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