Overview
ResNet models in PyTorch's `torchvision` library provide pre-trained deep learning architectures for image recognition tasks. These models, including ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152, are trained on the ImageNet dataset. The architecture leverages residual connections to mitigate the vanishing gradient problem, enabling the training of deeper networks. The models expect mini-batches of 3-channel RGB images normalized with specified mean and standard deviation. Use cases include image classification, feature extraction for transfer learning, and as a component in more complex vision systems. The pre-trained weights allow for rapid prototyping and deployment, offering a significant advantage in terms of training time and computational resources.
Common tasks