Awesome

OPS: Towards Open-World Segmentation of Parts

CVPR 2023

Tai-Yu Pan, Qing Liu, Wei-Lun Chao, Brian Price

[BibTeX]

Installation

See Mask2Former. Additaional packages for OPS:

pip install -e kmeans_pytorch
pip install -U scikit-image

Tested environment:

python==3.8.13
torch==1.13.0+cu116
torchaudio==0.13.0+cu116
torchvision==0.14.0+cu116
cudatoolkit==11.6.0

numpy==1.23.5
numba==0.56.3
scikit-image==0.20.0

Demo

Option 1: Streamlit

Interacitve mode to draw masks. Need to install streamlit.

pip install streamlit streamlit-drawable-canvas

then run

streamlit run predict_part_web.py CKPT

For a quick start, one can use demo/5682167295_e61bfbb33e_z.jpg, an image from COCO.

Option 2: Inference with RGBA images

python predict_part.py CKPT --input IMG_RGBA_1 IMG_RGBA_2 --score-threshold 0.1 --topk 10

Output images are located at demo/outputs if not specified. See predict_part.py for more information. For a quick start, one can use demo/5682167295_e61bfbb33e_z_1.png, a modified RGBA image from COCO. Will get the following:

Training

python train_net_part.py --config-file configs/part_segmentation/SETTING --num-gpus N

For more command line options, please see Mask2Former and train_net_part.py for more information.

License

Shield:

The majority of OPS is licensed under a MIT License.

However portions of the project are available under separate license terms: Swin-Transformer-Semantic-Segmentation is licensed under the MIT license, Deformable-DETR is licensed under the Apache-2.0 License.

<a name="CitingOPS"></a>Citing OPS

If you use OPS in your research, please use the following BibTeX entry.

@inproceedings{pan2023ops,
  title={Towards Open-World Segmentation of Parts},
  author={Tai-Yu Pan and Qing Liu and Wei-Lun Chao and Brian Price},
  journal={CVPR},
  year={2023}
}

Acknowledgement

Code is largely based on Mask2Former (https://github.com/facebookresearch/Mask2Former).