.weights b/vista2d/license.weights
index 7cdbe0b..cbe5ad1 100644
--- a/vista2d/license.weights
+++ b/vista2d/license.weights
@@ -435,4 +435,3 @@ the avoidance of doubt, this paragraph does not form part of the
 public licenses.
 
 Creative Commons may be contacted at creativecommons.org.
-
diff --git a/vista2d/scripts/cell_distributed_weighted_sampler.py b/vista2d/scripts/cell_distributed_weighted_sampler.py
index 6e690b9..dabc179 100644
--- a/vista2d/scripts/cell_distributed_weighted_sampler.py
+++ b/vista2d/scripts/cell_distributed_weighted_sampler.py
@@ -35,13 +35,20 @@ def __init__(
         seed: int = 0,
         drop_last: bool = False,
     ) -> None:
-        if not isinstance(num_samples, int) or isinstance(num_samples, bool) or num_samples <= 0:
-            raise ValueError(f"num_samples should be a positive integer value, but got num_samples={num_samples}")
+        if (
+            not isinstance(num_samples, int)
+            or isinstance(num_samples, bool)
+            or num_samples <= 0
+        ):
+            raise ValueError(
+                f"num_samples should be a positive integer value, but got num_samples={num_samples}"
+            )
 
         weights_tensor = torch.as_tensor(weights, dtype=torch.float)
         if len(weights_tensor.shape) != 1:
             raise ValueError(
-                "weights should be a 1d sequence but given " f"weights have shape {tuple(weights_tensor.shape)}"
+                "weights should be a 1d sequence but given "
+                f"weights have shape {tuple(weights_tensor.shape)}"
             )
 
         self.weights = weights_tensor
@@ -56,7 +63,9 @@ def __init__(
                 raise RuntimeError("Requires distributed package to be available")
             rank = dist.get_rank()
         if rank >= num_replicas or rank < 0:
-            raise ValueError(f"Invalid rank {rank}, rank should be in the interval [0, {num_replicas - 1}]")
+            raise ValueError(
+                f"Invalid rank {rank}, rank should be in the interval [0, {num_replicas - 1}]"
+            )
         self.dataset = dataset
         self.num_replicas = num_replicas
         self.rank = rank
@@ -100,7 +109,9 @@ def __iter__(self) -> Iterator[T_co]:
                 if padding_size <= len(indices):
                     indices += indices[:padding_size]
                 else:
-                    indices += (indices * math.ceil(padding_size / len(indices)))[:padding_size]
+                    indices += (indices * math.ceil(padding_size / len(indices)))[
+                        :padding_size
+                    ]
             else:
                 # remove tail of data to make it evenly divisible.
                 indices = indices[: self.total_size]
diff --git a/vista2d/scripts/cell_sam_wrapper.py b/vista2d/scripts/cell_sam_wrapper.py
index a2f08be..8cbd8af 100644
--- a/vista2d/scripts/cell_sam_wrapper.py
+++ b/vista2d/scripts/cell_sam_wrapper.py
@@ -42,10 +42,20 @@ def __init__(
         model.mask_decoder = nn.Sequential(
             nn.BatchNorm2d(num_features=256),
             nn.ReLU(inplace=True),
-            nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, output_padding=1, bias=False),
+            nn.ConvTranspose2d(
+                256,
+                128,
+                kernel_size=3,
+                stride=2,
+                padding=1,
+                output_padding=1,
+                bias=False,
+            ),
             nn.BatchNorm2d(num_features=128),
             nn.ReLU(inplace=True),
-            nn.ConvTranspose2d(128, 3, kernel_size=3, stride=2, padding=1, output_padding=1, bias=True),
+            nn.ConvTranspose2d(
+                128, 3, kernel_size=3, stride=2, padding=1, output_padding=1, bias=True
+            ),
         )
 
         self.model = model
diff --git a/vista2d/scripts/components.py b/vista2d/scripts/components.py
index c59f640..139247b 100644
--- a/vista2d/scripts/components.py
+++ b/vista2d/scripts/components.py
@@ -41,16 +41,23 @@ def __call__(self, data):
                 img_array = tifffile.imread(filename)  # use tifffile for tif images
                 image_size = img_array.shape
                 if len(img_array.shape) == 3 and img_array.shape[-1] <= 3:
-                    img_array = np.transpose(img_array, (2, 0, 1))  # channels first without transpose
+                    img_array = np.transpose(
+                        img_array, (2, 0, 1)
+                    )  # channels first without transpose
             else:
-                img_array = np.array(PIL.Image.open(filename))  # PIL for all other images (png, jpeg)
+                img_array = np.array(
+                    PIL.Image.open(filename)
+                )  # PIL for all other images (png, jpeg)
                 image_size = img_array.shape
                 if len(img_array.shape) == 3:
                     img_array = np.transpose(img_array, (2, 0, 1))  # channels first
 
             if len(img_array.shape) not in [2, 3]:
                 raise ValueError(
-                    "Unsupported image dimensions, filename " + str(filename) + " shape " + str(img_array.shape)
+                    "Unsupported image dimensions, filename "
+                    + str(filename)
+                    + " shape "
+                    + str(img_array.shape)
                 )
 
             if len(img_array.shape) == 2:
@@ -65,7 +72,9 @@ def __call__(self, data):
 
             elif key == "image":
                 if img_array.shape[0] == 1:
-                    img_array = np.repeat(img_array, repeats=3, axis=0)  # if grayscale, repeat as 3 channels
+                    img_array = np.repeat(
+                        img_array, repeats=3, axis=0
+                    )  # if grayscale, repeat as 3 channels
                 elif img_array.shape[0] == 2:
                     print(
                         f"Strange case, image with 2 channels {filename} shape {img_array.shape}, appending first channel to make 3"
@@ -74,17 +83,24 @@ def __call__(self, data):
                         (img_array[0], img_array[1], img_array[0]), axis=0
                     )  # this should not happen, we got 2 channel input image
                 elif img_array.shape[0] > 3:
-                    print(f"Strange case, image with >3 channels,  {filename} shape {img_array.shape}, keeping first 3")
+                    print(
+                        f"Strange case, image with >3 channels,  {filename} shape {img_array.shape}, keeping first 3"
+                    )
                     img_array = img_array[:3]
 
