r"""ResNeXt-50 32x4d 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 downloa
(pretrained: bool = False, progress: bool = True, **kwargs: Any)
| 326 | |
| 327 | |
| 328 | def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: |
| 329 | r"""ResNeXt-50 32x4d model from |
| 330 | `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_. |
| 331 | |
| 332 | Args: |
| 333 | pretrained (bool): If True, returns a model pre-trained on ImageNet |
| 334 | progress (bool): If True, displays a progress bar of the download to stderr |
| 335 | """ |
| 336 | kwargs['groups'] = 32 |
| 337 | kwargs['width_per_group'] = 4 |
| 338 | return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], |
| 339 | pretrained, progress, **kwargs) |
| 340 | |
| 341 | |
| 342 | def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: |