The following classes allow you to access ResNet models in PyTorch:


The number of channels in outer 1x1 convolutions is the same, e.g. The following are code examples for showing how to use torchvision.models.resnet50().They are from open source Python projects. def fasterrcnn_resnet50_fpn (pretrained = False, progress = True, num_classes = 91, pretrained_backbone = True, ** kwargs): """ Constructs a Faster R-CNN model with a ResNet-50-FPN backbone.

To analyze traffic and optimize your experience, we serve cookies on this site. The input to the model is expected to be a list of tensors, each of shape ``[C, H, W]``, one for each image, and should be in ``0-1`` range.

You can vote up the examples you like or vote down the ones you don't like. torchvision.models include the following ResNet implementations: ResNet-18, 34, 50, 101 and 152 (the numbers indicate the numbers of layers in the model), and Densenet-121, 161, 169, and 201. The input to the model is expected to be a list of tensors, each of shape ``[C, H, W]``, one for each image, and should be in ``0-1`` range. Different images can have different sizes. Different images can have different sizes. SENet-154, SE-ResNet-18, SE-ResNet-34, SE-ResNet-50, SE-ResNet-101, SE-ResNet-152, SE-ResNeXt-26 (32x4d), SE-ResNeXt50 (32x4d), SE-ResNeXt101 (32x4d) Inception-V3 (from torchvision) Inception-ResNet-V2 and Inception-V4 (from Cadene) Xception Original variant from Cadene; MXNet Gluon 'modified aligned' Xception-65 and 71 models from Gluon ModelZoo Wide ResNet¶ torchvision.models.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 bottleneck number of channels which is twice larger in every block. By clicking or navigating, you agree to allow our usage of cookies.

def maskrcnn_resnet50_fpn (pretrained = False, progress = True, num_classes = 91, pretrained_backbone = True, ** kwargs): """ Constructs a Mask R-CNN model with a ResNet-50-FPN backbone.