* Update LICENSE to AGPL-3.0
This pull request updates the license of the YOLOv5 project from GNU General Public License v3.0 (GPL-3.0) to GNU Affero General Public License v3.0 (AGPL-3.0).
We at Ultralytics have decided to make this change in order to better protect our intellectual property and ensure that any modifications made to the YOLOv5 source code will be shared back with the community when used over a network.
AGPL-3.0 is very similar to GPL-3.0, but with an additional clause to address the use of software over a network. This change ensures that if someone modifies YOLOv5 and provides it as a service over a network (e.g., through a web application or API), they must also make the source code of their modified version available to users of the service.
This update includes the following changes:
- Replace the `LICENSE` file with the AGPL-3.0 license text
- Update the license reference in the `README.md` file
- Update the license headers in source code files
We believe that this change will promote a more collaborative environment and help drive further innovation within the YOLOv5 community.
Please review the changes and let us know if you have any questions or concerns.
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Update headers to AGPL-3.0
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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Allow int8 quantization for export_tfjs
--int8 param currently has no effect on export_tfjs. With this change,
` python export.py --weights ../path/to/best.pt --include tfjs --int8` will add the --quantize_uint8 param to the tensorflowjs_converter script, greatly reducing model size for web usage.
Signed-off-by: David Strahm <david.strahm@lambda-it.ch>
* Update Dockerfile
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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* Update Dockerfile `FROM pytorch/pytorch:latest`
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* isort
* precommit
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* spelling
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* Update .pre-commit-config.yaml
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* Cleanup
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* Cleanup
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* Cleanup
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* Cleanup
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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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* Create docker.yml
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* Update docker.yml
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* Update docker.yml
* Update docker.yml
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* Cleanup
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* FROM nvcr.io/nvidia/pytorch:22.04-py3
* Update Docker
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* Update Docker
* Update TRT auto-install
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* Cleanup
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* Cleanup cpu
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Glenn Jocher