resnet101

torchvision.models.resnet101(*, weights: Optional[ResNet101_Weights] = None, progress: bool = True, **kwargs: Any) → ResNet

ResNet-101 from Deep Residual Learning for Image Recognition.

Note

The bottleneck of TorchVision places the stride for downsampling to the second 3x3 convolution while the original paper places it to the first 1x1 convolution. This variant improves the accuracy and is known as ResNet V1.5.

Parameters:

• weights (ResNet101_Weights, optional) – The pretrained weights to use. See ResNet101_Weights below for more details, and possible values. By default, no pre-trained weights are used.

• progress (bool, optional) – If True, displays a progress bar of the download to stderr. Default is True.

• **kwargs – parameters passed to the torchvision.models.resnet.ResNet base class. Please refer to the source code for more details about this class.

class torchvision.models.ResNet101_Weights(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)