# HRNet-Image-Classification

**Repository Path**: xuzhiyang483/HRNet-Image-Classification

## Basic Information

- **Project Name**: HRNet-Image-Classification
- **Description**: Train the HRNet model on ImageNet
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2019-12-28
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# High-resolution networks (HRNets) for Image classification

## Introduction
This is the official code of [high-resolution representations for ImageNet classification](https://arxiv.org/abs/1904.04514). 
We augment the HRNet with a classification head shown in the figure below. First, the four-resolution feature maps are fed into a bottleneck and the number of output channels are increased to 128, 256, 512, and 1024, respectively. Then, we downsample the high-resolution representations by a 2-strided 3x3 convolution outputting 256 channels and add them to the representations of the second-high-resolution representations. This process is repeated two times to get 1024 channels over the small resolution. Last, we transform 1024 channels to 2048 channels through a 1x1 convolution, followed by a global average pooling operation. The output 2048-dimensional representation is fed into the classifier.

![](figures/cls-hrnet.png)

## ImageNet pretrained models
HRNetV2 ImageNet pretrained models are now available!

| model |#Params | GFLOPs |top-1 error| top-5 error| Link |
| :--: | :--: | :--: | :--: | :--: | :--: |
| HRNet-W18-C | 21.3M | 3.99 | 23.2% | 6.6% |[OneDrive](https://1drv.ms/u/s!Aus8VCZ_C_33cMkPimlmClRvmpw)/[BaiduYun(Access Code:r5xn)](https://pan.baidu.com/s/1Px_g1E2BLVRkKC5t-b-R5Q)|
| HRNet-W30-C | 37.7M | 7.55 | 21.8% | 5.8% |[OneDrive](https://1drv.ms/u/s!Aus8VCZ_C_33cQoACCEfrzcSaVI)/[BaiduYun(Access Code:ajc1)](https://pan.baidu.com/s/1yEz7hKaJT-H7eHLteAotbQ)|
| HRNet-W32-C | 41.2M | 8.31 | 21.5% | 5.8% |[OneDrive](https://1drv.ms/u/s!Aus8VCZ_C_33dYBMemi9xOUFR0w)/[BaiduYun(Access Code:itc1)](https://pan.baidu.com/s/1xn92PSCg5KtXkKcnnLOycw)||
| HRNet-W40-C | 57.6M | 11.8 | 21.1% | 5.5% |[OneDrive](https://1drv.ms/u/s!Aus8VCZ_C_33ck0gvo5jfoWBOPo)/[BaiduYun(Access Code:i58x)](https://pan.baidu.com/s/1DD3WKxgLM1jawR87WdAtsw)|
| HRNet-W44-C | 67.1M | 13.9 | 21.1% | 5.6% |[OneDrive](https://1drv.ms/u/s!Aus8VCZ_C_33czZQ0woUb980gRs)/[BaiduYun(Access Code:3imd)](https://pan.baidu.com/s/1F679dvz9iJ8aFAp6YKr9Rw)|
| HRNet-W48-C | 77.5M | 16.1 | 20.7% | 5.5% |[OneDrive](https://1drv.ms/u/s!Aus8VCZ_C_33dKvqI6pBZlifgJk)/[BaiduYun(Access Code:68g2)](https://pan.baidu.com/s/13b8srQn8ARF9zHsaxvpRWA)|
| HRNet-W64-C | 128.1M | 26.9 | 20.5% | 5.4% |[OneDrive](https://1drv.ms/u/s!Aus8VCZ_C_33gQbJsUPTIj3rQu99)/[BaiduYun(Access Code:6kw4)](https://pan.baidu.com/s/16ycW99VAYat3fHjgKpUXvQ)|


## Quick start
### Install
1. Install PyTorch=0.4.1 following the [official instructions](https://pytorch.org/)
2. git clone https://github.com/HRNet/HRNet-Image-Classification
3. Install dependencies: pip install -r requirements.txt

### Data preparation
You can follow the Pytorch implementation:
https://github.com/pytorch/examples/tree/master/imagenet

The data should be under ./data/imagenet/images/.

### Train and test
Please specify the configuration file.

For example, train the HRNet-W18 on ImageNet with a batch size of 128 on 4 GPUs:
````bash
python tools/train.py --cfg experiments/cls_hrnet_w18_sgd_lr5e-2_wd1e-4_bs32_x100.yaml
````

For example, test the HRNet-W18 on ImageNet on 4 GPUs:
````bash
python tools/valid.py --cfg experiments/cls_hrnet_w18_sgd_lr5e-2_wd1e-4_bs32_x100.yaml --testModel hrnetv2_w18_imagenet_pretrained.pth
````

## Other applications of HRNet
* [Human pose estimation](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch)
* [Semantic segmentation](https://github.com/HRNet/HRNet-Semantic-Segmentation)
* [Object detection](https://github.com/HRNet/HRNet-Object-Detection)
* [Facial landmark detection](https://github.com/HRNet/HRNet-Facial-Landmark-Detection)

## Citation
If you find this work or code is helpful in your research, please cite:
````
@inproceedings{SunXLW19,
  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
  author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
  booktitle={CVPR},
  year={2019}
}

@article{SunZJCXLMWLW19,
  title={High-Resolution Representations for Labeling Pixels and Regions},
  author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao 
  and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang},
  journal   = {CoRR},
  volume    = {abs/1904.04514},
  year={2019}
}
````

## Reference
[1] Deep High-Resolution Representation Learning for Human Pose Estimation. Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. CVPR 2019. [download](https://arxiv.org/pdf/1902.09212.pdf)