* 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>
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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 utils/segment/general
* Align NMS-seg closer to NMS
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* remove easydict dependency
* update
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* update
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* 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
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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)
* speed up evaluation
* 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
* 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
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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
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* Apple Metal Performance Shader (MPS) device support
Following https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/
Should work with Apple M1 devices with PyTorch nightly installed with command `--device mps`. Usage examples:
```bash
python train.py --device mps
python detect.py --device mps
python val.py --device mps
```
* Update device strategy to fix MPS issue
* Add DWConvTranspose2d() module
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* Add DWConvTranspose2d() module
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* Fix
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* Fix
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* New TensorFlow `TFCrossConv()` module
* Move from experimental to common
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* Add C3x
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* Add to C3x to yolo.py
* Add to C3x to tf.py
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* TFC3x bug fix
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* Update tf.py
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* New TensorFlow `TFDWConv()` module
Analog to DWConv() module:
8aa2085a7e/models/common.py (L53-L57)
* Fix and new activations() function
* Update tf.py
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* precommit: yapf
* align isort
* fix
# Conflicts:
# utils/plots.py
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* Update setup.cfg
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* Update wandb_utils.py
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* PyTorch Hub tuple fix
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* removed transpose op for better edgetpu support
* fix for training case
* enabled experimental new quantizer flag
* precalculate add and mul ops at compile time
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* prune unused imports
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* precommit: isort
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* Update isort config
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* Update name
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* Add models/tf.py for TensorFlow and TFLite export
* Set auto=False for int8 calibration
* Update requirements.txt for TensorFlow and TFLite export
* Read anchors directly from PyTorch weights
* Add --tf-nms to append NMS in TensorFlow SavedModel and GraphDef export
* Remove check_anchor_order, check_file, set_logging from import
* Reformat code and optimize imports
* Autodownload model and check cfg
* update --source path, img-size to 320, single output
* Adjust representative_dataset
* Put representative dataset in tfl_int8 block
* detect.py TF inference
* weights to string
* weights to string
* cleanup tf.py
* Add --dynamic-batch-size
* Add xywh normalization to reduce calibration error
* Update requirements.txt
TensorFlow 2.3.1 -> 2.4.0 to avoid int8 quantization error
* Fix imports
Move C3 from models.experimental to models.common
* Add models/tf.py for TensorFlow and TFLite export
* Set auto=False for int8 calibration
* Update requirements.txt for TensorFlow and TFLite export
* Read anchors directly from PyTorch weights
* Add --tf-nms to append NMS in TensorFlow SavedModel and GraphDef export
* Remove check_anchor_order, check_file, set_logging from import
* Reformat code and optimize imports
* Autodownload model and check cfg
* update --source path, img-size to 320, single output
* Adjust representative_dataset
* detect.py TF inference
* Put representative dataset in tfl_int8 block
* weights to string
* weights to string
* cleanup tf.py
* Add --dynamic-batch-size
* Add xywh normalization to reduce calibration error
* Update requirements.txt
TensorFlow 2.3.1 -> 2.4.0 to avoid int8 quantization error
* Fix imports
Move C3 from models.experimental to models.common
* implement C3() and SiLU()
* Fix reshape dim to support dynamic batching
* Add epsilon argument in tf_BN, which is different between TF and PT
* Set stride to None if not using PyTorch, and do not warmup without PyTorch
* Add list support in check_img_size()
* Add list input support in detect.py
* sys.path.append('./') to run from yolov5/
* Add int8 quantization support for TensorFlow 2.5
* Add get_coco128.sh
* Remove --no-tfl-detect in models/tf.py (Use tf-android-tfl-detect branch for EdgeTPU)
* Update requirements.txt
* Replace torch.load() with attempt_load()
* Update requirements.txt
* Add --tf-raw-resize to set half_pixel_centers=False
* Add --agnostic-nms for TF class-agnostic NMS
* Cleanup after merge
* Cleanup2 after merge
* Cleanup3 after merge
* Add tf.py docstring with credit and usage
* pb saved_model and tflite use only one model in detect.py
* Add use cases in docstring of tf.py
* Remove redundant `stride` definition
* Remove keras direct import
* Fix `check_requirements(('tensorflow>=2.4.1',))`
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Glenn Jocher
Ayush Chaurasia
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Yoni Chechik
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Zhiqiang Wang
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