-            meta_data = {ImageMetaKey.FILENAME_OR_OBJ: filename, ImageMetaKey.SPATIAL_SHAPE: image_size}
+            meta_data = {
+                ImageMetaKey.FILENAME_OR_OBJ: filename,
+                ImageMetaKey.SPATIAL_SHAPE: image_size,
+            }
             d[key] = MetaTensor.ensure_torch_and_prune_meta(img_array, meta_data)
 
         return d
 
 
 class SaveTiffd(MapTransform):
-    def __init__(self, output_dir, data_root_dir="/", nested_folder=False, *args, **kwargs) -> None:
+    def __init__(
+        self, output_dir, data_root_dir="/", nested_folder=False, *args, **kwargs
+    ) -> None:
         super().__init__(*args, **kwargs)
 
         self.output_dir = output_dir
@@ -124,7 +140,9 @@ def __call__(self, data):
 
             tifffile.imwrite(outname, label)
 
-            print(f"Saving {outname} shape {label.shape} max {label.max()} dtype {label.dtype}")
+            print(
+                f"Saving {outname} shape {label.shape} max {label.max()} dtype {label.dtype}"
+            )
 
         return d
 
@@ -146,7 +164,9 @@ def __call__(self, data):
             veci = masks_to_flows(label[0], device=None)
 
             flows = np.concatenate((label > 0.5, veci), axis=0).astype(np.float32)
-            flows = convert_to_dst_type(flows, d[key], dtype=torch.float, device=d[key].device)[0]
+            flows = convert_to_dst_type(
+                flows, d[key], dtype=torch.float, device=d[key].device
+            )[0]
             d[self.flow_key] = flows
             # meta_data = {ImageMetaKey.FILENAME_OR_OBJ : filename}
             # d[key] = MetaTensor.ensure_torch_and_prune_meta(img_array, meta_data)
@@ -171,7 +191,9 @@ def __call__(self, logits, filename=None):
                 device=device,
             )
         except RuntimeError as e:
-            logger.warning(f"compute_masks failed on GPU retrying on CPU {logits.shape} file {filename} {e}")
+            logger.warning(
+                f"compute_masks failed on GPU retrying on CPU {logits.shape} file {filename} {e}"
+            )
             pred_mask, p = compute_masks(
                 dP,
                 cellprob,
@@ -197,7 +219,15 @@ def __call__(self, data):
 
 
 class SaveTiffExd(MapTransform):
-    def __init__(self, output_dir, output_ext=".png", output_postfix="seg", image_key="image", *args, **kwargs) -> None:
+    def __init__(
+        self,
+        output_dir,
+        output_ext=".png",
+        output_postfix="seg",
+        image_key="image",
+        *args,
+        **kwargs,
+    ) -> None:
         super().__init__(*args, **kwargs)
 
         self.output_dir = output_dir
@@ -224,9 +254,15 @@ def __call__(self, data):
         os.makedirs(self.output_dir, exist_ok=True)
 
         img = d.get(self.image_key, None)
-        filename = img.meta.get(ImageMetaKey.FILENAME_OR_OBJ) if img is not None else None
-        image_size = img.meta.get(ImageMetaKey.SPATIAL_SHAPE) if img is not None else None
-        basename = os.path.splitext(os.path.basename(filename))[0] if filename else "mask"
+        filename = (
+            img.meta.get(ImageMetaKey.FILENAME_OR_OBJ) if img is not None else None
+        )
+        image_size = (
+            img.meta.get(ImageMetaKey.SPATIAL_SHAPE) if img is not None else None
+        )
+        basename = (
+            os.path.splitext(os.path.basename(filename))[0] if filename else "mask"
+        )
         logger.info(f"File: {filename}; Base: {basename}")
 
         for key in self.key_iterator(d):
@@ -256,13 +292,17 @@ def __call__(self, data):
                     m = np.zeros_like(mask)
                     m[mask == i] = 1
                     color = np.random.choice(range(256), size=3).tolist()
-                    contours, _ = cv2.findContours(m, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+                    contours, _ = cv2.findContours(
+                        m, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
+                    )
                     polygons.extend(self.to_polygons(contours))
                     cv2.drawContours(image, contours, -1, color, 1)
                 cv2.imwrite(output_filepath, image)
                 logger.info(f"Overlay Masks: Saving {output_filepath}")
             else:
-                contours, _ = cv2.findContours(label, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+                contours, _ = cv2.findContours(
+                    label, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE
+                )
                 polygons.extend(self.to_polygons(contours))
 
             meta_json = {"image_size": image_size, "contours": len(polygons)}
@@ -272,7 +312,9 @@ def __call__(self, data):
             if output_contours:
                 logger.info(f"Total Polygons: {len(polygons)}")
                 with open(os.path.join(output_dir, "contours.json"), "w") as fp:
-                    json.dump({"count": len(polygons), "contours": polygons}, fp, indent=2)
+                    json.dump(
+                        {"count": len(polygons), "contours": polygons}, fp, indent=2
+                    )
 
         return d
 
@@ -280,9 +322,9 @@ def __call__(self, data):
 # Loss (adopted from Cellpose)
 class CellLoss:
     def __call__(self, y_pred, y):
-        loss = 0.5 * F.mse_loss(y_pred[:, 1:], 5 * y[:, 1:]) + F.binary_cross_entropy_with_logits(
-            y_pred[:, [0]], y[:, [0]]
-        )
+        loss = 0.5 * F.mse_loss(
+            y_pred[:, 1:], 5 * y[:, 1:]
+        ) + F.binary_cross_entropy_with_logits(y_pred[:, [0]], y[:, [0]])
         return loss
 
 
diff --git a/vista2d/scripts/utils.py b/vista2d/scripts/utils.py
index 843bc49..81c55be 100644
--- a/vista2d/scripts/utils.py
+++ b/vista2d/scripts/utils.py
@@ -25,14 +25,20 @@
 
 np.set_printoptions(formatter={"float": "{: 0.3f}".format}, suppress=True)
 logging.getLogger("torch.nn.parallel.distributed").setLevel(logging.WARNING)
-warnings.filterwarnings("ignore", message=".*Divide by zero.*")  # intensity transform divide by zero warning
+warnings.filterwarnings(
+    "ignore", message=".*Divide by zero.*"
+)  # intensity transform divide by zero warning
 
 LOGGING_CONFIG = {
     "version": 1,
     "disable_existing_loggers": False,
     "formatters": {"monai_default": {"format": DEFAULT_FMT}},
     "loggers": {
-        "VistaCell": {"handlers": ["file", "console"], "level": "DEBUG", "propagate": False},
+        "VistaCell": {
+            "handlers": ["file", "console"],
+            "level": "DEBUG",
+            "propagate": False,
+        },
     },
     "filters": {"rank_filter": {"()": RankFilter}},
     "handlers": {
@@ -70,13 +76,17 @@ def parsing_bundle_config(config_file, logging_file=None, meta_file=None):
     else:
         config_root_path = Path("configs")
 
