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571 lines
17 KiB
571 lines
17 KiB
# (Gluon) 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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The weights from this model were ported from [Gluon](https://cv.gluon.ai/model_zoo/classification.html).
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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('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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```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. `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](../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('gluon_resnet101_v1b', 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: Gloun 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: gluon_resnet101_v1b
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In Collection: Gloun ResNet
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Metadata:
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FLOPs: 10068547584
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Parameters: 44550000
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File Size: 178723172
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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: 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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Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1b-3b017079.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: 79.3%
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Top 5 Accuracy: 94.53%
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- Name: gluon_resnet101_v1c
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In Collection: Gloun ResNet
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Metadata:
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FLOPs: 10376567296
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Parameters: 44570000
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File Size: 178802575
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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: 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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Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1c-1f26822a.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: 79.53%
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Top 5 Accuracy: 94.59%
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- Name: gluon_resnet101_v1d
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In Collection: Gloun ResNet
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Metadata:
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FLOPs: 10377018880
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Parameters: 44570000
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File Size: 178802755
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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: 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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Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1d-0f9c8644.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: 80.4%
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Top 5 Accuracy: 95.02%
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- Name: gluon_resnet101_v1s
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In Collection: Gloun ResNet
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Metadata:
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FLOPs: 11805511680
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Parameters: 44670000
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File Size: 179221777
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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: 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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Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1s-60fe0cc1.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: 80.29%
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Top 5 Accuracy: 95.16%
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- Name: gluon_resnet152_v1b
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In Collection: Gloun ResNet
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Metadata:
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FLOPs: 14857660416
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Parameters: 60190000
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File Size: 241534001
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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: 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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Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1b-c1edb0dd.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: 79.69%
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Top 5 Accuracy: 94.73%
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- Name: gluon_resnet152_v1c
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In Collection: Gloun ResNet
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Metadata:
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FLOPs: 15165680128
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Parameters: 60210000
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File Size: 241613404
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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: 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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Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1c-a3bb0b98.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: 79.91%
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Top 5 Accuracy: 94.85%
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- Name: gluon_resnet152_v1d
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In Collection: Gloun ResNet
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Metadata:
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FLOPs: 15166131712
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Parameters: 60210000
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File Size: 241613584
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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: 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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Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1d-bd354e12.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: 80.48%
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Top 5 Accuracy: 95.2%
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- Name: gluon_resnet152_v1s
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In Collection: Gloun ResNet
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Metadata:
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FLOPs: 16594624512
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Parameters: 60320000
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File Size: 242032606
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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: gluon_resnet152_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#L175
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Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1s-dcc41b81.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: 81.02%
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Top 5 Accuracy: 95.42%
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- Name: gluon_resnet18_v1b
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In Collection: Gloun ResNet
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Metadata:
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FLOPs: 2337073152
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Parameters: 11690000
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File Size: 46816736
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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: 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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|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L65
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Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet18_v1b-0757602b.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: 70.84%
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Top 5 Accuracy: 89.76%
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- Name: gluon_resnet34_v1b
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In Collection: Gloun ResNet
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|
Metadata:
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FLOPs: 4718469120
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Parameters: 21800000
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|
File Size: 87295112
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|
Architecture:
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|
- 1x1 Convolution
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|
- Batch Normalization
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|
- Bottleneck Residual Block
|
|
- 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: gluon_resnet34_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#L73
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Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet34_v1b-c6d82d59.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: 74.59%
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Top 5 Accuracy: 92.0%
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- Name: gluon_resnet50_v1b
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In Collection: Gloun ResNet
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Metadata:
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FLOPs: 5282531328
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|
Parameters: 25560000
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File Size: 102493763
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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: 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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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L81
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Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1b-0ebe02e2.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: 77.58%
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Top 5 Accuracy: 93.72%
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- Name: gluon_resnet50_v1c
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In Collection: Gloun ResNet
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|
Metadata:
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FLOPs: 5590551040
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|
Parameters: 25580000
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File Size: 102573166
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|
Architecture:
|
|
- 1x1 Convolution
|
|
- Batch Normalization
|
|
- Bottleneck Residual Block
|
|
- Convolution
|
|
- Global Average Pooling
|
|
- Max Pooling
|
|
- ReLU
|
|
- Residual Block
|
|
- Residual Connection
|
|
- Softmax
|
|
Tasks:
|
|
- Image Classification
|
|
Training Data:
|
|
- ImageNet
|
|
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
|
|
Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1c-48092f55.pth
|
|
Results:
|
|
- Task: Image Classification
|
|
Dataset: ImageNet
|
|
Metrics:
|
|
Top 1 Accuracy: 78.01%
|
|
Top 5 Accuracy: 93.99%
|
|
- Name: gluon_resnet50_v1d
|
|
In Collection: Gloun ResNet
|
|
Metadata:
|
|
FLOPs: 5591002624
|
|
Parameters: 25580000
|
|
File Size: 102573346
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Batch Normalization
|
|
- Bottleneck Residual Block
|
|
- Convolution
|
|
- Global Average Pooling
|
|
- Max Pooling
|
|
- ReLU
|
|
- Residual Block
|
|
- Residual Connection
|
|
- Softmax
|
|
Tasks:
|
|
- Image Classification
|
|
Training Data:
|
|
- ImageNet
|
|
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
|
|
Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1d-818a1b1b.pth
|
|
Results:
|
|
- Task: Image Classification
|
|
Dataset: ImageNet
|
|
Metrics:
|
|
Top 1 Accuracy: 79.06%
|
|
Top 5 Accuracy: 94.46%
|
|
- Name: gluon_resnet50_v1s
|
|
In Collection: Gloun ResNet
|
|
Metadata:
|
|
FLOPs: 7019495424
|
|
Parameters: 25680000
|
|
File Size: 102992368
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Batch Normalization
|
|
- Bottleneck Residual Block
|
|
- Convolution
|
|
- Global Average Pooling
|
|
- Max Pooling
|
|
- ReLU
|
|
- Residual Block
|
|
- Residual Connection
|
|
- Softmax
|
|
Tasks:
|
|
- Image Classification
|
|
Training Data:
|
|
- ImageNet
|
|
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
|
|
Weights: https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1s-1762acc0.pth
|
|
Results:
|
|
- Task: Image Classification
|
|
Dataset: ImageNet
|
|
Metrics:
|
|
Top 1 Accuracy: 78.7%
|
|
Top 5 Accuracy: 94.25%
|
|
--> |