dzy-cxy / Deep-Learning-Final-Project

NYU-Deep-Learning-Final-Project-S24

Push the Limit of Diabetic Retinopathy Detection

Requirements

• Python 3.6+
• PyTorch 1.6.0+

Usage

1. Train

python main.py

2. Test, visualization and verification

When your training is done, You can run the Jupyter notebook file project.ipynb with clear visualization plots and results.

Note

If you want to specify GPU to use, you should set environment variable CUDA_VISIBLE_DEVICES=0, for example.

References

• Krizhevsky, A., Hinton, G., & others. (2009). Learning multiple layers of features from tiny images. Toronto, ON, Canada.
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).
• DeVries, T., & Taylor, G. W. (2017). Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552.
• Loshchilov, I., & Hutter, F. (2016). Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983.
• Sanghyun, W., Jongchan, P., & others (2018). CBAM: Convolutional Block Attention Module. arXiv preprint arXiv:1807.06521v2