You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
419 lines
13 KiB
419 lines
13 KiB
# Summary
|
|
|
|
**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).reset_classifier(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%
|
|
--> |