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