pytorch-image-models/docs/models/.templates/models/hrnet.md

HRNet

HRNet, or High-Resolution Net, is a general purpose convolutional neural network for tasks like semantic segmentation, object detection and image classification. It is able to maintain high resolution representations through the whole process. We start from a high-resolution convolution stream, gradually add high-to-low resolution convolution streams one by one, and connect the multi-resolution streams in parallel. The resulting network consists of several (4 in the paper) stages and the $n$th stage contains n streams corresponding to n resolutions. The authors conduct repeated multi-resolution fusions by exchanging the information across the parallel streams over and over.

{% include 'code_snippets.md' %}

How do I train this model?

You can follow the timm recipe scripts for training a new model afresh.

Citation

@misc{sun2019highresolution,
      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},
      year={2019},
      eprint={1904.04514},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}