If you are interested in ML model serving, feel free to have a look at my another project on model serving framework!

---

dance

A Deep Attentive Contour Model for Efficient Instance Segmentation (PDF)

DANCE's Pipeline	Illustration

note

The codes in master branch are mainly for experiments on COCO; if you want to have a look on the codes for experiments on SBD / Cityscapes, welcome to checkout the snake branch, which is developed based on the codebase of previous art.

Get started

1. Prepare the environment (the scripts are just examples)
   • gcc & g++ ≥ 5
   • Python 3.6.8 (developed & tested on this version)
     • conda create --name dance python==3.6.8
     • conda deactivate && conda activate dance
   • PyTorch 1.4 with CUDA 10.1
     • conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.1 -c pytorch
2. Clone this project and install framework / package dependency
   • clone dance and install dependencies: git clone https://github.com/lkevinzc/dance && cd dance && pip install -r requirements.txt && cd ..
   • clone Detectron2 and install v0.1: git clone https://github.com/facebookresearch/detectron2.git && cd detectron2 && git checkout 1a7daee064eeca2d7fddce4ba74b74183ba1d4a0 && python -m pip install -e . && cd ..
   • install cpp utils: cd dance/core/layers/extreme_utils && export CUDA_HOME="/usr/local/cuda-10.1" && python setup.py build_ext --inplace
   • install pycocotools: pip install cython; pip install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
   • fix the fvcore version: pip install fvcore==0.1.1.dev200512
3. Prepare dataset
   • Download form COCO official website
   • put it at dance/datasets/coco
4. Download pre-trained model (metrics on COCO test-dev)

model name	AP	AP50	AP75	weights
dance_r50_3x	36.8	58.5	39.0	link
dance_r101_3x	38.1	60.2	40.5	link

note: put them under output/

Evaluation

python train_net.py --config-file configs/Dance_R_50_3x.yaml --eval-only MODEL.WEIGHTS ./output/r50_3x_model_final.pth

python train_net.py --config-file configs/Dance_R_101_3x.yaml --eval-only MODEL.WEIGHTS ./output/r101_3x_model_final.pth

Discussion

Any discussion or suggestion is welcome! Feel free to contact the author via liuzichen@u.nus.edu :)

Citation

If you find this project helpful for your research, please consider citing using BibTeX below:

@InProceedings{liu2021dance,
    author    = {Liu, Zichen and Liew, Jun Hao and Chen, Xiangyu and Feng, Jiashi},
    title     = {DANCE: A Deep Attentive Contour Model for Efficient Instance Segmentation},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2021},
    pages     = {345-354}
}