-    logging_file = str(config_root_path / "logging.conf") if logging_file is None else logging_file
+    logging_file = (
+        str(config_root_path / "logging.conf") if logging_file is None else logging_file
+    )
     if os.path.exists(logging_file):
         fileConfig(logging_file, disable_existing_loggers=False)
 
     parser = ConfigParser()
     parser.read_config(config_file)
-    meta_file = str(config_root_path / "metadata.json") if meta_file is None else meta_file
+    meta_file = (
+        str(config_root_path / "metadata.json") if meta_file is None else meta_file
+    )
     if isinstance(meta_file, str) and not os.path.exists(meta_file):
         logger.error(
             f"Cannot find the metadata config file: {meta_file}. "
diff --git a/vista2d/scripts/workflow.py b/vista2d/scripts/workflow.py
index 26fa402..46076ca 100644
--- a/vista2d/scripts/workflow.py
+++ b/vista2d/scripts/workflow.py
@@ -79,11 +79,15 @@ def __init__(
         the rest key-values in the `override` are to override config content.
         """
 
-        parser = parsing_bundle_config(config_file, logging_file=logging_file, meta_file=meta_file)
+        parser = parsing_bundle_config(
+            config_file, logging_file=logging_file, meta_file=meta_file
+        )
         parser.update(pairs=override)
 
         mode = parser.get("mode", None)
-        if mode is not None:  # if user specified a `mode` it'll override the workflow_type arg
+        if (
+            mode is not None
+        ):  # if user specified a `mode` it'll override the workflow_type arg
             workflow_type = mode
         else:
             mode = workflow_type  # if user didn't specify mode, the workflow_type will be used
@@ -98,7 +102,8 @@ def __init__(
 
         # check if torchrun or bcprun started it
         if dist.is_torchelastic_launched() or (
-            os.getenv("NGC_ARRAY_SIZE") is not None and int(os.getenv("NGC_ARRAY_SIZE")) > 1
+            os.getenv("NGC_ARRAY_SIZE") is not None
+            and int(os.getenv("NGC_ARRAY_SIZE")) > 1
         ):
             if dist.is_available():
                 dist.init_process_group(backend="nccl", init_method="env://")
@@ -111,7 +116,11 @@ def __init__(
         else:
             self.is_distributed = False
 
-        if self.global_rank == 0 and self.config("ckpt_path") and not os.path.exists(self.config("ckpt_path")):
+        if (
+            self.global_rank == 0
+            and self.config("ckpt_path")
+            and not os.path.exists(self.config("ckpt_path"))
+        ):
             os.makedirs(self.config("ckpt_path"), exist_ok=True)
 
         if self.rank == 0:
@@ -196,7 +205,9 @@ def initialize(self):
 
     def get_mode(self):
         mode_str = self.config("mode", self.workflow_type)
-        return look_up_option(mode_str, ("train", "training", "infer", "inference", "eval", "evaluation"))
+        return look_up_option(
+            mode_str, ("train", "training", "infer", "inference", "eval", "evaluation")
+        )
 
     def run(self):
         if str(self.mode).startswith("train"):
@@ -218,9 +229,13 @@ def get_network(self):
         pretrained_ckpt_path = self.config("pretrained_ckpt_path", None)
         if pretrained_ckpt_name is not None and pretrained_ckpt_path is None:
             # if relative name specified, append to default ckpt_path dir
-            pretrained_ckpt_path = os.path.join(self.config("ckpt_path"), pretrained_ckpt_name)
+            pretrained_ckpt_path = os.path.join(
+                self.config("ckpt_path"), pretrained_ckpt_name
+            )
 
-        if pretrained_ckpt_path is not None and not os.path.exists(pretrained_ckpt_path):
+        if pretrained_ckpt_path is not None and not os.path.exists(
+            pretrained_ckpt_path
+        ):
             logger.info(f"Pretrained checkpoint {pretrained_ckpt_path} not found.")
             raise ValueError(f"Pretrained checkpoint {pretrained_ckpt_path} not found.")
 
@@ -251,7 +266,9 @@ def get_network(self):
             )
 
         pytorch_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
-        logger.info(f"total parameters count {pytorch_params} distributed {self.is_distributed}")
+        logger.info(
+            f"total parameters count {pytorch_params} distributed {self.is_distributed}"
+        )
         return model
 
     def get_train_dataset_data(self):
@@ -274,10 +291,14 @@ def get_train_dataset_data(self):
 
         for idx, d in enumerate(data_list_files):
             logger.info(f"adding datalist ({idx}): {d['datalist']}")
-            t, v = datafold_read(datalist=d["datalist"], basedir=d["basedir"], fold=self.config("fold"))
+            t, v = datafold_read(
+                datalist=d["datalist"], basedir=d["basedir"], fold=self.config("fold")
+            )
 
             if val_key is not None:
-                v, _ = datafold_read(datalist=d["datalist"], basedir=d["basedir"], fold=-1, key=val_key)  # e.g. testing
+                v, _ = datafold_read(
+                    datalist=d["datalist"], basedir=d["basedir"], fold=-1, key=val_key
+                )  # e.g. testing
 
             for item in t:
                 item["datalist_id"] = idx
@@ -296,7 +317,9 @@ def get_train_dataset_data(self):
             logger.warning("No validation data found.")
         return train_files, valid_files
 
-    def read_val_datalists(self, section="validate", data_list_files=None, val_key=None, merge=True):
+    def read_val_datalists(
+        self, section="validate", data_list_files=None, val_key=None, merge=True
+    ):
         """read the corresponding folds of the datalist for validation or inference"""
         dataset_data = self.config(f"{section}#dataset#data")
 
