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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

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

torchvision.models.vgg11(pretrained=False, **kwargs)

VGG 11-layer model (configuration “A”)

Parameters:pretrained (bool) – If True, returns a model pre-trained on ImageNet
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
torchvision.models.vgg13(pretrained=False, **kwargs)

VGG 13-layer model (configuration “B”)

Parameters:pretrained (bool) – If True, returns a model pre-trained on ImageNet
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
torchvision.models.vgg16(pretrained=False, **kwargs)

VGG 16-layer model (configuration “D”)

Parameters:pretrained (bool) – If True, returns a model pre-trained on ImageNet
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
torchvision.models.vgg19(pretrained=False, **kwargs)

VGG 19-layer model (configuration “E”)

Parameters:pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.vgg19_bn(pretrained=False, **kwargs)

VGG 19-layer model (configuration ‘E’) with batch normalization

Parameters:pretrained (bool) – If True, returns a model pre-trained on ImageNet

ResNet

torchvision.models.resnet18(pretrained=False, **kwargs)

Constructs a ResNet-18 model.

Parameters:pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.resnet34(pretrained=False, **kwargs)

Constructs a ResNet-34 model.

Parameters:pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.resnet50(pretrained=False, **kwargs)

Constructs a ResNet-50 model.

Parameters:pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.resnet101(pretrained=False, **kwargs)

Constructs a ResNet-101 model.

Parameters:pretrained (bool) – If True, returns a model pre-trained on ImageNet
torchvision.models.resnet152(pretrained=False, **kwargs)

Constructs a ResNet-152 model.

Parameters:pretrained (bool) – If True, returns a model pre-trained on ImageNet

SqueezeNet

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
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

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
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
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
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

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

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