* A minor correction in a comment
I added the 'h' in 'https' in the link to the label smoothing issue.
Signed-off-by: Kumar Selvakumaran <62794224+kumar-selvakumaran@users.noreply.github.com>
* Auto-format by https://ultralytics.com/actions
---------
Signed-off-by: Kumar Selvakumaran <62794224+kumar-selvakumaran@users.noreply.github.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
* 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>
* 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
* New TensorFlow `TFCrossConv()` module
* Move from experimental to common
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Add C3x
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Add to C3x to yolo.py
* Add to C3x to tf.py
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* TFC3x bug fix
* TFC3x bug fix
* TFC3x bug fix
* Add TFDWConv g==c1==c2 check
* Add comment
* 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 setup.cfg
* Update setup.cfg
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* Update wandb_utils.py
* Update augmentations.py
* Update setup.cfg
* Update yolo.py
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* Update val.py
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* simplify colorstr
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* val run fix
* export.py last comma
* Update export.py
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* Update hubconf.py
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* PyTorch Hub tuple fix
* PyTorch Hub tuple fix2
* PyTorch Hub tuple fix3
* Update setup
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>
* precommit: isort
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* Update isort config
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* Update name
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>
* 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>
* Added flag to enable/disable all inplace and assignment operations
* Removed shape print statements
* Scope Detect/Model import to avoid circular dependency
* PEP8
* create _descale_pred()
* replace lost space
* replace list with tuple
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Kumar Selvakumaran
Glenn Jocher
Paula Derrenger
Jirka Borovec
Jebastin Nadar
Jiacong Fang
KEN
jluntamazon
lorne
Yi_Zhang