wy3406 / HRNet-Image-Classification

Train the HRNet model on ImageNet

High-resolution networks (HRNets) for Image classification

Introduction

This is the official code of high-resolution representations for ImageNet classification. 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.

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/BaiduYun(Access Code:r5xn)
HRNet-W30-C	37.7M	7.55	21.8%	5.8%	OneDrive/BaiduYun(Access Code:ajc1)
HRNet-W32-C	41.2M	8.31	21.5%	5.8%	OneDrive/BaiduYun(Access Code:itc1)
HRNet-W40-C	57.6M	11.8	21.1%	5.5%	OneDrive/BaiduYun(Access Code:i58x)
HRNet-W44-C	67.1M	13.9	21.1%	5.6%	OneDrive/BaiduYun(Access Code:3imd)
HRNet-W48-C	77.5M	16.1	20.7%	5.5%	OneDrive/BaiduYun(Access Code:68g2)
HRNet-W64-C	128.1M	26.9	20.5%	5.4%	OneDrive/BaiduYun(Access Code:6kw4)

Quick start

Install

1. Install PyTorch=0.4.1 following the official instructions
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:

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:

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
• Semantic segmentation
• Object detection
• 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