Hao Fang, Tong Zhang, Xiaofei Zhou, Xinxin Zhang

[paper] [BibTeX]

See installation instructions.

We provide a script train_net.py, that is made to train all the configs provided in LBVQ.

To train a model with "train_net.py" on VIS, first setup the corresponding datasets following Preparing Datasets for LBVQ.

Then run with COCO pretrained weights in the Model Zoo:

python train_net.py --num-gpus 8 \
  --config-file configs/youtubevis_2019/lbvq_R50_bs8.yaml \
  MODEL.WEIGHTS mask2former_r50_coco.pkl

To evaluate a model's performance, use

python train_net.py \
  --config-file configs/youtubevis_2019/lbvq_R50_bs8.yaml \
  --eval-only MODEL.WEIGHTS lbvq_r50_ytvis19.pth

If you want to use SAM to refine your results, use

python train_net.py \
  --config-file configs/youtubevis_2019/lbvq_R50_bs8.yaml \
  --eval-only MODEL.WEIGHTS lbvq_r50_ytvis19.pth SAM True

To visualize a video in the dataset, use

python demo_lbvq/demo.py --config-file configs/youtubevis_2019/lbvq_R50_bs8.yaml \
  --input datasets/ytvis_2019/valid/JPEGImages/xxxxxxx/*.jpg \
  --output output/demo --save-frames True \
  --opts MODEL.WEIGHTS lbvq_r50_ytvis2019.pth

The majority of LBVQ is licensed under a Apache-2.0 License. However portions of the project are available under separate license terms: Detectron2(Apache-2.0 License), Mask2Former(MIT License), and VITA(Apache-2.0 License).

If you use LBVQ in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.

@article{Fang2024learning,
  title={Learning Better Video Query with SAM for Video Instance Segmentation},
  author={Fang, Hao and Zhang, Tong and Zhou, Xiaofei and Zhang, Xinxin},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  year={2024},
  publisher={IEEE}
}

Our code is largely based on Detectron2, Mask2Former, and VITA. We are truly grateful for their excellent work.