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()
squeezenet = models.squeezenet1_0()
densenet = models.densenet_161()

We provide pre-trained models for the ResNet variants and AlexNet, using the PyTorch torch.utils.model_zoo. These can constructed by passing pretrained=True:

import torchvision.models as models
resnet18 = models.resnet18(pretrained=True)
alexnet = models.alexnet(pretrained=True)

ImageNet 1-crop error rates (224x224)

Network Top-1 error Top-5 error
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
Inception v3 22.55 6.44
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
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
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
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
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(**kwargs)

VGG 11-layer model (configuration “A”) with batch normalization

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(**kwargs)

VGG 13-layer model (configuration “B”) with batch normalization

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(**kwargs)

VGG 16-layer model (configuration “D”) with batch normalization

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(**kwargs)

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