* 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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* Security improvements
* Security improvements
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* Security improvements
* Security improvements
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* Checks conf_thres and iou_thres at beign
Why checks conf_thres after operation with it?
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* Update general.py
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* New global TQDM_BAR_FORMAT
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* fix: try 2 - prevent logging config clobbering
Previous behavior: loading this repository with `torch.hub.load` clobbers the existing logging configuration by modifying the root logger's configuration.
New behavior: loading this repository with `torch.hub.load` only clobbers the logging configuration for logger `yolov5` and its descendants. This is done in a way compatible with Google Colab
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* chore: fill in comment
no-op so a pre-commit hook can auto-format files
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Previous behavior: loading this repository with `torch.hub.load` clobbers the existing logging configuration by modifying the root logger's configuration.
New behavior: loading this repository with `torch.hub.load` only clobbers the logging configuration for logger `yolov5` and its descendants.
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* AutoCache
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* AutoCache
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* fix: switched from suffix checks to archive checks
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* Cleanup
seems like both functions accept Path type input so removing str()
* import always
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* Update zipFile to context manager
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* Cleanup
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* Cleanup
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* fix CI
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* fix CI
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* fix CI
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* fix CI
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* Standardize warnings
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* Fix duplicate plots.py
* Fix check_font()
* # torch.use_deterministic_algorithms(True)
* update doc detect->predict
* Resolve precommit for segment/train and segment/val
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* Resolve precommit for utils/segment
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* Resolve precommit min_wh
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* Resolve precommit utils/segment/general
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* update
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* DetectMultiBackend compatibility
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* sort bbox by confidence
* enable overlap by default
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* Start segmentation CI
* fix plots
* Update ci-testing.yml
* fix training whitespace
* optimize process mask functions (can we merge both?)
* Update predict/detect
* Update plot_images
* Update plot_images_and_masks
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* fix
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* fix masks float float issues
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* fix no-predictions plotting bug
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* Add CSV Logger
* fix val len(plot_masks)
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* fix process_mask
* fix plots
* update segment/utils build_targets
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* optimize utils/segment/general crop()
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* minor updates
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* torch.where revert
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* loss cleanup
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* loss cleanup 3
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* Major val.py cleanup
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* precommit fix
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* optional pycocotools
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* Major train.py update
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* Major segments/val/process_batch() update
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* fix onnx_dynamic
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* eliminate permutations for process_mask(upsample=True)
* eliminate permute-contiguous in crop(), use native dimension order
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* Loss cleanup and mxyxy simplification 2
* revert validation plotting
* replace missing tanh
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* delete unneeded .float()
* Remove MaskIOULoss and crop(if HWC)
* Final v6.3 SegmentationModel architecture updates
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* Add support for TF export
* remove debugger trace
* add call
* update
* update
* Merge master
* Merge master
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* Update dataloaders.py
* Restore CI
* Update dataloaders.py
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* Cleanup predict.py mask plotting
* cleanup scale_masks()
* rename scale_masks to scale_image
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* Add Annotator.masks()
* Annotator.masks() fix
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* Annotator mask optimization
* Rename crop() to crop_mask()
* Do not crop in predict.py
* crop always
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* Merge master
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* Update seg model outputs
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* Add segmentation benchmarks
* Add segmentation benchmarks
* Add segmentation benchmarks
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* clean up
* revert pil
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* Fix CI error
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* update interpolate
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* Annotator.masks() cleanup
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* Remove segmentation_model definition
* Restore 0.99999 decimals
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