CSC-Unet: A Novel Convolutional Sparse Coding Strategy based Neural Network for Semantic Segmentation
2023年2月11日12:35:34
The implemented core codes of CSC-Unet are open here.
If you used our CSC-Unet codes, please cite our paper:
Tang H, He S, Lu X, et al. CSC-Unet: A Novel Convolutional Sparse Coding Strategy based Neural Network for Semantic Segmentation[J]. arXiv preprint arXiv:2108.00408, 2021. https://arxiv.org/abs/2108.00408
If you have any question or collaboration suggestion about our method, please contact [email protected].
The codes of various networks were tested in Pytorch 1.5 version or higher versions(a little bit different from 0.8 version in some functions) in Python 3.8 on Ubuntu machines (may need minor changes on Windows).
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- Clone this repo to local
git clone https://github.com/NZWANG/CSC-Unet.git
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- Download the experiment dataset from the link below, and put it into the directory:
./Datasets/CamVid/
./Datasets/DeepCrack/
./Datasets/Nuclei/
- CamVid: G. J. Brostow, J. Shotton, J. Fauqueur, and R. Cipolla, “Segmentation and recognition using structure from motion point clouds” http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/
- DeepCrack: Y. Liu, J. Yao, X. Lu, R. Xie, and L. Li, “DeepCrack: A deep hierarchical feature learning architecture for crack segmentation” https://github.com/yhlleo/DeepCrack
- Nuclei: https://www.kaggle.com/c/data-science-bowl-2018/overview
- Download the experiment dataset from the link below, and put it into the directory:
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- Set the hyper parameters of the experiment in
cfg.py.
- Set the hyper parameters of the experiment in
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- run the code by command
python train.py