'vgg13':'https://download.pytorch.org/models/vgg13-c768596a.pth', 'vgg16':'https://download.pytorch.org/models/vgg16-397923af.pth', 'vgg19':'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth', 'vgg11_bn':'https://download.pytorch.org/models/vgg11_bn-6002323d.pth', 'vgg...
VGG的网络结构非常简单,全部都是 $(3,3)$ 的卷积核,步长为 $1$,四周补 $1$ 圈 $0$(使得输入输出的 $(H,W)$ 不变): 我们可以参考 torchvision.models 中的源码: class VGG(nn.Module): def __init__(self, features, num_classes=1000, init_weights=True): super(VGG, self).__init__() s...
model = torchvision.models.densenet169(pretrained=False)#等价于:model = torchvision.models.densenet169() 例子: importtorchvision.models as models vgg16= models.vgg16(pretrained = True)#获取训练好的VGG16模型pretrained_dict = vgg16.state_dict()#返回包含模块所有状态的字典,包括参数和缓存 2. 源码分析...
torchvision.models.vgg19_bn(pretrained=False, progress=True, **kwargs) pretrained: 如果设置为 True,则返回在 ImageNet 预训练过的模型。
PyTorch框架提供了多种预训练模型,这些模型在大规模数据集(如ImageNet)上进行训练,已经学习到了丰富的特征表示。这些预训练模型包括但不限于各种常见的卷积神经网络架构,如VGG、ResNet、DenseNet、Inception等。 要在PyTorch中加载预训练模型,可以使用torchvision.models模块。torchvision.models模块提供了很多的预训练模型,...
torchvision.models.vgg16_bn(pretrained=False, **kwargs)[source]¶ VGG 16-layer model (configuration “D”) with batch normalization Parameters: pretrained (bool)– If True, returns a model pre-trained on ImageNet torchvision.models.vgg19(pretrained=False, **kwargs)[source]¶ VGG 19-layer...
'vgg11_bn':'https://download.pytorch.org/models/vgg11_bn-6002323d.pth','vgg13_bn':'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth','vgg16_bn':'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth','vgg19_bn':'https://download.pytorch.org/models/vgg19_bn-c...
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 ...
'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth', 'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth', 'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth', 'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd24...
model_ft = models.vgg11_bn(pretrained=use_pretrained) set_parameter_requires_grad(model_ft, feature_extract) num_ftrs = model_ft.classifier[6].in_features model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes) input_size = 224 ...