hloc.yaml

server:
  host: "0.0.0.0"
  port: 5000

location:
  place: New_York_University
  building: Langone
  floor: "16_floor@dog"
  scale: 0.02285770402

IO_root: "/mnt/data/UNav-IO"

devices: "cuda:0"

hloc:
  retrieval_num: 100
  implicit_num: 1
  ransac_thre: 10
  # match_type: 'nvs'
  match_type: "lightglue"
  batch_mode: true
  load_all_maps: true
  map_loading_keyframes_reload: 0

feature:
  global:
    MixVPR:
      use: false
      ckpt_path: 'parameters/MixVPR/ckpts/resnet50_MixVPR_4096_channels(1024)_rows(4).ckpt'
      pt_img_size: [320, 320]    # The input image resolution for MixVPR's pretrained weights
      cuda: true
    AnyLoc:
      use: false
      model_type: 'dinov2_vitg14'    # One of the following: "dinov2_vit<s|b|l|g>14"; should be same between training and testing
      ckpt_path: 'None'    # If set to 'None', loads model_type from PyTorch Hub
      max_image_size: 1024    # To prevent out of memory
      desc_layer: 31
      desc_facet: 'value'    # one of "query", "key", "value", "token"
      num_clusters: 32
      domain: 'indoor'    # one of "aerial", "indoor", "urban"
      cache_dir: 'parameters/AnyLoc/demo/cache'
      cuda: true
    DinoV2Salad:
      use: true
      ckpt_path: 'parameters/DinoV2Salad/ckpts/dino_salad.ckpt'
      max_image_size: 1024    # To prevent out of memory
      cuda: true
    CricaVPR:
      use: false
      ckpt_path: 'parameters/CricaVPR/ckpts/CricaVPR.pth'
      cuda: true
    NetVlad:
      use: false
      ckpt_path: 'parameters/netvlad/paper/checkpoints'
      arch: 'vgg16'
      num_clusters: 64
      pooling: 'netvlad'
      vladv2: false
      nocuda: false

  local:
    superpoint+lightglue:
      detector_name: superpoint
      nms_radius: 4
      max_keypoints: 4096
      matcher_name: lightglue
      match_conf:
        width_confidence: -1
        depth_confidence: -1

vpr:
  topk:
    - 1
    - 2
    - 5
    - 10
    - 15
    - 20
    - 25
  threshold:
    - 25
  global_extractor:
    # If netvlad is used, then remaining methods are ignored given GlobalExtractor preprocess=False