CSC-Unet

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CSC-Unet 최신버젼 다운로드

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// repository documentation

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# CSC-Unet: A Novel Convolutional Sparse Coding Strategy based Neural Network for Semantic Segmentation

The implemented core codes of **CSC-Unet** are open here.

If you used our CSC-Unet codes, please cite our following papers:

Tang H, He S, Yang M, et al. (2024). CSC-Unet: A Novel Convolutional Sparse Coding Strategy based Neural Network for Semantic Segmentation. IEEE Access，doi：10.1109/ACCESS.2024.3373619.

Tang H, Shi J, Lu X, Yin Z, Huang L, Jia D, & Wang N. (2020, November). Comparison of Convolutional Sparse Coding Network and Convolutional Neural Network for Pavement Crack Classification: A Validation Study. In Journal of Physics: Conference Series (Vol. 1682, No. 1, p. 012016).

If you have any question or collaboration suggestion about our method, please contact wangnizhuan1120@gmail.com.

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).

## Usage for CSC-Unet

- 1. Clone this repo to local

```
git clone https://github.com/NZWANG/CSC-Unet.git
```

- 2. Download the experiment dataset from the link below, and put it into the directory: ```./Datasets/CamVid/```	```./Datasets/DeepCrack/```	```./Datasets/Nuclei/```
      1) 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/
      2)  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
      3)  Nuclei: https://www.kaggle.com/c/data-science-bowl-2018/overview  
- 3. Set the hyper parameters of the experiment in `cfg.py.`

- 4. Run the code by command

```bash
python train.py
```

CSC-Unet

CSC-Unet 최신버젼 다운로드

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