LibMTL.model¶
- resnet18(pretrained=False, progress=True, **kwargs)[source]¶
ResNet-18 model from “Deep Residual Learning for Image Recognition”
- Parameters
pretrained (bool) – If True, returns a model pre-trained on the ImageNet dataset.
progress (bool) – If True, displays a progress bar of the download to stderr.
- resnet34(pretrained=False, progress=True, **kwargs)[source]¶
ResNet-34 model from “Deep Residual Learning for Image Recognition”
- Parameters
pretrained (bool) – If True, returns a model pre-trained on the ImageNet dataset.
progress (bool) – If True, displays a progress bar of the download to stderr.
- resnet50(pretrained=False, progress=True, **kwargs)[source]¶
ResNet-50 model from “Deep Residual Learning for Image Recognition”
- Parameters
pretrained (bool) – If True, returns a model pre-trained on the ImageNet dataset.
progress (bool) – If True, displays a progress bar of the download to stderr.
- resnet101(pretrained=False, progress=True, **kwargs)[source]¶
ResNet-101 model from “Deep Residual Learning for Image Recognition”
- Parameters
pretrained (bool) – If True, returns a model pre-trained on the ImageNet dataset.
progress (bool) – If True, displays a progress bar of the download to stderr.
- resnet152(pretrained=False, progress=True, **kwargs)[source]¶
ResNet-152 model from “Deep Residual Learning for Image Recognition”
- Parameters
pretrained (bool) – If True, returns a model pre-trained on the ImageNet dataset.
progress (bool) – If True, displays a progress bar of the download to stderr.
- resnext50_32x4d(pretrained=False, progress=True, **kwargs)[source]¶
ResNeXt-50 32x4d model from “Aggregated Residual Transformation for Deep Neural Networks”
- Parameters
pretrained (bool) – If True, returns a model pre-trained on the ImageNet dataset.
progress (bool) – If True, displays a progress bar of the download to stderr.
- resnext101_32x8d(pretrained=False, progress=True, **kwargs)[source]¶
ResNeXt-101 32x8d model from “Aggregated Residual Transformation for Deep Neural Networks”
- Parameters
pretrained (bool) – If True, returns a model pre-trained on the ImageNet dataset.
progress (bool) – If True, displays a progress bar of the download to stderr.
- wide_resnet50_2(pretrained=False, progress=True, **kwargs)[source]¶
Wide ResNet-50-2 model from “Wide Residual Networks”
The model is the same as ResNet except for the number of bottleneck channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g., the last block in ResNet-50 has 2048-512-2048 channels, while in wide ResNet-50-2 there are 2048-1024-2048.
- Parameters
pretrained (bool) – If True, returns a model pre-trained on the ImageNet dataset.
progress (bool) – If True, displays a progress bar of the download to stderr.
- wide_resnet101_2(pretrained=False, progress=True, **kwargs)[source]¶
Wide ResNet-101-2 model from “Wide Residual Networks”
The model is the same as ResNet except for the number of bottleneck channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g., the last block in ResNet-101 has 2048-512-2048 channels, while in wide ResNet-101-2 there are 2048-1024-2048.
- Parameters
pretrained (bool) – If True, returns a model pre-trained on the ImageNet dataset.
progress (bool) – If True, displays a progress bar of the download to stderr.
- resnet_dilated(basenet, pretrained=True, dilate_scale=8)[source]¶
Dilated Residual Network models from “Dilated Residual Networks”
- Parameters
basenet (str) – The type of ResNet.
pretrained (bool) – If True, returns a model pre-trained on ImageNet.
dilate_scale ({8, 16}, default=8) – The type of dilating process.