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