An Implementation of HRNets for Image Classification

Note

• Please refer to https://github.com/HRNet/HRNet-Image-Classification/ for the official implementation.

ImageNet-1K Accuracy

Model	Resolution	#Params	FLOPs	#Epochs	Top-1 Acc(%)	Pretrained Weights
HRNet-W18	224x224	21.3M	3.99G	300	78.6	hrnet18-6ca9d2049.pth
HRNet-W18	224x224	21.3M	3.99G	600	79.4	hrnet18-699e7ab89.pth
HRNet-W32	224x224	41.2M	8.31G	300	80.5	hrnet32-21df535e7.pth
HRNet-W32	224x224	41.2M	8.31G	600	81.2	hrnet32-9f864d2d6.pth

Training Example

• All models are trained using a similar strategy to DeiT.
• Environment: pytorch==1.10.0, timm==0.5.0.
• Training codes are modified from https://github.com/rwightman/pytorch-image-models/.

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./distributed_train.sh 8 /path/to/imagenet/ --model hrnet32 --amp

Reference

@article{wang2020deep,
  title={Deep High-Resolution Representation Learning for Visual Recognition},
  author={Jingdong Wang and Ke Sun and Tianheng Cheng and 
          Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and 
          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
  journal= {TPAMI}
  year={2020}
}