* 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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* Update
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* Update
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* Update Requirements with PyTorch CUDA
Added --extra-index-url https://download.pytorch.org/whl/cu116 URL to requirements file for ease of creating venv with CUDA enabled PyTorch. Otherwise CPU PyTorch is installed an unable to use local GPUs.
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* Update requirements.txt
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* Update requirements.txt
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* Try to fix DDP mAP drop by setting generator's seed to RANK
* Fix default activation bug
* Update dataloaders.py
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* Update dataloaders.py
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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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* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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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* import re
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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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* Update 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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* TensorFlow macOS AutoUpdate
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* Standardize warnings
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KristenKehrer
Glenn Jocher
Anant Sakhare
Soumik Rakshit
dependabot[bot]
David A. Macey
Forever518
Gaz Iqbal
zombob
FeiGeChuanShu
曾逸夫（Zeng Yifu）
Dhruv Nair
mucunwuxian