resnet101

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

The model builder above accepts the following values as the weights parameter. ResNet101_Weights.DEFAULT is equivalent to ResNet101_Weights.IMAGENET1K_V2. You can also use strings, e.g. weights='DEFAULT' or weights='IMAGENET1K_V1'.

ResNet101_Weights.IMAGENET1K_V1:

These weights reproduce closely the results of the paper using a simple training recipe.

acc@1 (on ImageNet-1K)	77.374
acc@5 (on ImageNet-1K)	93.546
min_size	height=1, width=1
categories	tench, goldfish, great white shark, … (997 omitted)
num_params	44549160
recipe	link

The inference transforms are available at ResNet101_Weights.IMAGENET1K_V1.transforms and perform the following preprocessing operations: Accepts PIL.Image, batched (B, C, H, W) and single (C, H, W) image torch.Tensor objects. The images are resized to resize_size=[256] using interpolation=InterpolationMode.BILINEAR, followed by a central crop of crop_size=[224]. Finally the values are first rescaled to [0.0, 1.0] and then normalized using mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].

ResNet101_Weights.IMAGENET1K_V2:

These weights improve upon the results of the original paper by using TorchVision’s new training recipe. Also available as ResNet101_Weights.DEFAULT.

acc@1 (on ImageNet-1K)	81.886
acc@5 (on ImageNet-1K)	95.78
min_size	height=1, width=1
categories	tench, goldfish, great white shark, … (997 omitted)
num_params	44549160
recipe	link

The inference transforms are available at ResNet101_Weights.IMAGENET1K_V2.transforms and perform the following preprocessing operations: Accepts PIL.Image, batched (B, C, H, W) and single (C, H, W) image torch.Tensor objects. The images are resized to resize_size=[232] using interpolation=InterpolationMode.BILINEAR, followed by a central crop of crop_size=[224]. Finally the values are first rescaled to [0.0, 1.0] and then normalized using mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].