# 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. ## How do I use this model on an image? To load a pretrained model: ```python import timm model = timm.create_model('hrnet_w18', pretrained=True) model.eval() ``` To load and preprocess the image: ```python import urllib from PIL import Image from timm.data import resolve_data_config from timm.data.transforms_factory import create_transform config = resolve_data_config({}, model=model) transform = create_transform(**config) url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg") urllib.request.urlretrieve(url, filename) img = Image.open(filename).convert('RGB') tensor = transform(img).unsqueeze(0) # transform and add batch dimension ``` To get the model predictions: ```python import torch with torch.no_grad(): out = model(tensor) probabilities = torch.nn.functional.softmax(out[0], dim=0) print(probabilities.shape) # prints: torch.Size([1000]) ``` To get the top-5 predictions class names: ```python # Get imagenet class mappings url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt") urllib.request.urlretrieve(url, filename) with open("imagenet_classes.txt", "r") as f: categories = [s.strip() for s in f.readlines()] # Print top categories per image top5_prob, top5_catid = torch.topk(probabilities, 5) for i in range(top5_prob.size(0)): print(categories[top5_catid[i]], top5_prob[i].item()) # prints class names and probabilities like: # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)] ``` Replace the model name with the variant you want to use, e.g. `hrnet_w18`. You can find the IDs in the model summaries at the top of this page. To extract image features with this model, follow the [timm feature extraction examples](https://rwightman.github.io/pytorch-image-models/feature_extraction/), just change the name of the model you want to use. ## How do I finetune this model? You can finetune any of the pre-trained models just by changing the classifier (the last layer). ```python model = timm.create_model('hrnet_w18', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) ``` To finetune on your own dataset, you have to write a training loop or adapt [timm's training script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset. ## How do I train this model? You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh. ## Citation ```BibTeX @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} } ``` <!-- Type: model-index Collections: - Name: HRNet Paper: Title: Deep High-Resolution Representation Learning for Visual Recognition URL: https://paperswithcode.com/paper/190807919 Models: - Name: hrnet_w18 In Collection: HRNet Metadata: FLOPs: 5547205500 Parameters: 21300000 File Size: 85718883 Architecture: - Batch Normalization - Convolution - ReLU - Residual Connection Tasks: - Image Classification Training Techniques: - Nesterov Accelerated Gradient - Weight Decay Training Data: - ImageNet Training Resources: 4x NVIDIA V100 GPUs ID: hrnet_w18 Epochs: 100 Layers: 18 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 256 Image Size: '224' Weight Decay: 0.001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L800 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w18-8cb57bb9.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 76.76% Top 5 Accuracy: 93.44% - Name: hrnet_w18_small In Collection: HRNet Metadata: FLOPs: 2071651488 Parameters: 13190000 File Size: 52934302 Architecture: - Batch Normalization - Convolution - ReLU - Residual Connection Tasks: - Image Classification Training Techniques: - Nesterov Accelerated Gradient - Weight Decay Training Data: - ImageNet Training Resources: 4x NVIDIA V100 GPUs ID: hrnet_w18_small Epochs: 100 Layers: 18 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 256 Image Size: '224' Weight Decay: 0.001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L790 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnet_w18_small_v1-f460c6bc.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 72.34% Top 5 Accuracy: 90.68% - Name: hrnet_w18_small_v2 In Collection: HRNet Metadata: FLOPs: 3360023160 Parameters: 15600000 File Size: 62682879 Architecture: - Batch Normalization - Convolution - ReLU - Residual Connection Tasks: - Image Classification Training Techniques: - Nesterov Accelerated Gradient - Weight Decay Training Data: - ImageNet Training Resources: 4x NVIDIA V100 GPUs ID: hrnet_w18_small_v2 Epochs: 100 Layers: 18 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 256 Image Size: '224' Weight Decay: 0.001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L795 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnet_w18_small_v2-4c50a8cb.