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425 lines
13 KiB
425 lines
13 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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## How do I use this model on an image?
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To load a pretrained model:
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```py
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>>> import timm
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>>> model = timm.create_model('hrnet_w18', pretrained=True)
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>>> model.eval()
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```
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To load and preprocess the image:
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```py
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>>> import urllib
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>>> from PIL import Image
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>>> from timm.data import resolve_data_config
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>>> from timm.data.transforms_factory import create_transform
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>>> config = resolve_data_config({}, model=model)
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>>> transform = create_transform(**config)
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>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
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>>> urllib.request.urlretrieve(url, filename)
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>>> img = Image.open(filename).convert('RGB')
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>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
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```
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To get the model predictions:
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```py
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>>> import torch
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>>> with torch.no_grad():
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... out = model(tensor)
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>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
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>>> print(probabilities.shape)
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>>> # prints: torch.Size([1000])
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```
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To get the top-5 predictions class names:
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```py
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>>> # Get imagenet class mappings
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>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
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>>> urllib.request.urlretrieve(url, filename)
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>>> with open("imagenet_classes.txt", "r") as f:
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... categories = [s.strip() for s in f.readlines()]
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>>> # Print top categories per image
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>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
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>>> for i in range(top5_prob.size(0)):
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... print(categories[top5_catid[i]], top5_prob[i].item())
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>>> # prints class names and probabilities like:
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>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
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```
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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.
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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.
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## How do I finetune this model?
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You can finetune any of the pre-trained models just by changing the classifier (the last layer).
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```py
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>>> model = timm.create_model('hrnet_w18', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
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```
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To finetune on your own dataset, you have to write a training loop or adapt [timm's training
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script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
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## How do I train this model?
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You can follow the [timm recipe scripts](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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Type: model-index
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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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Models:
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- Name: hrnet_w18
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In Collection: HRNet
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Metadata:
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FLOPs: 5547205500
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Parameters: 21300000
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File Size: 85718883
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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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Tasks:
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- Image Classification
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Training Techniques:
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 4x NVIDIA V100 GPUs
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ID: hrnet_w18
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Epochs: 100
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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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Batch Size: 256
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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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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w18-8cb57bb9.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 76.76%
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Top 5 Accuracy: 93.44%
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- Name: hrnet_w18_small
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In Collection: HRNet
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Metadata:
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FLOPs: 2071651488
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Parameters: 13190000
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File Size: 52934302
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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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Tasks:
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- Image Classification
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Training Techniques:
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 4x NVIDIA V100 GPUs
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ID: hrnet_w18_small
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Epochs: 100
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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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Batch Size: 256
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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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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnet_w18_small_v1-f460c6bc.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 72.34%
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Top 5 Accuracy: 90.68%
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- Name: hrnet_w18_small_v2
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In Collection: HRNet
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Metadata:
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FLOPs: 3360023160
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Parameters: 15600000
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File Size: 62682879
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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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Tasks:
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- Image Classification
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Training Techniques:
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 4x NVIDIA V100 GPUs
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ID: hrnet_w18_small_v2
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Epochs: 100
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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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Batch Size: 256
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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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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnet_w18_small_v2-4c50a8cb.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 75.11%
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Top 5 Accuracy: 92.41%
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- Name: hrnet_w30
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In Collection: HRNet
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Metadata:
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FLOPs: 10474119492
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Parameters: 37710000
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File Size: 151452218
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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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Tasks:
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- Image Classification
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Training Techniques:
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 4x NVIDIA V100 GPUs
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ID: hrnet_w30
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Epochs: 100
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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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Batch Size: 256
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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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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w30-8d7f8dab.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 78.21%
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Top 5 Accuracy: 94.22%
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- Name: hrnet_w32
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In Collection: HRNet
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Metadata:
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FLOPs: 11524528320
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Parameters: 41230000
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File Size: 165547812
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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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Tasks:
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- Image Classification
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Training Techniques:
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 4x NVIDIA V100 GPUs
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Training Time: 60 hours
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ID: hrnet_w32
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Epochs: 100
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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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Batch Size: 256
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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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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w32-90d8c5fb.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 78.45%
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Top 5 Accuracy: 94.19%
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- Name: hrnet_w40
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In Collection: HRNet
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Metadata:
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FLOPs: 16381182192
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Parameters: 57560000
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File Size: 230899236
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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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Tasks:
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- Image Classification
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Training Techniques:
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 4x NVIDIA V100 GPUs
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ID: hrnet_w40
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Epochs: 100
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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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Batch Size: 256
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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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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w40-7cd397a4.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 78.93%
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Top 5 Accuracy: 94.48%
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- Name: hrnet_w44
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In Collection: HRNet
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Metadata:
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FLOPs: 19202520264
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Parameters: 67060000
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File Size: 268957432
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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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Tasks:
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- Image Classification
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Training Techniques:
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 4x NVIDIA V100 GPUs
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ID: hrnet_w44
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Epochs: 100
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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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Batch Size: 256
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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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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w44-c9ac8c18.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 78.89%
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Top 5 Accuracy: 94.37%
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- Name: hrnet_w48
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In Collection: HRNet
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Metadata:
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FLOPs: 22285865760
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Parameters: 77470000
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File Size: 310603710
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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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Tasks:
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- Image Classification
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Training Techniques:
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 4x NVIDIA V100 GPUs
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Training Time: 80 hours
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ID: hrnet_w48
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Epochs: 100
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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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Batch Size: 256
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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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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w48-abd2e6ab.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 79.32%
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Top 5 Accuracy: 94.51%
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- Name: hrnet_w64
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In Collection: HRNet
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Metadata:
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FLOPs: 37239321984
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Parameters: 128060000
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File Size: 513071818
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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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Tasks:
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- Image Classification
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Training Techniques:
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- Nesterov Accelerated Gradient
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- Weight Decay
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Training Data:
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- ImageNet
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Training Resources: 4x NVIDIA V100 GPUs
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ID: hrnet_w64
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Epochs: 100
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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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Batch Size: 256
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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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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-hrnet/hrnetv2_w64-b47cc881.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 79.46%
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Top 5 Accuracy: 94.65%
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--> |