Vision Models

ResNets

Modified based on torchvision.models.resnet.

class dalib.vision.models.resnet.ResNet(*args, **kwargs)

Bases: torchvision.models.resnet.ResNet

ResNets without fully connected layer

forward(x)

out_features

The dimension of output features

dalib.vision.models.resnet.resnet18(pretrained=False, progress=True, **kwargs)

ResNet-18 model from “Deep Residual Learning for Image Recognition”

Parameters:

• pretrained (bool): If True, returns a model pre-trained on ImageNet
• progress (bool): If True, displays a progress bar of the download to stderr

dalib.vision.models.resnet.resnet34(pretrained=False, progress=True, **kwargs)

ResNet-34 model from “Deep Residual Learning for Image Recognition”

Parameters:

• pretrained (bool): If True, returns a model pre-trained on ImageNet
• progress (bool): If True, displays a progress bar of the download to stderr

dalib.vision.models.resnet.resnet50(pretrained=False, progress=True, **kwargs)

ResNet-50 model from “Deep Residual Learning for Image Recognition”

Parameters:

• pretrained (bool): If True, returns a model pre-trained on ImageNet
• progress (bool): If True, displays a progress bar of the download to stderr

dalib.vision.models.resnet.resnet101(pretrained=False, progress=True, **kwargs)

ResNet-101 model from “Deep Residual Learning for Image Recognition”

Parameters:

• pretrained (bool): If True, returns a model pre-trained on ImageNet
• progress (bool): If True, displays a progress bar of the download to stderr

dalib.vision.models.resnet.resnet152(pretrained=False, progress=True, **kwargs)

ResNet-152 model from “Deep Residual Learning for Image Recognition”

Parameters:

• pretrained (bool): If True, returns a model pre-trained on ImageNet
• progress (bool): If True, displays a progress bar of the download to stderr

dalib.vision.models.resnet.resnext50_32x4d(pretrained=False, progress=True, **kwargs)

ResNeXt-50 32x4d model from “Aggregated Residual Transformation for Deep Neural Networks”

Parameters:

• pretrained (bool): If True, returns a model pre-trained on ImageNet
• progress (bool): If True, displays a progress bar of the download to stderr

dalib.vision.models.resnet.resnext101_32x8d(pretrained=False, progress=True, **kwargs)

ResNeXt-101 32x8d model from “Aggregated Residual Transformation for Deep Neural Networks”

Parameters:

• pretrained (bool): If True, returns a model pre-trained on ImageNet
• progress (bool): If True, displays a progress bar of the download to stderr

dalib.vision.models.resnet.wide_resnet50_2(pretrained=False, progress=True, **kwargs)

Wide ResNet-50-2 model from “Wide Residual Networks”

The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048.

Parameters:

• pretrained (bool): If True, returns a model pre-trained on ImageNet
• progress (bool): If True, displays a progress bar of the download to stderr

dalib.vision.models.resnet.wide_resnet101_2(pretrained=False, progress=True, **kwargs)

Wide ResNet-101-2 model from “Wide Residual Networks”

The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048.

Parameters:

• pretrained (bool): If True, returns a model pre-trained on ImageNet
• progress (bool): If True, displays a progress bar of the download to stderr