[Learning Crisp Boundaries Using Deep Refinement Network and Adaptive Weighting Loss]

Citations

If you are using the code/model/data provided here in a publication, please consider citing our paper:

@article{cao2020learning,  
    author={Y. -J. {Cao} and C. {Lin} and Y. -J. {Li}},
    journal={IEEE Transactions on Multimedia}, 
    title={Learning Crisp Boundaries Using Deep Refinement Network and Adaptive Weighting Loss}, 
    year={2021},
    volume={23},
    number={},
    pages={761-771},
    doi={10.1109/TMM.2020.2987685}
} 

Precomputed results

Evaluation results for BSDS500 and NYUD datasets are available here.

For plot PR-curve or UCM, you can use here.

Pretrained models

Pretrained models are available here.

Testing

1. Clone the repository

2. Download pretrained models, and put them into $ROOT_DIR/$MODEL_NAME/ folder.

3. Download the datasets you need (you can download from RCF page), and modify the cfgs.yaml file.

4. run test_bsds.py or test_nyud.py.

Note: Before evaluating the predicted edges, you should do the standard non-maximum suppression (NMS) and edge thinning. We used Piotr's Structured Forest matlab toolbox available here.

Training

1. Download the datasets you need.

2. run train_bsds.py or train_nyud.py.

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