For a few datasets that detectron2 natively supports, the datasets are assumed to exist in a directory called "datasets/", under the directory where you launch the program. They need to have the following directory structure:

Expected dataset structure for COCO instance/keypoint detection:

coco/
  annotations/
    instances_{train,val}2017.json
    person_keypoints_{train,val}2017.json
  {train,val}2017/
    # image files that are mentioned in the corresponding json

You can use the 2014 version of the dataset as well.

Some of the builtin tests (dev/run_*_tests.sh) uses a tiny version of the COCO dataset, which you can download with ./prepare_for_tests.sh.

Expected dataset structure for PanopticFPN:

coco/
  annotations/
    panoptic_{train,val}2017.json
  panoptic_{train,val}2017/
    # png annotations
  panoptic_stuff_{train,val}2017/  # generated by the script mentioned below

Install panopticapi by:

pip install git+https://github.com/cocodataset/panopticapi.git

Then, run python prepare_panoptic_fpn.py, to extract semantic annotations from panoptic annotations.

Expected dataset structure for LVIS instance segmentation:

coco/
  {train,val,test}2017/
lvis/
  lvis_v0.5_{train,val}.json
  lvis_v0.5_image_info_test.json

Install lvis-api by:

pip install git+https://github.com/lvis-dataset/lvis-api.git

Expected dataset structure for cityscapes:

cityscapes/
  gtFine/
    train/
      aachen/
        color.png, instanceIds.png, labelIds.png, polygons.json,
        labelTrainIds.png
      ...
    val/
    test/
  leftImg8bit/
    train/
    val/
    test/

Install cityscapes scripts by:

pip install git+https://github.com/mcordts/cityscapesScripts.git

Note: labelTrainIds.png are created by cityscapesscripts/preparation/createTrainIdLabelImgs.py. They are not needed for instance segmentation.

Expected dataset structure for Pascal VOC:

VOC20{07,12}/
  Annotations/
  ImageSets/
    Main/
      trainval.txt
      test.txt
      # train.txt or val.txt, if you use these splits
  JPEGImages/