@@ -319,10 +342,18 @@ def read_val_datalists(self, section="validate", data_list_files=None, val_key=N
         val_files, idx = [], 0
         for d in data_list_files:
             if val_key is not None:
-                v_files, _ = datafold_read(datalist=d["datalist"], basedir=d["basedir"], fold=-1, key=val_key)
+                v_files, _ = datafold_read(
+                    datalist=d["datalist"], basedir=d["basedir"], fold=-1, key=val_key
+                )
             else:
-                _, v_files = datafold_read(datalist=d["datalist"], basedir=d["basedir"], fold=self.config("fold"))
-            logger.info(f"adding datalist ({idx} -- {val_key}): {d['datalist']} {len(v_files)}")
+                _, v_files = datafold_read(
+                    datalist=d["datalist"],
+                    basedir=d["basedir"],
+                    fold=self.config("fold"),
+                )
+            logger.info(
+                f"adding datalist ({idx} -- {val_key}): {d['datalist']} {len(v_files)}"
+            )
             if merge:
                 val_files.extend(v_files)
             else:
@@ -339,17 +370,33 @@ def get_train_preprocessing(self):
 
         train_xforms = []
         train_xforms.append(LoadTiffd(keys=["image", "label"]))
-        train_xforms.append(mt.EnsureTyped(keys=["image", "label"], data_type="tensor", dtype=torch.float))
+        train_xforms.append(
+            mt.EnsureTyped(
+                keys=["image", "label"], data_type="tensor", dtype=torch.float
+            )
+        )
         if self.config("prescale", True):
             print("Prescaling images to 0..1")
-            train_xforms.append(mt.ScaleIntensityd(keys="image", minv=0, maxv=1, channel_wise=True))
-        train_xforms.append(mt.ScaleIntensityd(keys="image", minv=0, maxv=1, channel_wise=True))
+            train_xforms.append(
+                mt.ScaleIntensityd(keys="image", minv=0, maxv=1, channel_wise=True)
+            )
+        train_xforms.append(
+            mt.ScaleIntensityd(keys="image", minv=0, maxv=1, channel_wise=True)
+        )
         train_xforms.append(
             mt.ScaleIntensityRangePercentilesd(
-                keys="image", lower=1, upper=99, b_min=0.0, b_max=1.0, channel_wise=True, clip=True
+                keys="image",
+                lower=1,
+                upper=99,
+                b_min=0.0,
+                b_max=1.0,
+                channel_wise=True,
+                clip=True,
             )
         )
-        train_xforms.append(mt.SpatialPadd(keys=["image", "label"], spatial_size=roi_size))
+        train_xforms.append(
+            mt.SpatialPadd(keys=["image", "label"], spatial_size=roi_size)
+        )
         train_xforms.append(
             mt.RandSpatialCropd(keys=["image", "label"], roi_size=roi_size)
         )  # crop roi_size (if image is large)
@@ -386,19 +433,34 @@ def get_val_preprocessing(self):
         val_xforms = []
         val_xforms.append(LoadTiffd(keys=["image", "label"], allow_missing_keys=True))
         val_xforms.append(
-            mt.EnsureTyped(keys=["image", "label"], data_type="tensor", dtype=torch.float, allow_missing_keys=True)
+            mt.EnsureTyped(
+                keys=["image", "label"],
+                data_type="tensor",
+                dtype=torch.float,
+                allow_missing_keys=True,
+            )
         )
 
         if self.config("prescale", True):
             print("Prescaling val images to 0..1")
-            val_xforms.append(mt.ScaleIntensityd(keys="image", minv=0, maxv=1, channel_wise=True))
+            val_xforms.append(
+                mt.ScaleIntensityd(keys="image", minv=0, maxv=1, channel_wise=True)
+            )
 
         val_xforms.append(
             mt.ScaleIntensityRangePercentilesd(
-                keys="image", lower=1, upper=99, b_min=0.0, b_max=1.0, channel_wise=True, clip=True
+                keys="image",
+                lower=1,
+                upper=99,
+                b_min=0.0,
+                b_max=1.0,
+                channel_wise=True,
+                clip=True,
             )
         )
-        val_xforms.append(LabelsToFlows(keys="label", flow_key="flow", allow_missing_keys=True))
+        val_xforms.append(
+            LabelsToFlows(keys="label", flow_key="flow", allow_missing_keys=True)
+        )
 
         return val_xforms
 
@@ -445,9 +507,14 @@ def get_train_sampler(self):
 
             if self.is_distributed:
                 return DistributedWeightedSampler(
-                    self.train_dataset, shuffle=True, weights=sample_weights, num_samples=num_samples_per_epoch
+                    self.train_dataset,
+                    shuffle=True,
+                    weights=sample_weights,
+                    num_samples=num_samples_per_epoch,
                 )  # custom implementation, as Pytorch does not have one
-            return WeightedRandomSampler(weights=sample_weights, num_samples=num_samples_per_epoch)
+            return WeightedRandomSampler(
+                weights=sample_weights, num_samples=num_samples_per_epoch
+            )
 
         if self.is_distributed:
             return DistributedSampler(self.train_dataset, shuffle=True)
@@ -486,9 +553,11 @@ def train(self):
         sliding_inferrer = config("inferer#sliding_inferer")
         use_amp = config("amp")
 
-        amp_dtype = {"float32": torch.float32, "bfloat16": torch.bfloat16, "float16": torch.float16}[
-            config("amp_dtype")
-        ]
+        amp_dtype = {
+            "float32": torch.float32,
+            "bfloat16": torch.bfloat16,
+            "float16": torch.float16,
+        }[config("amp_dtype")]
         if amp_dtype == torch.bfloat16 and not torch.cuda.is_bf16_supported():
             amp_dtype = torch.float16
             logger.warning(
@@ -496,8 +565,12 @@ def train(self):
             )
 
         use_gradscaler = use_amp and amp_dtype == torch.float16
-        logger.info(f"Using grad scaler {use_gradscaler} amp_dtype {amp_dtype} use_amp {use_amp}")
-        grad_scaler = GradScaler(enabled=use_gradscaler)  # using GradScaler only for AMP float16 (not bfloat16)
+        logger.info(
+            f"Using grad scaler {use_gradscaler} amp_dtype {amp_dtype} use_amp {use_amp}"
+        )
+        grad_scaler = GradScaler(
+            enabled=use_gradscaler
+        )  # using GradScaler only for AMP float16 (not bfloat16)
 
         loss_function = config("loss_function")
         acc_function = config("key_metric")
@@ -533,21 +606,38 @@ def train(self):
                     mlflow.set_tracking_uri(config("mlflow_tracking_uri"))
                     mlflow.set_experiment("vista2d")
 
-                    mlflow_run_name = config("mlflow_run_name", f'vista2d train fold{config("fold")}')
+                    mlflow_run_name = config(
+                        "mlflow_run_name", f'vista2d train fold{config("fold")}'
+                    )
                     mlflow.start_run(
-                        run_name=mlflow_run_name, log_system_metrics=config("mlflow_log_system_metrics", False)
+                        run_name=mlflow_run_name,
+                        log_system_metrics=config("mlflow_log_system_metrics", False),
                     )
                     mlflow.log_params(self.parser.config)
-                    mlflow.log_dict(self.parser.config, "hyper_parameters.yaml")  # experimental
+                    mlflow.log_dict(
+                        self.parser.config, "hyper_parameters.yaml"
+                    )  # experimental
 
             csv_path = os.path.join(ckpt_path, "accuracy_history.csv")
             self.save_history_csv(
                 csv_path=csv_path,
-                header=["epoch", "metric", "loss", "iter", "time", "train_time", "validation_time", "epoch_time"],
+                header=[
+                    "epoch",
+                    "metric",
+                    "loss",
+                    "iter",
+                    "time",
+                    "train_time",
+                    "validation_time",
+                    "epoch_time",
+                ],
             )
 
         do_torch_save = (
-            (self.global_rank == 0) and ckpt_path and config("ckpt_save") and not config("train#skip", False)
+            (self.global_rank == 0)
+            and ckpt_path
+            and config("ckpt_save")
+            and not config("train#skip", False)
         )
         best_ckpt_path = os.path.join(ckpt_path, "model.pt")
         intermediate_ckpt_path = os.path.join(ckpt_path, "model_final.pt")
@@ -582,7 +672,12 @@ def train(self):
         if distributed:
             dist.barrier()
 
