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Guillaume Lagrange a9abd8f746
Add missing output padding to conv transpose ONNX (#2216) 
* Add output_padding support for ONNX ConvTranspose

* Add missing codegen

* Fix output padding codegen test
 		2024-08-29 14:07:00 -04:00
..
onnx-tests Add missing output padding to conv transpose ONNX (#2216) 2024-08-29 14:07:00 -04:00
pytorch-tests Refactor xtask to use tracel-xtask and refactor CI workflow (#2063) 2024-08-28 15:57:13 -04:00
src Add missing output padding to conv transpose ONNX (#2216) 2024-08-29 14:07:00 -04:00
Cargo.toml Bump burn version to 0.15.0 2024-08-27 15:13:40 -04:00
DEVELOPMENT.md Add subtract tensor from scalar for ONNX sub op (#1964) 2024-07-05 13:52:02 -05:00
LICENSE-APACHE Update licenses symlinks (#1613) 2024-04-12 14:43:58 -04:00
LICENSE-MIT Update licenses symlinks (#1613) 2024-04-12 14:43:58 -04:00
README.md [refactor] Move burn crates to their own crates directory (#1336) 2024-02-20 13:57:55 -05:00
SUPPORTED-ONNX-OPS.md Update SUPPORTED-ONNX-OPS.md (#2217) 2024-08-29 14:06:42 -04:00

README.md

Importing Models

The Burn project supports the import of models from various frameworks, emphasizing efficiency and compatibility. Currently, it handles two primary model formats:

  1. ONNX: Facilitates direct import, ensuring the model's performance and structure are maintained.

  2. PyTorch: Enables the loading of PyTorch model weights into Burn’s native model architecture, ensuring seamless integration.

Contribution

Interested in contributing to burn-import? Check out our development guide for more information.