# sparse-structure-selection

**Repository Path**: rayufo/sparse-structure-selection

## Basic Information

- **Project Name**: sparse-structure-selection
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-10-27
- **Last Updated**: 2021-11-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# sparse-structure-selection

This code is a re-implementation of the imagenet classification experiments in the paper [Data-Driven Sparse Structure Selection for Deep Neural Networks
](https://arxiv.org/abs/1707.01213) (ECCV2018).

<div align="center">
  <img src="docs/sss.png" width="700px" />
</div>

## Citation
If you use our code in your research or wish to refer to the baseline results, please use the following BibTeX entry.
```
@article{SSS2018
  author =   {Zehao Huang and Naiyan Wang},
  title =    {Data-Driven Sparse Structure Selection for Deep Neural Networks},
  journal =  {ECCV},
  year =     {2018}
}
```

## Implementation
This code is implemented by a modified [MXNet](https://github.com/huangzehao/incubator-mxnet-bk) which supports [ResNeXt-like](https://github.com/facebookresearch/ResNeXt) augmentation. (This version of MXNet does not support cudnn7)

## ImageNet data preparation
Download the [ImageNet](http://image-net.org/download-images) dataset and create pass through rec (following [tornadomeet's repository](https://github.com/tornadomeet/ResNet#imagenet) but using unchange mode)

## Run
- modify ```config/cfgs.py```
- ```python train.py```

## Results on ImageNet-1k
<div align="left">
  <img src="docs/params.png" width="350px" />
  <img src="docs/flops.png" width="350px" />
</div>