PyTorch-Encoding
Delve into innovative neural network encoding methods in deep learning as pioneered by Hang Zhang, featuring ResNeSt's split-attention mechanism for leading semantic segmentation. Access extensive documentation within the PyTorch-Encoding model zoo, presenting models for image classification and segmentation. The project incorporates notable contributions such as Deep TEN and Context Encoding, serving as a vital resource for neural network encoding research. It emphasizes thorough unit testing and detailed build guides, aiding developers in achieving improved accuracy on datasets like ADE20K and Pascal Context.
