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DATASETS.md

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Dataset Preparation

Step 1: Prepare required benchmark datasets. Almost all popular DAOD benchmarks are supported in this project.

[DATASET_PATH]
Cityscapes
   └─ cocoAnnotations
   └─ leftImg8bit
      └─ train
      └─ val
   └─ leftImg8bit_foggy
      └─ train
      └─ val
KITTI
   └─ Annotations
   └─ ImageSets
   └─ JPEGImages
Sim10k
   └─ Annotations
   └─ ImageSets
   └─ JPEGImages
BDD100k
   └─ cocoAnnotations
   └─ images
      └─ train
      └─ val
style-transferred
   └─ VOC2007_to_clipart
   └─ VOC2012_to_clipart
   └─ VOC2007_to_watercolor
   └─ VOC2012_to_watercolor
   └─ VOC2007_to_comic
   └─ VOC2012_to_clipart
clipart
   └─ Annotations
   └─ ImageSets
   └─ JPEGImages
watercolor
   └─ Annotations
   └─ ImageSets
   └─ JPEGImages
comic
   └─ Annotations
   └─ ImageSets
   └─ JPEGImages
VOCdevkit # only used for source-only training
   └─ VOC2007
   └─ VOC2012

We follow EPM for city-based settings. Pure annotation files are available at onedrive. Some datasets are avaliable at datasets.

Cityscapes -> Foggy Cityscapes

  • Download Cityscapes and Foggy Cityscapes dataset from the link. Particularly, we use leftImg8bit_trainvaltest.zip for Cityscapes and leftImg8bit_trainvaltest_foggy.zip for Foggy Cityscapes.
  • Download and extract the converted annotation from the following links: Cityscapes and Foggy Cityscapes (COCO format)
  • Extract the training sets from leftImg8bit_trainvaltest.zip, then move the folder leftImg8bit/train/ to Cityscapes/leftImg8bit/ directory.
  • Extract the training and validation set from leftImg8bit_trainvaltest_foggy.zip, then move the folder leftImg8bit_foggy/train/ and leftImg8bit_foggy/val/ to Cityscapes/leftImg8bit_foggy/ directory.

Sim10k -> Cityscapes (class car only)

  • Download Sim10k dataset and Cityscapes dataset from the following links: Sim10k and Cityscapes. Particularly, we use repro_10k_images.tgz and repro_10k_annotations.tgz for Sim10k and leftImg8bit_trainvaltest.zip for Cityscapes.
  • Download and extract the converted annotation from the following links: Sim10k (VOC format) and Cityscapes (COCO format)
  • Extract the training set from repro_10k_images.tgz and repro_10k_annotations.tgz, then move all images under VOC2012/JPEGImages/ to Sim10k/JPEGImages/ directory and move all annotations under VOC2012/Annotations/ to Sim10k/Annotations/.
  • Extract the training and validation set from leftImg8bit_trainvaltest.zip, then move the folder leftImg8bit/train/ and leftImg8bit/val/ to Cityscapes/leftImg8bit/ directory.

KITTI -> Cityscapes (class car only)

  • Download KITTI dataset and Cityscapes dataset from the following links: KITTI and Cityscapes. Particularly, we use data_object_image_2.zip for KITTI and leftImg8bit_trainvaltest.zip for Cityscapes.
  • Download and extract the converted annotation from the following links: KITTI (VOC format) and Cityscapes (COCO format).
  • Extract the training set from data_object_image_2.zip, then move all images under training/image_2/ to KITTI/JPEGImages/ directory.
  • Extract the training and validation set from leftImg8bit_trainvaltest.zip, then move the folder leftImg8bit/train/ and leftImg8bit/val/ to Cityscapes/leftImg8bit/ directory.

Cityscapes -> BDD100k (7-class evaluation w/o class train)

  • You can use the uploaded version from this link BDD100K, which correct the inconsistent class names and remove unused images.
  • The official website: BDD100K.

Pascal VOC 07/12 -> Clipart

  • Download the style-transferred Pascal VOC 07/12 datasets from this link, which are borrowed from D-adapt.
  • Extract the training set and move them to style-transferred directory.
  • Download the clipart dataset from this link clipart, and extract it to clipart directory.

Pascal VOC 07/12 -> Watercolor/Comic

  • Download the style-transferred Pascal VOC 07/12 datasets from this link, which are borrowed from D-adapt.
  • Extract the training set and move them to style-transferred/ directory.
  • Since most images are not used for DAOD tasks, we upload a formatted vesion at this link , which removes the unused images to save the disk space.
  • The offical watercolor/comic dataset is available at this link watercolor/comic, and extract it to watercolor/ and comic/ directory, respectively.

Step 2: change the data root for your dataset at paths_catalog.py.

DATA_DIR = [$Your dataset root]