* A minor correction in a comment
I added the 'h' in 'https' in the link to the label smoothing issue.
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* Auto-format by https://ultralytics.com/actions
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* Parametrize multiple of number of channels in Conv
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* Fix fp16 (--half) support for TritonRemoteModel
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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.
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* Update headers to AGPL-3.0
---------
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* Apply make_divisible for onnx models in Autoshape
At line 697 we have this `make_divisible` function for pytorch models.
* Context: we want to run inference on varied input sizes instead of fixed image size.
* When I test an image of size [720, 720] for a pytorch model (e.g., yolov5n.pt), we can see that it will be reshaped to [736, 736] by the function. This is as expected.
* When I test the same image for the onnx model (e.g., yolov5n.onnx, exported with `--dynamic`), I got an error and it's due to the indivisible problem
```
onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Non-zero status code returned while running Concat node. Name:'Concat_143' Status Message: concat.cc:156 PrepareForCompute Non concat axis dimensions must match: Axis 3 has mismatched dimensions of 45 and 46
```
The simple solution is to enable the `make_divisible` function for onnx model too.
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* revise indent
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* Apply make_divisible to all formats
All formats from DetectMultiBackend should have default stride=32
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Update common.py
We have a division at line 694, and then a multiplication at line 695, so it makes `y*g` not an integer. And since `shape1` will be used at line 697 to ensure the size is divisible by the `stride`, this may lead to different image size.
In my experiment, my image is [640, 640], it's divisible by the default stride 32, but I found that the result is changed to [672, 672] after line 697. So the final detection result is slightly different from that directly using the `detect.py` script, which does not call the AutoShape methods.
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* added embedded meta data to tflite models
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* Allow PyTorch Hub results to display in notebooks
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* Try to fix DDP mAP drop by setting generator's seed to RANK
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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
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* Test CoreML update
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* Deploy category
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* Fix likely remote URL bug
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* Detect() and Segment() fixes for CoreML and Paddle
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* Fix duplicate plots.py
* Fix check_font()
* # torch.use_deterministic_algorithms(True)
* update doc detect->predict
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* DetectMultiBackend compatibility
* segment/predict.py update
* update plot colors
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* 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?)
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* Update plot_images_and_masks
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* optional pycocotools
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* Major segments/val/process_batch() update
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* threaded pycocotools ops for 2x speed increase
* Avoid permute contiguous if possible
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* eliminate permutations for process_mask(upsample=True)
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* 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
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* Remove MaskIOULoss and crop(if HWC)
* Final v6.3 SegmentationModel architecture updates
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* Add support for TF export
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* Add Annotator.masks()
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* crop always
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* added dilate conv support
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