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SimVOS

The codes for ICCV 2023 paper 'Scalable Video Object Segmentation with Simplified Framework'

:sunny: Highlights

* Our Goal: providing a simple and scalable VOS baseline to explore the effect of self-supervised pre-training.

* Our SimVOS only relies on video sequence for one-stage training and achieves favorable performance on DAVIS and YouTube datasets.

* Our project is built upon the STCN library. Thanks for their contribution.

Install the environment

We use the Anaconda to create the Python environment, which mainly follows the installation in STCN. The cuda environment we use for result reproduction is python3.6-cuda11.0-cudnn8.1. One installation packages can be found in environment.yaml.

Data preparation

We follow the same data preparation steps used in STCN. Download both DAVIS and YouTube-19 datasets:

├── DAVIS
│   ├── 2016
│   │   ├── Annotations
│   │   └── ...
│   └── 2017
│       ├── test-dev
│       │   ├── Annotations
│       │   └── ...
│       └── trainval
│           ├── Annotations
│           └── ...
├── YouTube
│   ├── all_frames
│   │   └── valid_all_frames
│   ├── train
│   ├── train_480p
│   └── valid

Pre-trained model download

Please download the pre-trained weights (e.g., MAE: ViT-Base or ViT-Large) and put them in ./pretrained_models folder.

Training command

To train a SimVOS model (ViT-Base with MAE Init.) w/ token refinement (e.g., the default seeting with 384/384 foreground/background prototypes and layer_index=4 for prptotype generation):

python -m torch.distributed.launch --master_port 9842 --nproc_per_node=8 train_simvos.py --id retrain_s03 --stage 3

If you want to train a SimVOS-B model w/o token refinement:

python -m torch.distributed.launch --master_port 9842 --nproc_per_node=8 train_simvos.py --id retrain_s03 --stage 3 --layer_index 0 --use_token_learner False

or SimVOS-L model:

python -m torch.distributed.launch --master_port 9842 --nproc_per_node=8 train_simvos.py --id retrain_s03 --stage 3 --layer_index 0 --use_token_learner False --backbone_type vit_large

Evaluation command

Download the SimVOS models SimVOS-BS(384/384-layer_index=4-vitbase), SimVOS-B(vitbase), and SimVOS-L(vitlarge). Put the models in the test_checkpoints folder. After taht, run the evaluation w/ the following commands. All evaluations are done in the 480p resolution.

#SimVOS-BS
python submit_eval_davis_ours_all.py --model_path ./test_checkpoints --davis_path ./Data/DAVIS/2017 --output ./results --split val --layer_index 4 --use_token_learner --backbone_type vit_base
#SimVOS-B
python submit_eval_davis_ours_all.py --model_path ./test_checkpoints --davis_path ./Data/DAVIS/2017 --output ./results --split val --layer_index 0 --backbone_type vit_base
#SimVOS-L
python submit_eval_davis_ours_all.py --model_path ./test_checkpoints --davis_path ./Data/DAVIS/2017 --output ./results --split val --layer_index 0 --backbone_type vit_large

After running the above evaluation, you could get the qualitative results saved in the root project directory. You could use the offline evaluation toolikit (https://github.com/davisvideochallenge/davis2017-evaluation) to get the validation performance on DAVIS-16/17. For test-dev on DAVIS-17, using the online evaluation server instead.

If you find our work useful in your research, please consider citing:

@inproceedings{wu2023,
  title={Scalable Video Object Segmentation with Simplified Framework},
  author={Qiangqiang Wu and Tianyu Yang and Wei Wu and Antoni B. Chan},
  booktitle={ICCV},
  year={2023}
}