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445 lines
12 KiB
445 lines
12 KiB
# ResNet
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**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual blocks](https://paperswithcode.com/method/residual-block) ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks.
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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('resnet18', 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. `resnet18`. 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](../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('resnet18', 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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@article{DBLP:journals/corr/HeZRS15,
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author = {Kaiming He and
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Xiangyu Zhang and
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Shaoqing Ren and
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Jian Sun},
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title = {Deep Residual Learning for Image Recognition},
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journal = {CoRR},
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volume = {abs/1512.03385},
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year = {2015},
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url = {http://arxiv.org/abs/1512.03385},
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archivePrefix = {arXiv},
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eprint = {1512.03385},
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timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
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biburl = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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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: ResNet
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Paper:
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Title: Deep Residual Learning for Image Recognition
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URL: https://paperswithcode.com/paper/deep-residual-learning-for-image-recognition
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Models:
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- Name: resnet18
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In Collection: ResNet
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Metadata:
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FLOPs: 2337073152
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Parameters: 11690000
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File Size: 46827520
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Bottleneck Residual Block
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- Convolution
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Data:
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- ImageNet
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ID: resnet18
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L641
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Weights: https://download.pytorch.org/models/resnet18-5c106cde.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: 69.74%
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Top 5 Accuracy: 89.09%
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- Name: resnet26
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In Collection: ResNet
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Metadata:
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FLOPs: 3026804736
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Parameters: 16000000
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File Size: 64129972
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Bottleneck Residual Block
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- Convolution
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Data:
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- ImageNet
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ID: resnet26
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L675
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26-9aa10e23.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: 75.29%
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Top 5 Accuracy: 92.57%
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- Name: resnet34
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In Collection: ResNet
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Metadata:
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FLOPs: 4718469120
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Parameters: 21800000
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File Size: 87290831
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Bottleneck Residual Block
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- Convolution
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Data:
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- ImageNet
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ID: resnet34
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L658
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34-43635321.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: 92.28%
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- Name: resnet50
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In Collection: ResNet
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Metadata:
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FLOPs: 5282531328
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Parameters: 25560000
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File Size: 102488165
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Bottleneck Residual Block
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- Convolution
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Data:
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- ImageNet
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ID: resnet50
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L691
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50_ram-a26f946b.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: 79.04%
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Top 5 Accuracy: 94.39%
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- Name: resnetblur50
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In Collection: ResNet
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Metadata:
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FLOPs: 6621606912
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Parameters: 25560000
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File Size: 102488165
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Blur Pooling
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- Bottleneck Residual Block
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- Convolution
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Data:
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- ImageNet
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ID: resnetblur50
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L1160
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnetblur50-84f4748f.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: 79.29%
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Top 5 Accuracy: 94.64%
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- Name: tv_resnet101
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In Collection: ResNet
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Metadata:
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FLOPs: 10068547584
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Parameters: 44550000
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File Size: 178728960
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Bottleneck Residual Block
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- Convolution
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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ID: tv_resnet101
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LR: 0.1
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Epochs: 90
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Crop Pct: '0.875'
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LR Gamma: 0.1
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Momentum: 0.9
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Batch Size: 32
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Image Size: '224'
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LR Step Size: 30
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L761
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Weights: https://download.pytorch.org/models/resnet101-5d3b4d8f.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: 77.37%
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Top 5 Accuracy: 93.56%
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- Name: tv_resnet152
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In Collection: ResNet
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Metadata:
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FLOPs: 14857660416
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Parameters: 60190000
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File Size: 241530880
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Bottleneck Residual Block
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- Convolution
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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ID: tv_resnet152
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LR: 0.1
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Epochs: 90
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Crop Pct: '0.875'
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LR Gamma: 0.1
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Momentum: 0.9
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Batch Size: 32
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Image Size: '224'
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LR Step Size: 30
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L769
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Weights: https://download.pytorch.org/models/resnet152-b121ed2d.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: 78.32%
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Top 5 Accuracy: 94.05%
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- Name: tv_resnet34
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In Collection: ResNet
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Metadata:
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FLOPs: 4718469120
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Parameters: 21800000
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File Size: 87306240
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Bottleneck Residual Block
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- Convolution
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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ID: tv_resnet34
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LR: 0.1
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Epochs: 90
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Crop Pct: '0.875'
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LR Gamma: 0.1
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Momentum: 0.9
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Batch Size: 32
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Image Size: '224'
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LR Step Size: 30
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L745
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Weights: https://download.pytorch.org/models/resnet34-333f7ec4.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: 73.3%
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Top 5 Accuracy: 91.42%
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- Name: tv_resnet50
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In Collection: ResNet
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Metadata:
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FLOPs: 5282531328
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Parameters: 25560000
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File Size: 102502400
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Bottleneck Residual Block
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- Convolution
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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ID: tv_resnet50
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LR: 0.1
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Epochs: 90
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Crop Pct: '0.875'
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LR Gamma: 0.1
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Momentum: 0.9
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Batch Size: 32
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Image Size: '224'
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LR Step Size: 30
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L753
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Weights: https://download.pytorch.org/models/resnet50-19c8e357.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: 76.16%
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Top 5 Accuracy: 92.88%
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--> |