-        if self.global_rank == 0 and tb_writer is not None and mlflow_is_imported and mlflow.is_tracking_uri_set():
+        if (
+            self.global_rank == 0
+            and tb_writer is not None
+            and mlflow_is_imported
+            and mlflow.is_tracking_uri_set()
+        ):
             mlflow.log_param("len_train_set", len(train_loader.dataset))
             mlflow.log_param("len_val_set", len(val_loader.dataset))
 
@@ -675,8 +770,12 @@ def train(self):
                         tb_writer.add_scalar("val/acc", val_acc_mean, report_epoch)
                         tb_writer.add_scalar("val/loss", val_loss, report_epoch)
                         if mlflow_is_imported and mlflow.is_tracking_uri_set():
-                            mlflow.log_metric("val/acc", val_acc_mean, step=report_epoch)
-                            mlflow.log_metric("val/epoch_time", validation_time, step=report_epoch)
+                            mlflow.log_metric(
+                                "val/acc", val_acc_mean, step=report_epoch
+                            )
+                            mlflow.log_metric(
+                                "val/epoch_time", validation_time, step=report_epoch
+                            )
 
                     timing_dict = {
                         "time": f"{(time.time() - pre_loop_time) / 3600:.2f} hr",
@@ -686,12 +785,17 @@ def train(self):
                     }
 
                     if val_acc_mean > best_metric:
-                        logger.info(f"New best metric ({best_metric:.6f} --> {val_acc_mean:.6f}). ")
+                        logger.info(
+                            f"New best metric ({best_metric:.6f} --> {val_acc_mean:.6f}). "
+                        )
                         best_metric, best_metric_epoch = val_acc_mean, report_epoch
                         save_time = 0
                         if do_torch_save:
                             save_time = self.checkpoint_save(
-                                ckpt=best_ckpt_path, model=model, epoch=best_metric_epoch, best_metric=best_metric
+                                ckpt=best_ckpt_path,
+                                model=model,
+                                epoch=best_metric_epoch,
+                                best_metric=best_metric,
                             )
 
                         if progress_path is not None:
@@ -714,7 +818,11 @@ def train(self):
                         )
 
                 # sanity check
-                if epoch > max(20, num_epochs / 4) and 0 <= val_acc_mean < 0.01 and config("stop_on_lowacc", True):
+                if (
+                    epoch > max(20, num_epochs / 4)
+                    and 0 <= val_acc_mean < 0.01
+                    and config("stop_on_lowacc", True)
+                ):
                     logger.info(
                         f"Accuracy seems very low at epoch {report_epoch}, acc {val_acc_mean}. "
                         "Most likely optimization diverged, try setting a smaller learning_rate"
@@ -727,16 +835,25 @@ def train(self):
                     )
 
             # save intermediate checkpoint every num_epochs_per_saving epochs
-            if do_torch_save and ((epoch + 1) % num_epochs_per_saving == 0 or (epoch + 1) >= num_epochs):
+            if do_torch_save and (
+                (epoch + 1) % num_epochs_per_saving == 0 or (epoch + 1) >= num_epochs
+            ):
                 if report_epoch != best_metric_epoch:
                     self.checkpoint_save(
-                        ckpt=intermediate_ckpt_path, model=model, epoch=report_epoch, best_metric=val_acc_mean
+                        ckpt=intermediate_ckpt_path,
+                        model=model,
+                        epoch=report_epoch,
+                        best_metric=val_acc_mean,
                     )
                 else:
                     try:
-                        shutil.copyfile(best_ckpt_path, intermediate_ckpt_path)  # if already saved once
+                        shutil.copyfile(
+                            best_ckpt_path, intermediate_ckpt_path
+                        )  # if already saved once
                     except Exception as err:
-                        logger.warning(f"error copying {best_ckpt_path} {intermediate_ckpt_path} {err}")
+                        logger.warning(
+                            f"error copying {best_ckpt_path} {intermediate_ckpt_path} {err}"
+                        )
                         pass
 
             if lr_scheduler is not None:
@@ -765,9 +882,15 @@ def train(self):
         if config("run_final_testing"):
             if distributed:
                 dist.barrier()
-            _ckpt_name = best_ckpt_path if os.path.exists(best_ckpt_path) else intermediate_ckpt_path
+            _ckpt_name = (
+                best_ckpt_path
+                if os.path.exists(best_ckpt_path)
+                else intermediate_ckpt_path
+            )
             if not os.path.exists(_ckpt_name):
-                logger.info(f"Unable to validate final no checkpoints found {best_ckpt_path}, {intermediate_ckpt_path}")
+                logger.info(
+                    f"Unable to validate final no checkpoints found {best_ckpt_path}, {intermediate_ckpt_path}"
+                )
             else:
                 # self._props.pop("network", None)
                 # self._set_props.pop("network", None)
@@ -802,7 +925,9 @@ def train(self):
         )
         return best_metric
 
-    def run_final_testing(self, pretrained_ckpt_path, progress_path, best_metric_epoch, pre_loop_time):
+    def run_final_testing(
+        self, pretrained_ckpt_path, progress_path, best_metric_epoch, pre_loop_time
+    ):
         logger.info("Running final best model testing set!")
 
