This project is a pretrained model library for PyTorch, providing a collection of convolutional neural network architectures and weights. It serves as a computer vision model zoo for image classification and feature extraction, offering a framework for transfer learning where pretrained networks are adapted for custom image recognition tasks.
The library focuses on transforming images into high-level numerical representations and calculating class probability scores. It includes utilities for downloading and initializing standard architectures such as ResNet, Inception, and Xception.
Capabilities cover the entire computer vision pipeline, from retrieving model-specific normalization metadata and input dimensions to executing inference. It supports both full image classification and layer-based feature extraction by isolating high-level tensors for downstream analysis.