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Collections of pre-optimized weights and architectures implemented in PyTorch.
Distinct from PyTorch Model Components: Focuses on the provision of full pretrained libraries rather than individual modular components
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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. Capabil
Provides a library of pretrained convolutional neural network architectures specifically for PyTorch users.