torchvision.models¶
The models subpackage contains definitions for the following model architectures:
You can construct a model with random weights by calling its constructor:
import torchvision.models as models
resnet18 = models.resnet18()
alexnet = models.alexnet()
vgg16 = models.vgg16()
squeezenet = models.squeezenet1_0()
densenet = models.densenet161()
inception = models.inception_v3()
We provide pre-trained models, using the PyTorch torch.utils.model_zoo.
These can be constructed by passing pretrained=True:
import torchvision.models as models
resnet18 = models.resnet18(pretrained=True)
alexnet = models.alexnet(pretrained=True)
squeezenet = models.squeezenet1_0(pretrained=True)
vgg16 = models.vgg16(pretrained=True)
densenet = models.densenet161(pretrained=True)
inception = models.inception_v3(pretrained=True)
Some models use modules which have different training and evaluation
behavior, such as batch normalization. To switch between these modes, use
model.train() or model.eval() as appropriate. See
train() or eval() for details.
All pre-trained models expect input images normalized in the same way,
i.e. mini-batches of 3-channel RGB images of shape (3 x H x W),
where H and W are expected to be at least 224.
The images have to be loaded in to a range of [0, 1] and then normalized
using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].
You can use the following transform to normalize:
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
An example of such normalization can be found in the imagenet example here
ImageNet 1-crop error rates (224x224)
| Network | Top-1 error | Top-5 error |
|---|---|---|
| AlexNet | 43.45 | 20.91 |
| VGG-11 | 30.98 | 11.37 |
| VGG-13 | 30.07 | 10.75 |
| VGG-16 | 28.41 | 9.62 |
| VGG-19 | 27.62 | 9.12 |
| VGG-11 with batch normalization | 29.62 | 10.19 |
| VGG-13 with batch normalization | 28.45 | 9.63 |
| VGG-16 with batch normalization | 26.63 | 8.50 |
| VGG-19 with batch normalization | 25.76 | 8.15 |
| ResNet-18 | 30.24 | 10.92 |
| ResNet-34 | 26.70 | 8.58 |
| ResNet-50 | 23.85 | 7.13 |
| ResNet-101 | 22.63 | 6.44 |
| ResNet-152 | 21.69 | 5.94 |
| SqueezeNet 1.0 | 41.90 | 19.58 |
| SqueezeNet 1.1 | 41.81 | 19.38 |
| Densenet-121 | 25.35 | 7.83 |
| Densenet-169 | 24.00 | 7.00 |
| Densenet-201 | 22.80 | 6.43 |
| Densenet-161 | 22.35 | 6.20 |
| Inception v3 | 22.55 | 6.44 |
Alexnet¶
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torchvision.models.alexnet(pretrained=False, **kwargs)¶ AlexNet model architecture from the “One weird trick…” paper.
Parameters: pretrained (bool) – If True, returns a model pre-trained on ImageNet
VGG¶
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torchvision.models.vgg11(pretrained=False, **kwargs)¶ VGG 11-layer model (configuration “A”)
Parameters: pretrained (bool) – If True, returns a model pre-trained on ImageNet
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torchvision.models.vgg11_bn(pretrained=False, **kwargs)¶ VGG 11-layer model (configuration “A”) with batch normalization
Parameters: pretrained (bool) – If True, returns a model pre-trained on ImageNet
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torchvision.models.vgg13(pretrained=False, **kwargs)¶ VGG 13-layer model (configuration “B”)
Parameters: pretrained (bool) – If True, returns a model pre-trained on ImageNet
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torchvision.models.vgg13_bn(pretrained=False, **kwargs)¶ VGG 13-layer model (configuration “B”) with batch normalization
Parameters: pretrained (bool) – If True, returns a model pre-trained on ImageNet
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torchvision.models.vgg16(pretrained=False, **kwargs)¶ VGG 16-layer model (configuration “D”)
Parameters: pretrained (bool) – If True, returns a model pre-trained on ImageNet
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torchvision.models.vgg16_bn(pretrained=False, **kwargs)¶ VGG 16-layer model (configuration “D”) with batch normalization
Parameters: pretrained (bool) – If True, returns a model pre-trained on ImageNet
ResNet¶
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torchvision.models.resnet18(pretrained=False, **kwargs)¶ Constructs a ResNet-18 model.
Parameters: pretrained (bool) – If True, returns a model pre-trained on ImageNet
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torchvision.models.resnet34(pretrained=False, **kwargs)¶ Constructs a ResNet-34 model.
Parameters: pretrained (bool) – If True, returns a model pre-trained on ImageNet
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torchvision.models.resnet50(pretrained=False, **kwargs)¶ Constructs a ResNet-50 model.
Parameters: pretrained (bool) – If True, returns a model pre-trained on ImageNet
SqueezeNet¶
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torchvision.models.squeezenet1_0(pretrained=False, **kwargs)¶ SqueezeNet model architecture from the “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size” paper.
Parameters: pretrained (bool) – If True, returns a model pre-trained on ImageNet
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torchvision.models.squeezenet1_1(pretrained=False, **kwargs)¶ SqueezeNet 1.1 model from the official SqueezeNet repo. SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters than SqueezeNet 1.0, without sacrificing accuracy.
Parameters: pretrained (bool) – If True, returns a model pre-trained on ImageNet
DenseNet¶
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torchvision.models.densenet121(pretrained=False, **kwargs)¶ Densenet-121 model from “Densely Connected Convolutional Networks”
Parameters: pretrained (bool) – If True, returns a model pre-trained on ImageNet
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torchvision.models.densenet169(pretrained=False, **kwargs)¶ Densenet-169 model from “Densely Connected Convolutional Networks”
Parameters: pretrained (bool) – If True, returns a model pre-trained on ImageNet
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torchvision.models.densenet161(pretrained=False, **kwargs)¶ Densenet-161 model from “Densely Connected Convolutional Networks”
Parameters: pretrained (bool) – If True, returns a model pre-trained on ImageNet
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torchvision.models.densenet201(pretrained=False, **kwargs)¶ Densenet-201 model from “Densely Connected Convolutional Networks”
Parameters: pretrained (bool) – If True, returns a model pre-trained on ImageNet
Inception v3¶
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torchvision.models.inception_v3(pretrained=False, **kwargs)¶ Inception v3 model architecture from “Rethinking the Inception Architecture for Computer Vision”.
Parameters: pretrained (bool) – If True, returns a model pre-trained on ImageNet