r"""ResNeXt-101 32x8d model from `"Aggregated Residual Transformation for Deep Neural Networks" `_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the downlo
(pretrained: bool = False, progress: bool = True, **kwargs: Any)
| 340 | |
| 341 | |
| 342 | def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: |
| 343 | r"""ResNeXt-101 32x8d model from |
| 344 | `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_. |
| 345 | |
| 346 | Args: |
| 347 | pretrained (bool): If True, returns a model pre-trained on ImageNet |
| 348 | progress (bool): If True, displays a progress bar of the download to stderr |
| 349 | """ |
| 350 | kwargs['groups'] = 32 |
| 351 | kwargs['width_per_group'] = 8 |
| 352 | return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], |
| 353 | pretrained, progress, **kwargs) |
| 354 | |
| 355 | |
| 356 | def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: |