The models subpackage contains definitions for the following model architectures for ... ResNet. torchvision.models. resnet18 (pretrained: bool = False, ...
torchvision.models. wide_resnet101_2 (pretrained: bool = False, progress: bool = True, ** kwargs: Any) → torchvision.models.resnet.ResNet [source] ¶ Wide ResNet-101-2 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.
Resnet models were proposed in “Deep Residual Learning for Image Recognition”. Here we have the 5 versions of resnet models, which contains 18, 34, 50, 101, 152 layers respectively. Detailed model architectures can be found in Table 1.
03.09.2020 · In this post, you will learn about how to load and predict using pre-trained Resnet model using PyTorch library. Here is arxiv paper on Resnet.. Before getting into the aspect of loading and predicting using Resnet (Residual neural network) using PyTorch, you would want to learn about how to load different pretrained models such as AlexNet, ResNet, DenseNet, …