* 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: David Strahm <david.strahm@lambda-it.ch>
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Update parse_opt() to work as in train.py
Change parse_opt() be able to use parse_known_args(), same as in train.py, so export.main() can be called from other script without error.
e.g.:
from yolov5 import export
opt = export.parse_opt(True)
opt.weights = <model_path>
opt.include = ("torchscript", "onnx")
opt.data = <data>
opt.imgsz = [<height>, <width>]
export.main(opt)
Signed-off-by: Johan Bergman <35481994+duran67@users.noreply.github.com>
Signed-off-by: Johan Bergman <35481994+duran67@users.noreply.github.com>
* added embedded meta data to tflite models
* added try block for inference
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* refactored tfite meta data into separate function
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* Creat tmp file in /tmp
* Update export.py
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* Update export.py
* Update export.py
* Update export.py
* Update export.py
* Update common.py
* Update export.py
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* Update common.py
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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
* import re
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* TensorFlow macOS AutoUpdate
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* Standardize warnings
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* Remove `.train()` mode exports
No common use cases.
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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* Update `check_requirements(args, cmds='')`
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* New export decorator
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* New export decorator
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Cleanup
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* rename fcn to func
* rename to @try_export
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* export.py replace `check_file` -> `check_yaml`
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* Add check_file(data) i.e. `--data coco128.yaml`
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* Dynamic batch size support for TensorRT
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* Update export.py
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* Fix optimization profile when batch size is 1
* Warn users if they use batch-size=1 with dynamic
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* More descriptive assertion error
* Fix syntax
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* pre-commit formatting sucked
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* Update export.py
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* Add `--hard-fail` list argument to benchmarks for CI
Will cause CI to fail on a benchmark failure for given indices.
* Update ci-testing.yml
* Attempt Failure (CI should fail)
* Update benchmarks.py
* Update export.py
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* Update benchmarks.py
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* Update ci-testing.yml
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* Update benchmarks.py
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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
Glenn Jocher
Aarni Koskela
David Strahm
Johan Bergman
paradigm
曾逸夫（Zeng Yifu）
Katteria
Colin Wong
daquexian
Sahil Chachra