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ConvLSTM.pytorch

This repository is an unofficial pytorch implementation of Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. We reimplement the experiments in the paper based on the MovingMNIST dataset, which is followed by Github. Detailed understanding is available on my Blog.

Requirements

  • Pytorch>=0.4.0
  • CPU or GPU
  • Other packages can be installed with the following instruction:
pip install requirements.txt

Quick start

Running the code with the following command, and the '--config' parameter represents different network architectures.

python main.py --config 3x3_16_3x3_32_3x3_64

Results

Model Parameters(M) Flops(G) DiceLoss
3x3_16_3x3_32_3x3_64 0.61 9.19 0.682311
3x3_32_3x3_64_3x3_128 2.45 36.35 0.665905
  • Note: In order to reduce the number of parameters and flops, we did not strictly follow the model architecture in the paper, but modified it into unet style structure. result1 result2

Citation

@inproceedings{xingjian2015convolutional,
  title={Convolutional LSTM network: A machine learning approach for precipitation nowcasting},
  author={Xingjian, SHI and Chen, Zhourong and Wang, Hao and Yeung, Dit-Yan and Wong, Wai-Kin and Woo, Wang-chun},
  booktitle={Advances in neural information processing systems},
  pages={802--810},
  year={2015}
}

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  • Jupyter Notebook 95.3%
  • Python 4.7%