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 75.11% Top 5 Accuracy: 92.41% - Name: hrnet_w30 In Collection: HRNet Metadata: FLOPs: 10474119492 Parameters: 37710000 File Size: 151452218 Architecture: - Batch Normalization - Convolution - ReLU - Residual Connection Tasks: - Image Classification Training Techniques: - Nesterov Accelerated Gradient - Weight Decay Training Data: - ImageNet Training Resources: 4x NVIDIA V100 GPUs ID: hrnet_w30 Epochs: 100 Layers: 30 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 256 Image Size: '224' Weight Decay: 0.001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L805 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w30-8d7f8dab.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.21% Top 5 Accuracy: 94.22% - Name: hrnet_w32 In Collection: HRNet Metadata: FLOPs: 11524528320 Parameters: 41230000 File Size: 165547812 Architecture: - Batch Normalization - Convolution - ReLU - Residual Connection Tasks: - Image Classification Training Techniques: - Nesterov Accelerated Gradient - Weight Decay Training Data: - ImageNet Training Resources: 4x NVIDIA V100 GPUs Training Time: 60 hours ID: hrnet_w32 Epochs: 100 Layers: 32 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 256 Image Size: '224' Weight Decay: 0.001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L810 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w32-90d8c5fb.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.45% Top 5 Accuracy: 94.19% - Name: hrnet_w40 In Collection: HRNet Metadata: FLOPs: 16381182192 Parameters: 57560000 File Size: 230899236 Architecture: - Batch Normalization - Convolution - ReLU - Residual Connection Tasks: - Image Classification Training Techniques: - Nesterov Accelerated Gradient - Weight Decay Training Data: - ImageNet Training Resources: 4x NVIDIA V100 GPUs ID: hrnet_w40 Epochs: 100 Layers: 40 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 256 Image Size: '224' Weight Decay: 0.001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L815 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w40-7cd397a4.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.93% Top 5 Accuracy: 94.48% - Name: hrnet_w44 In Collection: HRNet Metadata: FLOPs: 19202520264 Parameters: 67060000 File Size: 268957432 Architecture: - Batch Normalization - Convolution - ReLU - Residual Connection Tasks: - Image Classification Training Techniques: - Nesterov Accelerated Gradient - Weight Decay Training Data: - ImageNet Training Resources: 4x NVIDIA V100 GPUs ID: hrnet_w44 Epochs: 100 Layers: 44 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 256 Image Size: '224' Weight Decay: 0.001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L820 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w44-c9ac8c18.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 78.89% Top 5 Accuracy: 94.37% - Name: hrnet_w48 In Collection: HRNet Metadata: FLOPs: 22285865760 Parameters: 77470000 File Size: 310603710 Architecture: - Batch Normalization - Convolution - ReLU - Residual Connection Tasks: - Image Classification Training Techniques: - Nesterov Accelerated Gradient - Weight Decay Training Data: - ImageNet Training Resources: 4x NVIDIA V100 GPUs Training Time: 80 hours ID: hrnet_w48 Epochs: 100 Layers: 48 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 256 Image Size: '224' Weight Decay: 0.001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L825 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w48-abd2e6ab.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.32% Top 5 Accuracy: 94.51% - Name: hrnet_w64 In Collection: HRNet Metadata: FLOPs: 37239321984 Parameters: 128060000 File Size: 513071818 Architecture: - Batch Normalization - Convolution - ReLU - Residual Connection Tasks: - Image Classification Training Techniques: - Nesterov Accelerated Gradient - Weight Decay Training Data: - ImageNet Training Resources: 4x NVIDIA V100 GPUs ID: hrnet_w64 Epochs: 100 Layers: 64 Crop Pct: '0.875' Momentum: 0.9 Batch Size: 256 Image Size: '224' Weight Decay: 0.001 Interpolation: bilinear Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/hrnet.py#L830 Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w64-b47cc881.pth Results: - Task: Image Classification Dataset: ImageNet Metrics: Top 1 Accuracy: 79.46% Top 5 Accuracy: 94.65% -->