# DeepCompression-PyTorch

**Repository Path**: gvraky/DeepCompression-PyTorch

## Basic Information

- **Project Name**: DeepCompression-PyTorch
- **Description**: Resnet剪枝
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-23
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# [Learning both Weights and Connections for Efficient Neural Networks](https://arxiv.org/abs/1506.02626)

A PyTorch implementation of [this paper](https://arxiv.org/abs/1506.02626).

I'm currently in the process of updating this to work with the latest version of PyTorch! Currently the only network type that works is ResNet - other networks coming soon. 

To run, try:
```bash
python train.py --model='resnet34' --checkpoint='resnet34'
python prune.py --model='resnet34' --checkpoint='resnet34'
```

## Summary

Given a family of ResNets, we can construct a Pareto frontier of the tradeoff between accuracy and number of parameters:

![alt text](./resources/resnets.png)

Han et al. posit that we can beat this Pareto frontier by leaving network structures fixed, but removing individual parameters:

![alt text](./resources/pareto.png)