         # validate
@@ -815,7 +940,9 @@ def run_final_testing(self, pretrained_ckpt_path, progress_path, best_metric_epo
         val_acc_mean, val_loss, val_acc = self.validate(val_key="testing")
         validation_time = f"{time.time() - start_time:.2f}s"
         val_acc_mean = float(np.mean(val_acc))
-        logger.info(f"Testing: loss: {val_loss:.4f} acc_avg: {val_acc_mean:.4f} acc {val_acc} time {validation_time}")
+        logger.info(
+            f"Testing: loss: {val_loss:.4f} acc_avg: {val_acc_mean:.4f} acc {val_acc} time {validation_time}"
+        )
 
         if self.global_rank == 0 and progress_path is not None:
             self.save_progress_yaml(
@@ -830,24 +957,35 @@ def run_final_testing(self, pretrained_ckpt_path, progress_path, best_metric_epo
         return val_acc_mean
 
     def validate(self, validation_files=None, val_key=None, datalist=None):
-        if self.config("pretrained_ckpt_name", None) is None and self.config("pretrained_ckpt_path", None) is None:
+        if (
+            self.config("pretrained_ckpt_name", None) is None
+            and self.config("pretrained_ckpt_path", None) is None
+        ):
             self.parser["pretrained_ckpt_name"] = "model.pt"
             logger.info("Using default model.pt checkpoint for validation.")
 
-        grouping = self.config("validate#grouping", False)  # whether to computer average per datalist
+        grouping = self.config(
+            "validate#grouping", False
+        )  # whether to computer average per datalist
         if validation_files is None:
-            validation_files = self.read_val_datalists("validate", datalist, val_key=val_key, merge=not grouping)
+            validation_files = self.read_val_datalists(
+                "validate", datalist, val_key=val_key, merge=not grouping
+            )
         if len(validation_files) == 0:
             logger.warning(f"No validation files found {datalist} {val_key}!")
             return 0, 0, 0
         if not grouping or not isinstance(validation_files[0], (list, tuple)):
             validation_files = [validation_files]
-        logger.info(f"validation file groups {len(validation_files)} grouping {grouping}")
+        logger.info(
+            f"validation file groups {len(validation_files)} grouping {grouping}"
+        )
         val_acc_dict = {}
 
-        amp_dtype = {"float32": torch.float32, "bfloat16": torch.bfloat16, "float16": torch.float16}[
-            self.config("amp_dtype")
-        ]
+        amp_dtype = {
+            "float32": torch.float32,
+            "bfloat16": torch.bfloat16,
+            "float16": torch.float16,
+        }[self.config("amp_dtype")]
         if amp_dtype == torch.bfloat16 and not torch.cuda.is_bf16_supported():
             amp_dtype = torch.float16
             logger.warning(
@@ -886,12 +1024,17 @@ def validate(self, validation_files=None, val_key=None, datalist=None):
         return val_acc_mean, val_loss, val_acc
 
     def infer(self, infer_files=None, infer_key=None, datalist=None):
-        if self.config("pretrained_ckpt_name", None) is None and self.config("pretrained_ckpt_path", None) is None:
+        if (
+            self.config("pretrained_ckpt_name", None) is None
+            and self.config("pretrained_ckpt_path", None) is None
+        ):
             self.parser["pretrained_ckpt_name"] = "model.pt"
             logger.info("Using default model.pt checkpoint for inference.")
 
         if infer_files is None:
-            infer_files = self.read_val_datalists("infer", datalist, val_key=infer_key, merge=True)
+            infer_files = self.read_val_datalists(
+                "infer", datalist, val_key=infer_key, merge=True
+            )
         if len(infer_files) == 0:
             logger.warning(f"no file to infer {datalist} {infer_key}.")
             return
@@ -899,9 +1042,11 @@ def infer(self, infer_files=None, infer_key=None, datalist=None):
         self.set_val_datalist(infer_files)
         val_loader = self.val_loader
 
-        amp_dtype = {"float32": torch.float32, "bfloat16": torch.bfloat16, "float16": torch.float16}[
-            self.config("amp_dtype")
-        ]
+        amp_dtype = {
+            "float32": torch.float32,
+            "bfloat16": torch.bfloat16,
+            "float16": torch.float16,
+        }[self.config("amp_dtype")]
         if amp_dtype == torch.bfloat16 and not torch.cuda.is_bf16_supported():
             amp_dtype = torch.bfloat16
             logger.warning(
@@ -961,11 +1106,21 @@ def val_epoch(
         # hence let's compute a proper subset length
         nonrepeated_data_length = len(val_loader.dataset)
         sampler = val_loader.sampler
-        if distributed and isinstance(sampler, DistributedSampler) and not sampler.drop_last:
-            nonrepeated_data_length = len(range(sampler.rank, len(sampler.dataset), sampler.num_replicas))
+        if (
+            distributed
+            and isinstance(sampler, DistributedSampler)
+            and not sampler.drop_last
+        ):
+            nonrepeated_data_length = len(
+                range(sampler.rank, len(sampler.dataset), sampler.num_replicas)
+            )
 
         for idx, batch_data in enumerate(val_loader):
-            data = batch_data["image"].as_subclass(torch.Tensor).to(memory_format=memory_format, device=device)
+            data = (
+                batch_data["image"]
+                .as_subclass(torch.Tensor)
+                .to(memory_format=memory_format, device=device)
+            )
             filename = batch_data["image"].meta[ImageMetaKey.FILENAME_OR_OBJ]
             batch_size = data.shape[0]
             loss = acc = None
@@ -976,7 +1131,11 @@ def val_epoch(
 
             # calc loss
             if loss_function is not None:
-                target = batch_data["flow"].as_subclass(torch.Tensor).to(device=logits.device)
+                target = (
+                    batch_data["flow"]
+                    .as_subclass(torch.Tensor)
+                    .to(device=logits.device)
+                )
                 loss = loss_function(logits, target)
                 run_loss.append(loss.to(device=device), count=batch_size)
                 target = None
@@ -992,7 +1151,11 @@ def val_epoch(
 
                 for b_ind in range(label.shape[0]):
                     acc = acc_function(pred_mask_all[b_ind], label[b_ind, 0].long())
-                    acc = acc.detach().clone() if isinstance(acc, torch.Tensor) else torch.tensor(acc)
+                    acc = (
+                        acc.detach().clone()
+                        if isinstance(acc, torch.Tensor)
+                        else torch.tensor(acc)
+                    )
 
                     if idx < nonrepeated_data_length:
                         run_acc.append(acc.to(device=device), count=1)
@@ -1009,9 +1172,14 @@ def val_epoch(
             )
 
             if post_transforms:
-                seg = torch.from_numpy(np.stack(pred_mask_all, axis=0).astype(np.int32)).unsqueeze(1)
+                seg = torch.from_numpy(
+                    np.stack(pred_mask_all, axis=0).astype(np.int32)
+                ).unsqueeze(1)
                 batch_data["seg"] = convert_to_dst_type(
-                    seg, batch_data["image"], dtype=torch.int32, device=torch.device("cpu")
+                    seg,
+                    batch_data["image"],
+                    dtype=torch.int32,
+                    device=torch.device("cpu"),
                 )[0]
                 for bd in decollate_batch(batch_data):
                     post_transforms(bd)  # (currently only to save output mask)
@@ -1028,7 +1196,9 @@ def val_epoch(
 
