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RTTS

@article{li2018benchmarking,
  title={Benchmarking single-image dehazing and beyond},
  author={Li, Boyi and Ren, Wenqi and Fu, Dengpan and Tao, Dacheng and Feng, Dan and Zeng, Wenjun and Wang, Zhangyang},
  journal={IEEE Transactions on Image Processing},
  volume={28},
  number={1},
  pages={492--505},
  year={2018},
  publisher={IEEE}
}

The dataset contains 4,322 foggy images, covering five categories: bicycle, bus, car, motorbike, and person.

Download RTTS Dataset

The Real-word Task-driven Testing Set (RTTS) dataset is a part of RESIDE dataset, which can be downloaded from here.

We randomly divides the RTTS dataset into training and testing groups with 3,457 and 865 images, respectively. If users want to divide by their own, tools/misc/write_txt.py should be used to split the train and val set first. Then tools/dataset_converters/xml_to_json.py can use to convert xml style annotations to coco format.

The data structure is as follows:

lqit
├── lqit
├── tools
├── configs
├── data
│   ├── RESIDE
│   │   ├── RTTS
│   │   │   ├── ImageSets
│   │   │   │   ├── Main
│   │   │   │   │   ├── train.txt
│   │   │   │   │   ├── val.txt
│   │   │   ├── annotations_xml     # pascal voc style annotations
│   │   │   │   ├── AM_Bing_211.xml
│   │   │   │   ├── AM_Bing_217.xml
│   │   │   │   ├── ...
│   │   │   ├── annotations_json    # coco style annotations
│   │   │   │   ├── rtts_train.json
│   │   │   │   ├── rtts_val.json
│   │   │   ├── JPEGImages
│   │   │   │   ├── AM_Bing_211.png
│   │   │   │   ├── AM_Bing_217.png
│   │   │   │   ├── ...