* 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>
* Security improvements
* Security improvements
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* Update zipFile to context manager
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* Cleanup
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Failing on missing path, i.e. no 'www.'
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* update coco128-seg comments
* Enables detect.py to use Triton for inference
Triton Inference Server is an open source inference serving software
that streamlines AI inferencing.
https://github.com/triton-inference-server/server
The user can now provide a "--triton-url" argument to detect.py to use
a local or remote Triton server for inference.
For e.g., http://localhost:8000 will use http over port 8000
and grpc://localhost:8001 will use grpc over port 8001.
Note, it is not necessary to specify a weights file to use Triton.
A Triton container can be created by first exporting the Yolov5 model
to a Triton supported runtime. Onnx, Torchscript, TensorRT are
supported by both Triton and the export.py script.
The exported model can then be containerized via the OctoML CLI.
See https://github.com/octoml/octo-cli#getting-started for a guide.
* added triton client to requirements
* fixed support for TFSavedModels in Triton
* reverted change
* Test CoreML update
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* Update ci-testing.yml
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* Use pathlib
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* Refacto DetectMultiBackend to directly accept triton url as --weights http://...
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* Deploy category
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* Update detect.py
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* Update common.py
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* Update common.py
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* Update predict.py
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* Update predict.py
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* Update predict.py
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* Update triton.py
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* Update triton.py
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* Add printout and requirements check
* Cleanup
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* triton fixes
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* fixed triton model query over grpc
* Update check_requirements('tritonclient[all]')
* group imports
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* Fix likely remote URL bug
* update comment
* Update is_url()
* Fix 2x download attempt on http://path/to/model.pt
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* Refactor for simplification
* cleanup
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* Single checkpoint resume
* Update train.py
* Add hyp
* Add hyp
* Add hyp
* FIX
* avoid resume on url data
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* avoid resume on url data
* avoid resume on url data
* Update
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* Pin downloads to release version
Fixes a release version to avoid forward-compatibility issues in future releases.
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* Ability to dowlnoad older assets
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* Cleanup
* Cleanup2
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* PyTorch Hub `_verbose=False` fix2
* Update downloads.py
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* Update hubconf.py
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* Add redundant weights download mirrors
This PR seeks to improve access worldwide to YOLOv5 weights. Universal access to AI for all is our core value, and we are against any censorship or restriction efforts.
I've uploaded the official YOLOv5 v6.1 weights to a primary backup bucket on GCP and to secondary backup on Google Drive at https://drive.google.com/drive/folders/1EFQTEUeXWSFww0luse2jB9M1QNZQGwNl. Autodownload with try the first two locations (GitHuB release assets and GCP bucket), and point users to the Google Drive folder if the first two fail.
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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
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* PyTorch Hub tuple fix
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* PyTorch Hub tuple fix3
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