# ShuffleNetV2-pytorch

**Repository Path**: beijieer/ShuffleNetV2-pytorch

## Basic Information

- **Project Name**: ShuffleNetV2-pytorch
- **Description**: Implementation of ShuffleNetV2 for pytorch
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 2
- **Created**: 2021-11-03
- **Last Updated**: 2021-11-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ShuffleNetv2 in PyTorch

An implementation of `ShuffleNetv2` in PyTorch. `ShuffleNetv2` is an efficient convolutional neural network architecture for mobile devices. For more information check the paper:
[ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://arxiv.org/abs/1807.11164)

## Usage

Clone the repo:
```bash
git clone https://github.com/Randl/ShuffleNetV2-pytorch
pip install -r requirements.txt
```

Use the model defined in `model.py` to run ImageNet example:
```bash
python imagenet.py --dataroot "/path/to/imagenet/"
```

To continue training from checkpoint
```bash
python imagenet.py --dataroot "/path/to/imagenet/" --resume "/path/to/checkpoint/folder"
```
## Results


For x0.5 model I achieved 0.4% lower top-1 accuracy than claimed.

|Classification Checkpoint| MACs (M)   | Parameters (M)| Top-1 Accuracy| Top-5 Accuracy|  Claimed top-1|  Claimed top-5|
|-------------------------|------------|---------------|---------------|---------------|---------------|---------------|
|      [shufflenet_v2_0.5]|41          |1.37           |          59.86|          81.63|           60.3|              -|

You can test it with
```bash
python imagenet.py --dataroot "/path/to/imagenet/" --resume "results/shufflenet_v2_0.5/model_best.pth.tar" -e --scaling 0.5
```