jinming-su/stmask

STMask

The code is implmented for our paper in CVPR2021:

• STMask: Spatial Feature Calibration and Temporal Fusion for Effective One-stage Video Instance Segmentation

News

• [14/04/2021] Release code on Github and paper on arxiv

Installation

• Clone this repository and enter it:

  git clone https://github.com/MinghanLi/STMask.git
  cd STMask

• Set up the environment using one of the following methods:

  • Using Anaconda
    • Run conda env create -f environment.yml
    • conda activate STMask-env
  • Manually with pip
    • Set up a Python3 environment.
    • Install Pytorch 1.0.1 (or higher) and TorchVision.
    • Install some other packages:

      # Cython needs to be installed before pycocotools
      pip install cython
      pip install opencv-python pillow pycocotools matplotlib 

• Install mmcv and mmdet

  • According to your Cuda and pytorch version to install mmcv or mmcv-full from here. Here my cuda and torch version are 10.1 and 1.5.0 respectively.

    pip install mmcv-full==1.1.2 -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.5.0/index.html

  • install cocoapi and a customized COCO API for YouTubeVIS dataset from here

    pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI"
    git clone https://github.com/youtubevos/cocoapi
    cd cocoapi/PythonAPI
    # To compile and install locally 
    python setup.py build_ext --inplace
    # To install library to Python site-packages 
    python setup.py build_ext install

• Install spatial-correlation-sampler

  pip install spatial-correlation-sampler

• Complie DCNv2 code (see Installation)

  • Download code for deformable convolutional layers from here

    git clone https://github.com/CharlesShang/DCNv2.git
    cd DCNv2
    python setup.py build develop

Dataset

• If you'd like to train STMask, download the YTVOS2019 dataset from the official web.

Evaluation

Quantitative Results on YTVOS2019

Here are our STMask models (released on April, 2021) along with their FPS on a 2080Ti and mAP on validset, where mAP and mAP* are obtained under cross class fast nms and fast nms respectively:

Image Size	Backbone	FCA	FCB	TF	FPS	mAP	mAP*	Weights
[384,640]	R50-DCN-FPN	FCA	-	TF	29.3	32.6	33.4	STMask_plus_resnet50.pth
[384,640]	R50-DCN-FPN	FCA	FCB(ali)	TF	27.8	-	32.1	STMask_plus_resnet50_ali.pth
[384,640]	R50-DCN-FPN	FCA	FCB(ada)	TF	28.6	32.8	33.0	STMask_plus_resnet50_ada.pth
[384,640]	R101-DCN-FPN	FCA	-	TF	24.5	36.0	36.3	STMask_plus_base.pth
[384,640]	R101-DCN-FPN	FCA	FCB(ali)	TF	22.1	36.3	37.1	STMask_plus_base_ali.pth
[384,640]	R101-DCN-FPN	FCA	FCB(ada)	TF	23.4	36.8	37.9	STMask_plus_base_ada.pth

To evalute the model, put the corresponding weights file in the ./weights directory and run one of the following commands. The name of each config is everything before the numbers in the file name (e.g., STMask_plus_base for STMask_plus_base.pth). Here all STMask models are trained based on yolact_plus_base_54_80000.pth or yolact_plus_resnet_54_80000.pth from Yolact++ here.

Quantitative Results on COCO

We also provide quantitative results of Yolcat++ with our proposed feature calibration for anchors and boxes on COCO. Here are the results on COCO valid set.

Image Size	Backbone	FCA	FCB	B_AP	M_AP	Weights
[550,550]	R50-DCN-FPN	FCA	-	34.5	32.9	yolact_plus_resnet50_54.pth
[550,550]	R50-DCN-FPN	FCA	FCB(ali)	34.6	33.3	yolact_plus_resnet50_ali_54.pth
[550,550]	R50-DCN-FPN	FCA	FCB(ada)	34.7	33.2	yolact_plus_resnet50_ada_54.pth
[550,550]	R101-DCN-FPN	FCA	-	35.7	33.3	yolact_plus_base_54.pth
[550,550]	R101-DCN-FPN	FCA	FCB(ali)	35.6	34.1	yolact_plus_base_ali_54.pth
[550,550]	R101-DCN-FPN	FCA	FCB(ada)	36.4	34.8	yolact_plus_baseada_54.pth

Inference

# Output a YTVOSEval json to submit to the website.
# This command will create './weights/results.json' for instance segmentation.
python eval.py --config=STMask_plus_base_ada_config --trained_model=weights/STMask_plus_base_ada.pth --mask_det_file=weights/results.json

Training

By default, we train on YouTubeVOS2019 dataset. Make sure to download the entire dataset using the commands above.

• To train, grab an COCO-pretrained model and put it in ./weights.

  • [Yolcat++]: For Resnet-50/-101, download yolact_plus_base_54_80000.pth or yolact_plus_resnet_54_80000.pth from Yolact++ here.
  • [Yolcat++ & FC]: Alternatively, you can use those Yolact++ with FC models on Table. 2 for training, which can obtain a relative higher performance than that of Yolact++ models.

• Run one of the training commands below.

  • Note that you can press ctrl+c while training and it will save an *_interrupt.pth file at the current iteration.
  • All weights are saved in the ./weights directory by default with the file name <config>_<epoch>_<iter>.pth.

# Trains STMask_plus_base_config with a batch_size of 8.
CUDA_VISIBLE_DEVICES=0,1 python train.py --config=STMask_plus_base_config --batch_size=8 --lr=1e-4 --save_folder=weights/weights_r101


# Resume training STMask_plus_base with a specific weight file and start from the iteration specified in the weight file's name.
CUDA_VISIBLE_DEVICES=0,1 python train.py --config=STMask_plus_base_config --resume=weights/STMask_plus_base_10_32100.pth 

Citation

If you use STMask or this code base in your work, please cite

@inproceedings{STMask-CVPR2021,
  author    = {Minghan Li and Shuai Li and Lida Li and Lei Zhang},
  title     = {Spatial Feature Calibration and Temporal Fusion for Effective \\ One-stage Video Instance Segmentation},
  booktitle = {CVPR},
  year      = {2021},
}

Contact

For questions about our paper or code, please contact Li Minghan ([email protected] or [email protected]).