pytorch之models

torchvision.models模块的子模块中包含以下模型结构。

• AlexNet
• VGG
• ResNet
• SqueezeNet
• DenseNet

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()

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)

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

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

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

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

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

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

torchvision.models.vgg11_bn(** kwargs)with batch normalization

torchvision.models.vgg13(pretrained=False, ** kwargs)

torchvision.models.vgg13_bn(** kwargs)

torchvision.models.vgg16(pretrained=False, ** kwargs)

torchvision.models.vgg16_bn(** kwargs)

torchvision.models.vgg19(pretrained=False, ** kwargs)

torchvision.models.vgg19_bn(** kwargs)

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