Skip to content

Latest commit

 

History

History
194 lines (160 loc) · 6.28 KB

README.md

File metadata and controls

194 lines (160 loc) · 6.28 KB

Prepare Datasets for Mask-Adapter

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.

Mask-Adapter 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/
  # Training datasets
  coco/
  # Evaluatation datasets
  ADEChallengeData2016/
  VOCdevkit/
  ADE20K_2021_17_01/
  pascal_ctx_d2/
  pascal_voc_d2/

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.

Expected dataset structure for COCO:

coco/
  #http://images.cocodataset.org/zips/train2017.zip
  train2017/ 
  #http://images.cocodataset.org/zips/val2017.zip
  val2017/
  # image files that are mentioned in the corresponding json
  #http://images.cocodataset.org/annotations/annotations_trainval2017.zip  
  annotations/ 
    instances_{train,val}2017.json
    panoptic_{train,val}2017.json
    captions_{train,val}2017.json

  #COCO-Panoptic
  panoptic_{train,val}2017/  # png annotations
  panoptic_semseg_{train,val}2017/  # generated by the script mentioned below

  #COCO-Stuff
  #http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip
  stuffthingmaps/
  # generate by prepare_coco_stuff_sem_seg.py
  stuffthingmaps_detectron2/

Install panopticapi by:

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

Then, run python datasets/prepare_coco_semantic_annos_from_panoptic_annos.py, to extract semantic annotations from panoptic annotations (only used for evaluation). Generate the semantic segmentation annotations coco/stuffthingmaps_detectron2/ by running:python datasets/prepare_coco_stuff_sem_seg.py

Expected dataset structure for ADE20k (A150):

ADEChallengeData2016/
  images/
  annotations/
  objectInfo150.txt
  # download instance annotation
  annotations_instance/
  # generated by prepare_ade20k_sem_seg.py
  annotations_detectron2/
  # below are generated by prepare_ade20k_pan_seg.py
  ade20k_panoptic_{train,val}.json
  ade20k_panoptic_{train,val}/
  # below are generated by prepare_ade20k_ins_seg.py
  ade20k_instance_{train,val}.json

The directory annotations_detectron2 is generated by running python datasets/prepare_ade20k_sem_seg.py.

Install panopticapi by:

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

Download the instance annotation from http://sceneparsing.csail.mit.edu/:

wget http://sceneparsing.csail.mit.edu/data/ChallengeData2017/annotations_instance.tar

Then, run python datasets/prepare_ade20k_pan_seg.py, to combine semantic and instance annotations for panoptic annotations.

And run python datasets/prepare_ade20k_ins_seg.py, to extract instance annotations in COCO format.

Expected dataset structure for ADE20k-Full (A-847):

ADE20K_2021_17_01/
  images/
  index_ade20k.pkl
  objects.txt
  # generated by prepare_ade20k_full_sem_seg.py
  images_detectron2/
  annotations_detectron2/

Register and download the dataset from https://groups.csail.mit.edu/vision/datasets/ADE20K/:

cd $DETECTRON2_DATASETS
wget your/personal/download/link/{username}_{hash}.zip
unzip {username}_{hash}.zip

Generate the directories ADE20K_2021_17_01/images_detectron2 and ADE20K_2021_17_01/annotations_detectron2 by running:

python datasets/prepare_ade20k_full_sem_seg.py

Expected dataset structure for PASCAL Context (PC-59), PASCAL Context Full (PC-459):

VOCdevkit/
  VOC2012/
    Annotations/
    JPEGImages/
    ImageSets/
      Segmentation/
  VOC2010/
    JPEGImages/
    trainval/
    trainval_merged.json
# generated by prepare_pascal_voc_sem_seg.py
pascal_voc_d2/
  images/
  annotations_pascal20/
# generated by prepare_pascal_ctx_sem_seg.py
pascal_ctx_d2/
  images/
  annotations_ctx59/
  # generated by prepare_pascal_ctx_full_sem_seg.py
  annotations_ctx459/

PASCAL VOC (PAS-20)

Download the dataset from http://host.robots.ox.ac.uk/pascal/VOC/:

cd $DETECTRON2_DATASETS
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
# generate folder VOCdevkit/VOC2012
tar -xvf VOCtrainval_11-May-2012.tar

Generate directory pascal_voc_d2 running:

python datasets/prepare_pascal_voc_sem_seg.py

PASCAL Context (PC-59)

Download the dataset from http://host.robots.ox.ac.uk/pascal/VOC/ and annotation from https://www.cs.stanford.edu/~roozbeh/pascal-context/:

cd $DETECTRON2_DATASETS
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2010/VOCtrainval_03-May-2010.tar
# generate folder VOCdevkit/VOC2010
tar -xvf VOCtrainval_03-May-2010.tar 
wget https://www.cs.stanford.edu/~roozbeh/pascal-context/trainval.tar.gz
# generate folder VOCdevkit/VOC2010/trainval
tar -xvzf trainval.tar.gz -C VOCdevkit/VOC2010 
wget https://codalabuser.blob.core.windows.net/public/trainval_merged.json -P VOCdevkit/VOC2010/

Install Detail API by:

git clone https://github.com/zhanghang1989/detail-api.git
rm detail-api/PythonAPI/detail/_mask.c
pip install -e detail-api/PythonAPI/

Generate directory pascal_ctx_d2/images and pascal_ctx_d2/annotations_ctx59 running:

python datasets/prepare_pascal_ctx_sem_seg.py

PASCAL Context Full (PC-459)

Generate directory pascal_ctx_d2/annotations_ctx459 running:

python datasets/prepare_pascal_ctx_full_sem_seg.py