         if np.any(avg_acc < 0):
             dist.barrier()
-            logger.warning(f"Avg accuracy is negative ({avg_acc}), something went wrong!!!!!")
+            logger.warning(
+                f"Avg accuracy is negative ({avg_acc}), something went wrong!!!!!"
+            )
 
         return avg_loss, avg_acc
 
@@ -1057,8 +1227,16 @@ def train_epoch(
         start_time = time.time()
         avg_loss = avg_acc = 0
         for idx, batch_data in enumerate(train_loader):
-            data = batch_data["image"].as_subclass(torch.Tensor).to(memory_format=memory_format, device=device)
-            target = batch_data["flow"].as_subclass(torch.Tensor).to(memory_format=memory_format, device=device)
+            data = (
+                batch_data["image"]
+                .as_subclass(torch.Tensor)
+                .to(memory_format=memory_format, device=device)
+            )
+            target = (
+                batch_data["flow"]
+                .as_subclass(torch.Tensor)
+                .to(memory_format=memory_format, device=device)
+            )
 
             optimizer.zero_grad(set_to_none=True)
 
@@ -1110,10 +1288,19 @@ def save_progress_yaml(self, progress_path=None, ckpt=None, **report):
 
         if progress_path is not None:
             yaml.add_representer(
-                float, lambda dumper, value: dumper.represent_scalar("tag:yaml.org,2002:float", f"{value:.4f}")
+                float,
+                lambda dumper, value: dumper.represent_scalar(
+                    "tag:yaml.org,2002:float", f"{value:.4f}"
+                ),
             )
             with open(progress_path, "a") as progress_file:
-                yaml.dump([report], stream=progress_file, allow_unicode=True, default_flow_style=None, sort_keys=False)
+                yaml.dump(
+                    [report],
+                    stream=progress_file,
+                    allow_unicode=True,
+                    default_flow_style=None,
+                    sort_keys=False,
+                )
 
         logger.info("Progress:" + ",".join(f" {k}: {v}" for k, v in report.items()))
 
@@ -1128,13 +1315,18 @@ def checkpoint_save(self, ckpt: str, model: torch.nn.Module, **kwargs):
         if self.config("compile", False):
             # remove key prefix of compiled models
             state_dict = OrderedDict(
-                (k[len("_orig_mod.") :] if k.startswith("_orig_mod.") else k, v) for k, v in state_dict.items()
+                (k[len("_orig_mod.") :] if k.startswith("_orig_mod.") else k, v)
+                for k, v in state_dict.items()
             )
 
-        torch.save({"state_dict": state_dict, "config": self.parser.config, **kwargs}, ckpt)
+        torch.save(
+            {"state_dict": state_dict, "config": self.parser.config, **kwargs}, ckpt
+        )
 
         save_time = time.time() - save_time
-        logger.info(f"Saving checkpoint process: {ckpt}, {kwargs}, save_time {save_time:.2f}s")
+        logger.info(
+            f"Saving checkpoint process: {ckpt}, {kwargs}, save_time {save_time:.2f}s"
+        )
 
         return save_time
 
@@ -1165,7 +1357,9 @@ def checkpoint_load(self, ckpt: str, model: torch.nn.Module, **kwargs):
         self.parser["start_epoch"] = int(self.config("start_epoch")) + 1
         return
 
-    def schedule_validation_epochs(self, num_epochs, num_epochs_per_validation=None, fraction=0.16) -> list:
+    def schedule_validation_epochs(
+        self, num_epochs, num_epochs_per_validation=None, fraction=0.16
+    ) -> list:
         """
         Schedule of epochs to validate (progressively more frequently)
             num_epochs - total number of epochs
@@ -1174,7 +1368,10 @@ def schedule_validation_epochs(self, num_epochs, num_epochs_per_validation=None,
         """
 
         if num_epochs_per_validation is None:
-            x = (np.sin(np.linspace(0, np.pi / 2, max(10, int(fraction * num_epochs)))) * num_epochs).astype(int)
+            x = (
+                np.sin(np.linspace(0, np.pi / 2, max(10, int(fraction * num_epochs))))
+                * num_epochs
+            ).astype(int)
             x = np.cumsum(np.sort(np.diff(np.unique(x)))[::-1])
             x[-1] = num_epochs
             x = x.tolist()
@@ -1182,7 +1379,11 @@ def schedule_validation_epochs(self, num_epochs, num_epochs_per_validation=None,
             if num_epochs_per_validation >= num_epochs:
                 x = [num_epochs_per_validation]
             else:
-                x = list(range(num_epochs_per_validation, num_epochs, num_epochs_per_validation))
+                x = list(
+                    range(
+                        num_epochs_per_validation, num_epochs, num_epochs_per_validation
+                    )
+                )
 
         if len(x) == 0:
             x = [0]
diff --git a/vista2d/unit_tests/test_vista2d.py b/vista2d/unit_tests/test_vista2d.py
index 8d3c39b..b32e931 100644
--- a/vista2d/unit_tests/test_vista2d.py
+++ b/vista2d/unit_tests/test_vista2d.py
@@ -45,8 +45,12 @@ def setUp(self):
         self.dataset_size = 5
         input_shape = (256, 256)
         for s in range(self.dataset_size):
-            test_image = np.random.randint(low=0, high=2, size=input_shape).astype(np.int8)
-            test_label = np.random.randint(low=0, high=2, size=input_shape).astype(np.int8)
+            test_image = np.random.randint(low=0, high=2, size=input_shape).astype(
+                np.int8
+            )
+            test_label = np.random.randint(low=0, high=2, size=input_shape).astype(
+                np.int8
+            )
             image_filename = os.path.join(self.dataset_dir, f"image_{s}.png")
             label_filename = os.path.join(self.dataset_dir, f"label_{s}.png")
             plt.imsave(image_filename, test_image, cmap="gray")
@@ -82,7 +86,9 @@ def test_infer_config(self, override):
         # check_properties=False, need to add monai service properties later
         check_workflow(workflow, check_properties=False)
 
-        expected_output_file = os.path.join(self.tmp_output_dir, f"image_{self.dataset_size-1}.tif")
+        expected_output_file = os.path.join(
+            self.tmp_output_dir, f"image_{self.dataset_size-1}.tif"
+        )
         self.assertTrue(os.path.isfile(expected_output_file))
 
