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454 lines
13 KiB
454 lines
13 KiB
3 years ago
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# Summary
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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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The weights from this model were ported from Gluon.
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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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```python
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import timm
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model = timm.create_model('gluon_resnet101_v1b', 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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```python
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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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```python
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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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```python
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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. `gluon_resnet101_v1b`. 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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```python
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model = timm.create_model('gluon_resnet101_v1b', pretrained=True).reset_classifier(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](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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@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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Models:
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- Name: gluon_resnet101_v1b
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Metadata:
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FLOPs: 10068547584
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Training Data:
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- ImageNet
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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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File Size: 178723172
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Tasks:
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- Image Classification
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ID: gluon_resnet101_v1b
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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/gluon_resnet.py#L89
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In Collection: Gloun ResNet
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- Name: gluon_resnet101_v1s
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Metadata:
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FLOPs: 11805511680
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Training Data:
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- ImageNet
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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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File Size: 179221777
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Tasks:
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- Image Classification
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ID: gluon_resnet101_v1s
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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/gluon_resnet.py#L166
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In Collection: Gloun ResNet
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- Name: gluon_resnet101_v1c
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Metadata:
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FLOPs: 10376567296
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Training Data:
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- ImageNet
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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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File Size: 178802575
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Tasks:
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- Image Classification
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ID: gluon_resnet101_v1c
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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/gluon_resnet.py#L113
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In Collection: Gloun ResNet
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- Name: gluon_resnet152_v1c
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Metadata:
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FLOPs: 15165680128
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Training Data:
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- ImageNet
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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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File Size: 241613404
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Tasks:
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- Image Classification
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ID: gluon_resnet152_v1c
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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/gluon_resnet.py#L121
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In Collection: Gloun ResNet
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- Name: gluon_resnet152_v1b
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Metadata:
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FLOPs: 14857660416
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Training Data:
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- ImageNet
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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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File Size: 241534001
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Tasks:
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- Image Classification
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ID: gluon_resnet152_v1b
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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/gluon_resnet.py#L97
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In Collection: Gloun ResNet
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- Name: gluon_resnet101_v1d
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Metadata:
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FLOPs: 10377018880
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Training Data:
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- ImageNet
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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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File Size: 178802755
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Tasks:
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- Image Classification
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ID: gluon_resnet101_v1d
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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/gluon_resnet.py#L138
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In Collection: Gloun ResNet
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- Name: gluon_resnet152_v1d
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Metadata:
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FLOPs: 15166131712
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Training Data:
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- ImageNet
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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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File Size: 241613584
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Tasks:
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- Image Classification
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ID: gluon_resnet152_v1d
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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/gluon_resnet.py#L147
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In Collection: Gloun ResNet
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|
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- Name: gluon_resnet152_v1s
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Metadata:
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FLOPs: 16594624512
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Training Data:
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- ImageNet
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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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|
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- ReLU
