* fix: try 2 - prevent logging config clobbering
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New behavior: loading this repository with `torch.hub.load` only clobbers the logging configuration for logger `yolov5` and its descendants. This is done in a way compatible with Google Colab
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no-op so a pre-commit hook can auto-format files
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Previous behavior: loading this repository with `torch.hub.load` clobbers the existing logging configuration by modifying the root logger's configuration.
New behavior: loading this repository with `torch.hub.load` only clobbers the logging configuration for logger `yolov5` and its descendants.
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Remove box area function and support expandable bbox_iou() calls.
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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
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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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* 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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* reverted change
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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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* DetectMultiBackend compatibility
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Per
```
/content/yolov5/utils/dataloaders.py:458: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
```
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If < 1 or > 1024 set output to default batch size 16.
May partially address https://github.com/ultralytics/yolov5/issues/9156
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* Fix confidence threshold
The confidence is converted to a percentage on line 144, but it is being compared to a default conf_threshold value of a decimal value instead of percent value.
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* allow image dirs
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* Replace torchvision annotator with native local Annotator
* Add colored bounding boxes and rewrite for efficiency
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* Fix pep8 issue
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* Reduced conf_threshold to 0.25 to match defaults
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* allow to serve multiple models using flask restapi
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* fix: added transparent image and empty alt to social bar
* fix: remove underlines
* fix: hide underlines for all social links
* fix: always unzip into folder
* Single checkpoint resume
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* Add hyp
* Add hyp
* FIX
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* avoid resume on url data
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* Add remote ultralytics and check git status with that
* Simplify
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* s fix
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Checking if ``/workspace`` exists is not a reliable method to check if
the process runs in a docker container.
Reusing the logic from the npm "is-docker" package to check if the
process runs in a container.
References: https://github.com/sindresorhus/is-docker/blob/main/index.jsFixes#8710.
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* Fix BGR->RGB Bug in albumentations https://github.com/ultralytics/yolov5/issues/8641
* Change transform methode from cv2 to numpy
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* New `HUBDatasetStats()` class
Usage examples:
```
from utils.dataloaders import *
stats = HUBDatasetStats('coco128.yaml', autodownload=True) # method 1
stats = HUBDatasetStats('path/to/coco128_with_yaml.zip') # method 1
stats.get_json(save=False)
stats.process_images()
```
@kalenmike
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* assert torch!=1.12.0 for DDP training
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* Refactor optimizer initialization
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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.
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* Attempt Failure (CI should fail)
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* attempt at reproducibility
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* use deterministic algs
* fix everything :)
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* Add filesize to benchmarks.py
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* HUB dataset_stats() error reporting
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* Reject webcam inference on Colab/Kaggle
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* AutoBatch checks against failed solutions
@kalenmike this is a simple improvement to AutoBatch to verify that returned solutions have not already failed, i.e. return batch-size 8 when 8 already produced CUDA out of memory.
This is a halfway fix until I can implement a 'final solution' that will actively verify the solved-for batch size rather than passively assume it works.
* Update autobatch.py
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Ryan Echols
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Forever518
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mucunwuxian
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Ayush Chaurasia
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黄不盈
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