     @parameterized.expand([TEST_CASE_TRAIN])
diff --git a/vista2d/unit_tests/test_vista2d_mgpu.py b/vista2d/unit_tests/test_vista2d_mgpu.py
index af27f2f..850f206 100644
--- a/vista2d/unit_tests/test_vista2d_mgpu.py
+++ b/vista2d/unit_tests/test_vista2d_mgpu.py
@@ -36,8 +36,12 @@ def setUp(self):
         self.dataset_size = 5
         input_shape = (256, 256)
         for s in range(self.dataset_size):
-            test_image = np.random.randint(low=0, high=2, size=input_shape).astype(np.int8)
-            test_label = np.random.randint(low=0, high=2, size=input_shape).astype(np.int8)
+            test_image = np.random.randint(low=0, high=2, size=input_shape).astype(
+                np.int8
+            )
+            test_label = np.random.randint(low=0, high=2, size=input_shape).astype(
+                np.int8
+            )
             image_filename = os.path.join(self.dataset_dir, f"image_{s}.png")
             label_filename = os.path.join(self.dataset_dir, f"label_{s}.png")
             plt.imsave(image_filename, test_image, cmap="gray")
diff --git a/vista2d/unit_tests/utils.py b/vista2d/unit_tests/utils.py
index 67ecf5e..cf74e7a 100644
--- a/vista2d/unit_tests/utils.py
+++ b/vista2d/unit_tests/utils.py
@@ -59,7 +59,9 @@ def export_config_and_run_mgpu_cmd(
     step 3: produce multi-gpu running command
     step 4: run produced command
     """
-    export_overrided_config(config_file=config_file, override_dict=override_dict, output_path=output_path)
+    export_overrided_config(
+        config_file=config_file, override_dict=override_dict, output_path=output_path
+    )
     if check_config is True:
         workflow = create_workflow(
             workflow_name=workflow_name,
@@ -69,8 +71,12 @@ def export_config_and_run_mgpu_cmd(
         )
         check_result = workflow.check_properties()
         if check_result is not None and len(check_result) > 0:
-            raise ValueError(f"check properties for overrided mgpu configs failed: {check_result}")
-    cmd = produce_mgpu_cmd(config_file=output_path, meta_file=meta_file, nnodes=nnode, nproc_per_node=ngpu)
+            raise ValueError(
+                f"check properties for overrided mgpu configs failed: {check_result}"
+            )
+    cmd = produce_mgpu_cmd(
+        config_file=output_path, meta_file=meta_file, nnodes=nnode, nproc_per_node=ngpu
+    )
     env = os.environ.copy()
     # ensure customized library can be loaded in subprocess
     env["PYTHONPATH"] = override_dict.get("bundle_root", ".")

From 29b4f3684265a0ffaad892dbb696e74477f31af7 Mon Sep 17 00:00:00 2001
From: am <am>
Date: Thu, 1 Aug 2024 18:04:04 -0700
Subject: [PATCH 3/4] syntax

Signed-off-by: am <am>
---
 vista2d/download_preprocessor/generate_json.py |  2 +-
 vista2d/download_preprocessor/process_data.py  | 10 +++-------
 2 files changed, 4 insertions(+), 8 deletions(-)

diff --git a/vista2d/download_preprocessor/generate_json.py b/vista2d/download_preprocessor/generate_json.py
index 8aacde2..ecf8b1d 100644
--- a/vista2d/download_preprocessor/generate_json.py
+++ b/vista2d/download_preprocessor/generate_json.py
@@ -1103,7 +1103,7 @@ def main():
             try:
                 if os.path.exists(in_path):
                     print(f"File exists at: {in_path}")
-            except:
+            except Exception as err:
                 print(f"File: {in_path} was not found")
             out_path = os.path.join(dataset_path)
             extract_zip(in_path, out_path)
diff --git a/vista2d/download_preprocessor/process_data.py b/vista2d/download_preprocessor/process_data.py
index ae216ff..a9567ef 100644
--- a/vista2d/download_preprocessor/process_data.py
+++ b/vista2d/download_preprocessor/process_data.py
@@ -212,12 +212,11 @@ def livecell_process_files(dataset_dir):
                 img = Image.open(os.path.join(img_path, im["file_name"])).convert("L")
                 height.append(img.size[0])
                 width.append(img.size[1])
-                # arr = np.asarray(img) #? not used
-                # msk = np.zeros(arr.shape)
+
                 # load and display instance annotations
                 annIds = annotation.getAnnIds(imgIds=im["id"], iscrowd=None)
                 anns = annotation.loadAnns(annIds)
-                idx = 1
+
                 medians = []
                 masks = []
                 k = 0
@@ -235,9 +234,6 @@ def livecell_process_files(dataset_dir):
                         )
                     )
                     k += 1
-                    # add instance mask to image mask
-                    # msk = np.add(msk, mask*idx)
-                    # idx += 1
 
                 masks = np.array(masks).astype(np.int8)
                 medians = np.array(medians)
@@ -440,7 +436,7 @@ def main():
             try:
                 if os.path.exists(in_path):
                     print(f"File exists at: {in_path}")
-            except:
+            except Exception as err:
                 print(f"File: {in_path} was not found")
             out_path = os.path.join(dataset_path)
             extract_zip(in_path, out_path)

From b27307a01badcf7245d32bb53fdaeb79b0d1c27e Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 2 Aug 2024 01:05:49 +0000
Subject: [PATCH 4/4] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 vista2d/download_preprocessor/generate_json.py | 2 +-
 vista2d/download_preprocessor/process_data.py  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/vista2d/download_preprocessor/generate_json.py b/vista2d/download_preprocessor/generate_json.py
index ecf8b1d..0453072 100644
--- a/vista2d/download_preprocessor/generate_json.py
+++ b/vista2d/download_preprocessor/generate_json.py
@@ -1103,7 +1103,7 @@ def main():
             try:
                 if os.path.exists(in_path):
                     print(f"File exists at: {in_path}")
-            except Exception as err:
+            except Exception:
                 print(f"File: {in_path} was not found")
             out_path = os.path.join(dataset_path)
             extract_zip(in_path, out_path)
diff --git a/vista2d/download_preprocessor/process_data.py b/vista2d/download_preprocessor/process_data.py
index a9567ef..856dfc2 100644
--- a/vista2d/download_preprocessor/process_data.py
+++ b/vista2d/download_preprocessor/process_data.py
@@ -436,7 +436,7 @@ def main():
             try:
                 if os.path.exists(in_path):
                     print(f"File exists at: {in_path}")
-            except Exception as err:
+            except Exception:
                 print(f"File: {in_path} was not found")
             out_path = os.path.join(dataset_path)
             extract_zip(in_path, out_path)