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Prepare Datasets for CAT-Seg

A dataset can be used by accessing DatasetCatalog for its data, or MetadataCatalog for its metadata (class names, etc). This document explains how to setup the builtin datasets so they can be used by the above APIs. Use Custom Datasets gives a deeper dive on how to use DatasetCatalog and MetadataCatalog, and how to add new datasets to them.

CAT-Seg has builtin support for a few datasets. The datasets are assumed to exist in a directory specified by the environment variable DETECTRON2_DATASETS. Under this directory, detectron2 will look for datasets in the structure described below, if needed.

$DETECTRON2_DATASETS/
  coco/                   # COCO-Stuff
  ADEChallengeData2016/   # ADE20K-150
  ADE20K_2021_17_01/      # ADE20K-847
  VOCdevkit/ 
    VOC2010/              # PASCAL Context
    VOC2012/              # PASCAL VOC

You can set the location for builtin datasets by export DETECTRON2_DATASETS=/path/to/datasets. If left unset, the default is ./datasets relative to your current working directory.

Prepare data for COCO-Stuff:

Expected data structure 

coco-stuff/
  annotations/
    train2017/
    val2017/
  images/
    train2017/
    val2017/
  # below are generated by prepare_coco_stuff.py
  annotations_detectron2/
    train2017/
    val2017/

Download the COCO (2017) images from https://cocodataset.org/

wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip

Download the COCO-Stuff annotation from https://github.com/nightrome/cocostuff.

wget http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip

Unzip train2017.zip, val2017.zip, and stuffthingmaps_trainval2017.zip. Then put them to the correct location listed above.

Generate the labels for training and testing.

python datasets/prepare_coco_stuff.py

Prepare data for ADE20K-150:

Expected data structure 

ADEChallengeData2016/
  annotations/
    validation/
  images/
    validation/
  # below are generated by prepare_ade20k_150.py
  annotations_detectron2/
    validation/

Download the data of ADE20K-150 from http://sceneparsing.csail.mit.edu.

wget http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip

Unzip ADEChallengeData2016.zip and generate the labels for testing.

python datasets/prepare_ade20k_150.py

Prepare data for ADE20k-847:

Expected data structure 

ADE20K_2021_17_01/
  images/
    ADE/
      validation/
  index_ade20k.mat
  index_ade20k.pkl
  # below are generated by prepare_ade20k_847.py
  annotations_detectron2/
    validation/

Download the data of ADE20k-Full from https://groups.csail.mit.edu/vision/datasets/ADE20K/request_data/ Unzip the dataset and generate the labels for testing.

python datasets/prepare_ade20k_847.py

Prepare data for PASCAL VOC 2012:

Expected data structure 

VOCdevkit/
  VOC2012/
    Annotations/
    ImageSets/
    JPEGImages/
    SegmentationClass/
    SegmentationClassAug/ 
    SegmentationObject/
    # below are generated by prepare_voc.py
    annotations_detectron2
    annotations_detectron2_bg

Download the data of PASCAL VOC from http://host.robots.ox.ac.uk/pascal/VOC/voc2012/#devkit.

We use SBD augmentated training data as SegmentationClassAug following Deeplab.

wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
wget https://www.dropbox.com/s/oeu149j8qtbs1x0/SegmentationClassAug.zip

Unzip VOCtrainval_11-May-2012.tar and SegmentationClassAug.zip. Then put them to the correct location listed above and generate the labels for testing.

python datasets/prepare_voc.py

Prepare data for PASCAL Context:

Expected data structure 

VOCdevkit/
  VOC2010/
    Annotations/
    ImageSets/
    JPEGImages/
    SegmentationClass/
    SegmentationObject/
    trainval/
    labels.txt
    59_labels.txt
    pascalcontext_val.txt
    # below are generated by prepare_pascal_context.py
    annotations_detectron2/
      pc459_val
      pc59_val

Download the data of PASCAL VOC 2010 from https://www.cs.stanford.edu/~roozbeh/pascal-context/. 

wget http://host.robots.ox.ac.uk/pascal/VOC/voc2010/VOCtrainval_03-May-2010.tar
wget https://www.cs.stanford.edu/~roozbeh/pascal-context/trainval.tar.gz
wget https://www.cs.stanford.edu/~roozbeh/pascal-context/59_labels.txt

Unzip VOCtrainval_03-May-2010.tar and trainval.tar.gz. Then put them to the correct location listed above and generate the labels for testing.

python datasets/prepare_pascal_context.py