torchvision.models

torchvision.models

torchvision.models模块的子模块中包含以下模型结构。

AlexNet
VGG
ResNet
SqueezeNet
DenseNet
You can construct a model with random weights by calling its constructor:

你可以使用随机初始化的权重来创建这些模型。

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:
对于ResNet variants和AlexNet，我们也提供了预训练(pre-trained)的模型。

import torchvision.models as models
#pretrained=True就可以使用预训练的模型
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 模型结构 paper地址

pretrained (bool) – True, 返回在ImageNet上训练好的模型。

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

构建一个resnet18模型

pretrained (bool) – True, 返回在ImageNet上训练好的模型。

torchvision.models.resnet34(pretrained=False, ** kwargs)

构建一个ResNet-34 模型.

Parameters: pretrained (bool) – True, 返回在ImageNet上训练好的模型。

torchvision.models.resnet50(pretrained=False, ** kwargs)

构建一个ResNet-50模型

pretrained (bool) – True, 返回在ImageNet上训练好的模型。

torchvision.models.resnet101(pretrained=False, ** kwargs)

Constructs a ResNet-101 model.

pretrained (bool) – True, 返回在ImageNet上训练好的模型。

torchvision.models.resnet152(pretrained=False, ** kwargs)

Constructs a ResNet-152 model.

pretrained (bool) – True, 返回在ImageNet上训练好的模型。

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

VGG 11-layer model (configuration “A”)

pretrained (bool) – True, 返回在ImageNet上训练好的模型。

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”)

pretrained (bool) – True, 返回在ImageNet上训练好的模型。

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”)

pretrained (bool) – True, 返回在ImageNet上训练好的模型。
torchvision.models.vgg19_bn(** kwargs)
VGG 19-layer model (configuration ‘E’) with batch normalization