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Prepare Datasets for BoxVIS

The required datasets should be

$datasets/
  coco/
  ytvis_2019/
  ytvis_2021/
  ovis/
  bvisd/

./datasets relative to your current working directory.

STEP-1: Prepare Image & VIS datasets

Expected dataset structure for COCO:

coco/
  annotations/
    instances_{train,val}2017.json
    coco2bvisd_train.json (converted)
    {train,val}2017/
    # image files that are mentioned in the corresponding json

Expected dataset structure for YouTube-VIS 2021 and 2022:

For evaluating on valid set of YouTubeVIS 2022, you just need to replace 'valid.json' of YouTubeVIS 2021 with 'valid.json' of YouTubeVIS 2022.

ytvis_2021/
  {train,valid,test}.json
  {train,valid,test}/
    JPEGImages/

Expected dataset structure for OVIS:

ovis/
  {train,valid,test}.json
  {train,valid,test}/
    JPEGImages/

Split train set for YouTube-VIS and OVIS

For convenient model evaluation, we split the training annotations train.json into two sets: train_sub.json and valid_sub.json. And train_sub.json is used for training, while valid_sub.json is used for validation.

python boxvis/data/data_utils/split_train_set.py

STEP-2: Prepare Box-supervised VIS Dataset (BVISD)

Convert COCO to BVISD dataset

There are 25 overlapping categories between COCO and BVISD, including around 90k images and 450k objects. Please run the following command to extract these images:

python boxvis/data/data_utils/convert_coco2bvisd.py

Alternatively, you can download the converted annotation files from here.

During training, the static images from COCO will be augmented to pseudo video clips by the module build_pseudo_augmentation, please refer to boxvis/data/augmentation.py for more details.

Joint training on BVISD

We utilize CombinedDataLoader to simultaneously load video clips from YTVIS21, OVIS and COCO datasets, where the sampling weights are 1/2, 1/4, and 1/4, respectively. Please refer to boxvis/data/combined_loader.py for more details.

For your easier understanding, we point out some core code of joint training on BVISD.

  • The mapping ids of object categories between source and target datasets are provided in boxvis/data/datasets/bvisd.py.

  • When loading video clips, the category ids of YTVIS21, OVIS and COCO are mapped to BVISD's category ids. Please refer to Lines 176-191 and 369-381 in boxvis/data/dataset_mapper.py.

  • You can modify the sampling weights of BVISD datasets from the configure file:

    DATASETS:
  DATASET_RATIO: [2., 1., 1.]
  TRAIN: ("ytvis_2021_train", "ovis_train", "coco2bvisd_train")
  TEST: ("bvisd_dev", )

Convert BVISD valid_sub set

Valid_sub set of BVISD consists of all videos in the valid_sub sets of YTVIS21 and OVIS, and category ids of YTVIS21 and OVIS are mapped to BVISD's category ids. Please run:

python boxvis/data/data_utils/convert_bvisd_valid_set.py

Alternatively, you can download the converted annotation files from here.

Compared with isolated YTVIS21 or OVIS valid_sub set, BVISD's valid_sub set covers more diverse variants of videos, thereby can be used to evaluate the generalization ability of VIS models.

Expected final dataset structure for all:

$datasets/
    +-- coco
    |   |
    |   +-- annotations
    |   |   |
    |   |   +-- instances_{train,val}2017.json
    |   |   +-- coco2bjvis_train.json
    |   |
    |   +-- {train,val}2017
    |
    +-- ytvis_2021
    |   | 
    |   +-- train.json
    |   +-- train_sub.json
    |   +-- valid.json
    |   +-- valid_sub.json
    |   +-- test.json
    |
    +-- ovis
    |   | 
    |   +-- train.json
    |   +-- train_sub.json
    |   +-- valid.json
    |   +-- valid_sub.json
    |   +-- test.json
    |
    +-- bvisd
    |   | 
    |   +-- valid_sub.json
    ```