* added imagenet small versions 10,100 and 1000
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* 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
---------
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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
* Resolve precommit utils/segment/plots
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* Resolve precommit utils/segment/general
* Align NMS-seg closer to NMS
* restore deterministic init_seeds code
* remove easydict dependency
* update
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* restore output_to_target mask
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* update
* cleanup
* Remove unused ImageFont import
* Unified NMS
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* DetectMultiBackend compatibility
* segment/predict.py update
* update plot colors
* fix bbox shifted
* sort bbox by confidence
* enable overlap by default
* Merge detect/segment output_to_target() function
* 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
* Add train to CI
* fix precommit
* fix precommit CI
* fix precommit pycocotools
* fix val float issues
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* fix masks float float issues
* suppress errors
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* fix no-predictions plotting bug
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* Add CSV Logger
* fix val len(plot_masks)
* speed up evaluation
* fix process_mask
* fix plots
* update segment/utils build_targets
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* optimize utils/segment/general crop()
* optimize utils/segment/general crop() 2
* minor updates
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* torch.where revert
* downsample only if different shape
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* loss cleanup
* loss cleanup 2
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* loss cleanup 3
* update project names
* Rename -seg yamls from _underscore to -dash
* prepare for yolov5n-seg.pt
* precommit space fix
* add coco128-seg.yaml
* update coco128-seg comments
* cleanup val.py
* Major val.py cleanup
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* precommit fix
* precommit fix
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* optional pycocotools
* remove CI pip install pycocotools (auto-installed now)
* seg yaml fix
* optimize mask_iou() and masks_iou()
* threaded fix
* Major train.py update
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* Major segments/val/process_batch() update
* yolov5/val updates from segment
* process_batch numpy/tensor fix
* opt-in to pycocotools with --save-json
* threaded pycocotools ops for 2x speed increase
* Avoid permute contiguous if possible
* Add max_det=300 argument to both val.py and segment/val.py
* fix onnx_dynamic
* speed up pycocotools ops
* faster process_mask(upsample=True) for predict
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* eliminate permutations for process_mask(upsample=True)
* eliminate permute-contiguous in crop(), use native dimension order
* cleanup comment
* Add Proto() module
* fix class count
* fix anchor order
* broadcast mask_gti in loss for speed
* Cleanup seg loss
* faster indexing
* faster indexing fix
* faster indexing fix2
* revert faster indexing
* fix validation plotting
* Loss cleanup and mxyxy simplification
* Loss cleanup and mxyxy simplification 2
* revert validation plotting
* replace missing tanh
* Eliminate last permutation
* 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
* Fix TF/TFLite export for segmentation model
* Merge master
* Cleanup predict.py mask plotting
* cleanup scale_masks()
* rename scale_masks to scale_image
* cleanup/optimize plot_masks
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* Add Annotator.masks()
* Annotator.masks() fix
* Update plots.py
* Annotator mask optimization
* Rename crop() to crop_mask()
* Do not crop in predict.py
* crop always
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* Merge master
* Add vid-stride from master PR
* Update seg model outputs
* Update seg model outputs
* Add segmentation benchmarks
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* Add segmentation benchmarks
* Add segmentation benchmarks
* Add segmentation benchmarks
* Fix DetectMultiBackend for OpenVINO
* update Annotator.masks
* fix val plot
* revert val plot
* clean up
* revert pil
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* Fix CI error
* fix predict log
* remove upsample
* update interpolate
* fix validation plot logging
* Annotator.masks() cleanup
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* Remove segmentation_model definition
* Restore 0.99999 decimals
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: Jiacong Fang <zldrobit@126.com>
* fix: disable usage of root logger
`logging.basicConfig` configures Python's root logger. This prohibits
fine control of logging, overwrites logging configuration done outside
the package, and is not best practice. Instead, the used logger is now
configured directly, and the root logger is untouched.
Example:
If yolov5 is used as part of another project with some sophisticated
logging, the internal `logging.basicConfig` call overwrites all the
external configuration.
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* Update general.py
* Update general.py
* Comment kaggle
* Uncomment kaggle
Co-authored-by: Maximilian Strobel <Maximilian.Strobel@infineon.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Add nano model to the download list
* Add possibility to also download the "*6" models
* Cleanup
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* Update Objects365.yaml
Download the official Objects365 validation set and convert the labels
* Enforce 4-space indent, reformat and cleanup
* shorten list comprehension
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Glenn Jocher
Nouran Ali
tino926
Shayan Mousavinia
Amir Pourmand
Ayush Chaurasia
Max Strobel
Jirka Borovec
Cristi Fati
Farley Lai
Jebastin Nadar