# SimpleNet

**Repository Path**: caomeng997/SimpleNet

## Basic Information

- **Project Name**: SimpleNet
- **Description**: SimpleNet-master
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-08-09
- **Last Updated**: 2023-08-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# SimpleNet


![](imgs/cover.png)

**SimpleNet: A Simple Network for Image Anomaly Detection and Localization**

*Zhikang Liu, Yiming Zhou, Yuansheng Xu, Zilei Wang**

Paper: [arxiv 2303.15140](https://arxiv.org/pdf/2303.15140.pdf)

##  Introduction

This repo contains source code for **SimpleNet** implemented with pytorch.

SimpleNet is a simple defect detection and localization network that built with a feature encoder, feature generator and defect discriminator. It is designed conceptionally simple without complex network deisng, training schemes or external data source.

## Get Started 

### Environment 

**Python3.8**

**Packages**:
- torch==1.12.1
- torchvision==0.13.1
- numpy==1.22.4
- opencv-python==4.5.1

(Above environment setups are not the minimum requiremetns, other versions might work too.)


### Data

Edit `run.sh` to edit dataset class and dataset path.

#### MvTecAD

Download the dataset from [here](https://www.mvtec.com/company/research/datasets/mvtec-ad/).

The dataset folders/files follow its original structure.

### Run

#### Demo train

Please specicy dataset path (line1) and log folder (line10) in `run.sh` before running.

`run.sh` gives the configuration to train models on MVTecAD dataset.
```
bash run.sh
```

## Citation
```
@inproceedings{liu2023simplenet,
  title={SimpleNet: A Simple Network for Image Anomaly Detection and Localization},
  author={Liu, Zhikang and Zhou, Yiming and Xu, Yuansheng and Wang, Zilei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={20402--20411},
  year={2023}
}
```

## Acknowledgement

Thanks for great inspiration from [PatchCore](https://github.com/amazon-science/patchcore-inspection)

## License

All code within the repo is under [MIT license](https://mit-license.org/)