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Codes for "TransVOS: Video Object Setmentation with Transformers"

This repository contains the official codes for TransVOS: Video Object Setmentation with Transformers.

Requirements

To installl requirements, run:

conda env update -n TransVOS --file requirements.yaml

Data Organization

Static images

We follow AFB-URR to convert static images (MSRA10K, ECSSD, PASCAL-S, PASCAL VOC2012, COCO) into a uniform format (followed DAVIS).

Youtube-VOS

Download the YouTube-VOS dataset, then organize data as following format:

YTBVOS
      |----train
      |     |-----JPEGImages
      |     |-----Annotations
      |     |-----meta.json
      |----valid
      |     |-----JPEGImages
      |     |-----Annotations
      |     |-----meta.json

Where JPEGImages and Annotations contain the frames and annotation masks of each video.

DAVIS

Download the DAVIS17 datasets, then organize data as following format:

DAVIS
      |----JPEGImages
      |     |-----480p
      |----Annotations
      |     |-----480p (annotations for DAVIS 2017)
      |----ImageSets
      |     |-----2016
      |     |-----2017
      |----DAVIS-test-dev (data for DAVIS 2017 test-dev)

Training

Pretraining on static images

To pretrain the TransVOS network on static images, modify the dataset root ($cfg.DATA.PRETRAIN_ROOT) in config.py, then run following command.

python train.py --gpu ${GPU-IDS} --exp_name ${experiment} --pretrain

Training on DAVIS17 & YouTube-VOS

To train the TransVOS network on DAVIS & YouTube-VOS, modify the dataset root ($cfg.DATA.DAVIS_ROOT, $cfg.DATA.YTBVOS_ROOT) in config.py, then run following command.

python train.py --gpu ${GPU-IDS} --exp_name ${experiment} --initial ${./checkpoints/*.pth.tar}

Testing

Download the pretrained DAVIS17 checkpoint and YouTube-VOS checkpoint.

To eval the TransVOS network on (DAVIS16/17), modify $cfg.DATA.VAL.DATASET_NAME, then run following command

python eval.py --checkpoint ${./checkpoints/*.pth.tar}

To test the TransVOS network on (DAVIS17 test-dev/youTube-vos), modify $cfg.DATA.TEST.DATASET_NAME, then run following command

python test.py --checkpoint ${./checkpoints/*.pth.tar}

The test results will be saved as indexed png file at ${results}/.

Additionally, you can modify some setting parameters in config.py to change configuration.

Acknowledgement

This codebase is built upon official AFB-URR repository and official DETR repository.

Citation

@article{mei2021transvos,
  title={TransVOS: Video Object Segmentation with Transformers},
  author={Mei, Jianbiao and Wang, Mengmeng and Lin, Yeneng and Liu, Yong},
  journal={arXiv preprint arXiv:2106.00588},
  year={2021}
}