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|
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- Residual Block
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||
|
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- Residual Connection
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||
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- Softmax
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||
|
|
File Size: 242032606
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||
|
|
Tasks:
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||
|
|
- Image Classification
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||
|
|
ID: gluon_resnet152_v1s
|
||
|
|
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/gluon_resnet.py#L175
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|
|
In Collection: Gloun ResNet
|
||
|
|
- Name: gluon_resnet50_v1b
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||
|
|
Metadata:
|
||
|
|
FLOPs: 5282531328
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||
|
|
Training Data:
|
||
|
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- ImageNet
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|
|
Architecture:
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|
|
- 1x1 Convolution
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|
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- Batch Normalization
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|
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- Bottleneck Residual Block
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|
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- Convolution
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|
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- Global Average Pooling
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||
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- Max Pooling
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||
|
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- ReLU
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||
|
|
- Residual Block
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||
|
|
- Residual Connection
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||
|
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- Softmax
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||
|
|
File Size: 102493763
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||
|
|
Tasks:
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||
|
|
- Image Classification
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||
|
|
ID: gluon_resnet50_v1b
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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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||
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|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L81
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In Collection: Gloun ResNet
|
||
|
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- Name: gluon_resnet18_v1b
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Metadata:
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||
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FLOPs: 2337073152
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Training Data:
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||
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- ImageNet
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||
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Architecture:
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||
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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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File Size: 46816736
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||
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Tasks:
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- Image Classification
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ID: gluon_resnet18_v1b
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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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||
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L65
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In Collection: Gloun ResNet
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||
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- Name: gluon_resnet34_v1b
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Metadata:
|
||
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FLOPs: 4718469120
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||
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Training Data:
|
||
|
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- ImageNet
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||
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Architecture:
|
||
|
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- 1x1 Convolution
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- Batch Normalization
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|
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- Bottleneck Residual Block
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||
|
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- Convolution
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||
|
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- Global Average Pooling
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||
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- Max Pooling
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||
|
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- ReLU
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||
|
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- Residual Block
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||
|
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- Residual Connection
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- Softmax
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File Size: 87295112
|
||
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Tasks:
|
||
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|
- Image Classification
|
||
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|
ID: gluon_resnet34_v1b
|
||
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|
Crop Pct: '0.875'
|
||
|
|
Image Size: '224'
|
||
|
|
Interpolation: bicubic
|
||
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|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L73
|
||
|
|
In Collection: Gloun ResNet
|
||
|
|
- Name: gluon_resnet50_v1c
|
||
|
|
Metadata:
|
||
|
|
FLOPs: 5590551040
|
||
|
|
Training Data:
|
||
|
|
- ImageNet
|
||
|
|
Architecture:
|
||
|
|
- 1x1 Convolution
|
||
|
|
- Batch Normalization
|
||
|
|
- Bottleneck Residual Block
|
||
|
|
- Convolution
|
||
|
|
- Global Average Pooling
|
||
|
|
- Max Pooling
|
||
|
|
- ReLU
|
||
|
|
- Residual Block
|
||
|
|
- Residual Connection
|
||
|
|
- Softmax
|
||
|
|
File Size: 102573166
|
||
|
|
Tasks:
|
||
|
|
- Image Classification
|
||
|
|
ID: gluon_resnet50_v1c
|
||
|
|
Crop Pct: '0.875'
|
||
|
|
Image Size: '224'
|
||
|
|
Interpolation: bicubic
|
||
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L105
|
||
|
|
In Collection: Gloun ResNet
|
||
|
|
- Name: gluon_resnet50_v1d
|
||
|
|
Metadata:
|
||
|
|
FLOPs: 5591002624
|
||
|
|
Training Data:
|
||
|
|
- ImageNet
|
||
|
|
Architecture:
|
||
|
|
- 1x1 Convolution
|
||
|
|
- Batch Normalization
|
||
|
|
- Bottleneck Residual Block
|
||
|
|
- Convolution
|
||
|
|
- Global Average Pooling
|
||
|
|
- Max Pooling
|
||
|
|
- ReLU
|
||
|
|
- Residual Block
|
||
|
|
- Residual Connection
|
||
|
|
- Softmax
|
||
|
|
File Size: 102573346
|
||
|
|
Tasks:
|
||
|
|
- Image Classification
|
||
|
|
ID: gluon_resnet50_v1d
|
||
|
|
Crop Pct: '0.875'
|
||
|
|
Image Size: '224'
|
||
|
|
Interpolation: bicubic
|
||
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L129
|
||
|
|
In Collection: Gloun ResNet
|
||
|
|
- Name: gluon_resnet50_v1s
|
||
|
|
Metadata:
|
||
|
|
FLOPs: 7019495424
|
||
|
|
Training Data:
|
||
|
|
- ImageNet
|
||
|
|
Architecture:
|
||
|
|
- 1x1 Convolution
|
||
|
|
- Batch Normalization
|
||
|
|
- Bottleneck Residual Block
|
||
|
|
- Convolution
|
||
|
|
- Global Average Pooling
|
||
|
|
- Max Pooling
|
||
|
|
- ReLU
|
||
|
|
- Residual Block
|
||
|
|
- Residual Connection
|
||
|
|
- Softmax
|
||
|
|
File Size: 102992368
|
||
|
|
Tasks:
|
||
|
|
- Image Classification
|
||
|
|
ID: gluon_resnet50_v1s
|
||
|
|
Crop Pct: '0.875'
|
||
|
|
Image Size: '224'
|
||
|
|
Interpolation: bicubic
|
||
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L156
|
||
|
|
In Collection: Gloun ResNet
|
||
|
|
Collections:
|
||
|
|
- Name: Gloun ResNet
|
||
|
|
Paper:
|
||
|
|
title: Deep Residual Learning for Image Recognition
|
||
|
3 years ago
|
url: https://paperswithcode.com//paper/deep-residual-learning-for-image-recognition
|
||
|
3 years ago
|
type: model-index
|
||
|
|
Type: model-index
|
||
|
|
-->
|