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304 lines
8.1 KiB
304 lines
8.1 KiB
# HRNet
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**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.
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{% include 'code_snippets.md' %}
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## How do I train this model?
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You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
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## Citation
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```BibTeX
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@misc{sun2019highresolution,
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title={High-Resolution Representations for Labeling Pixels and Regions},
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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},
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year={2019},
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eprint={1904.04514},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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<!--
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Models:
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- Name: hrnet_w18_small
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Metadata:
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FLOPs: 2071651488
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Epochs: 100
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Batch Size: 256
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Training Data:
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- ImageNet
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Training Techniques:
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Resources: 4x NVIDIA V100 GPUs
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Architecture:
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- Batch Normalization
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- Convolution
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- ReLU
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- Residual Connection
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File Size: 52934302
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Tasks:
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- Image Classification
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Training Time: ''
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ID: hrnet_w18_small
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Layers: 18
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 0.001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L790
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Config: ''
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In Collection: HRNet
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- Name: hrnet_w18_small_v2
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Metadata:
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FLOPs: 3360023160
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Epochs: 100
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Batch Size: 256
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Training Data:
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- ImageNet
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Training Techniques:
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Resources: 4x NVIDIA V100 GPUs
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Architecture:
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- Batch Normalization
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- Convolution
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- ReLU
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- Residual Connection
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File Size: 62682879
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Tasks:
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- Image Classification
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Training Time: ''
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ID: hrnet_w18_small_v2
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Layers: 18
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 0.001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L795
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Config: ''
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In Collection: HRNet
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- Name: hrnet_w32
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Metadata:
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FLOPs: 11524528320
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Epochs: 100
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Batch Size: 256
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Training Data:
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- ImageNet
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Training Techniques:
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Resources: 4x NVIDIA V100 GPUs
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Architecture:
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- Batch Normalization
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- Convolution
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- ReLU
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- Residual Connection
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File Size: 165547812
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Tasks:
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- Image Classification
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Training Time: 60 hours
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ID: hrnet_w32
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Layers: 32
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 0.001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L810
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Config: ''
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In Collection: HRNet
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- Name: hrnet_w40
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Metadata:
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FLOPs: 16381182192
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Epochs: 100
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Batch Size: 256
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Training Data:
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- ImageNet
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Training Techniques:
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Resources: 4x NVIDIA V100 GPUs
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Architecture:
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- Batch Normalization
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- Convolution
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- ReLU
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- Residual Connection
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File Size: 230899236
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Tasks:
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- Image Classification
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Training Time: ''
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ID: hrnet_w40
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Layers: 40
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 0.001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L815
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Config: ''
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In Collection: HRNet
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- Name: hrnet_w44
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Metadata:
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FLOPs: 19202520264
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Epochs: 100
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Batch Size: 256
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Training Data:
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- ImageNet
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Training Techniques:
|
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Resources: 4x NVIDIA V100 GPUs
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Architecture:
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- Batch Normalization
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- Convolution
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- ReLU
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- Residual Connection
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File Size: 268957432
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Tasks:
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- Image Classification
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Training Time: ''
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ID: hrnet_w44
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Layers: 44
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 0.001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L820
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Config: ''
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In Collection: HRNet
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- Name: hrnet_w48
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Metadata:
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FLOPs: 22285865760
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Epochs: 100
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Batch Size: 256
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Training Data:
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- ImageNet
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Training Techniques:
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Resources: 4x NVIDIA V100 GPUs
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Architecture:
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- Batch Normalization
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- Convolution
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- ReLU
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- Residual Connection
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File Size: 310603710
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Tasks:
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- Image Classification
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Training Time: 80 hours
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ID: hrnet_w48
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Layers: 48
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 0.001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L825
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Config: ''
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In Collection: HRNet
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- Name: hrnet_w18
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Metadata:
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FLOPs: 5547205500
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Epochs: 100
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Batch Size: 256
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Training Data:
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- ImageNet
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Training Techniques:
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Resources: 4x NVIDIA V100 GPUs
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Architecture:
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- Batch Normalization
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- Convolution
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- ReLU
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- Residual Connection
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File Size: 85718883
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Tasks:
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- Image Classification
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Training Time: ''
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ID: hrnet_w18
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Layers: 18
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 0.001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L800
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Config: ''
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In Collection: HRNet
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- Name: hrnet_w64
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Metadata:
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FLOPs: 37239321984
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Epochs: 100
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Batch Size: 256
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Training Data:
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- ImageNet
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Training Techniques:
|
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Resources: 4x NVIDIA V100 GPUs
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Architecture:
|
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- Batch Normalization
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- Convolution
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- ReLU
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- Residual Connection
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File Size: 513071818
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Tasks:
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- Image Classification
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Training Time: ''
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ID: hrnet_w64
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Layers: 64
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 0.001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L830
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Config: ''
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In Collection: HRNet
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- Name: hrnet_w30
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Metadata:
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FLOPs: 10474119492
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Epochs: 100
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Batch Size: 256
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Training Data:
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- ImageNet
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Training Techniques:
|
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Resources: 4x NVIDIA V100 GPUs
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Architecture:
|
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- Batch Normalization
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- Convolution
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- ReLU
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- Residual Connection
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File Size: 151452218
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Tasks:
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- Image Classification
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Training Time: ''
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ID: hrnet_w30
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Layers: 30
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 0.001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L805
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Config: ''
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In Collection: HRNet
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Collections:
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- Name: HRNet
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Paper:
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title: Deep High-Resolution Representation Learning for Visual Recognition
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url: https://paperswithcode.com//paper/190807919
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type: model-index
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Type: model-index
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-->
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