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* import always
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If a parameter does not fall into any other category
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When running segmentation predict on gpu, the conversion from tensor to numpy fails. Calling `.cpu()` solves this problem.
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* Update __init__.py
In the Comet logger, when I run train.py, it wants to download the data artifact. It was requiring me to format the 'names' field in the data artifact metadata as a dictionary, so I've changed this so that it also accepts a list.
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* Added cutout import from utils/augmentations.py to use Cutout Aug in data loader by un-commenting line 679, 680, 681
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* disabled upload_dataset flag temporarily due to an artifact related bug
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Failing on missing path, i.e. no 'www.'
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Until more ops are fully supported this update will allow for seamless MPS inference (but slower MPS to CPU transfer before NMS, so slower NMS times).
Partially resolves https://github.com/ultralytics/yolov5/issues/9596
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* Add segment line predictions
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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
* 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 triton.py
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* triton fixes
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* fixed triton model query over grpc
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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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* Add `RandomResizedCrop(ratio)`
* Update ratio
* Update ratio
* Update ratio
* Update ratio
* Update ratio
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* Create augmentations.py
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* Segmentation `polygons2masks_overlap()` in `np.int32`
May resolve https://github.com/ultralytics/yolov5/issues/9461
WARNING: Masks should be uint8 for fastest speed, change needs profiling results to determine impact.
@AyushExel @Laughing-q
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* Update dataloaders.py
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* Standardize warnings
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* Optimize imports
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