* feat: change file suffix in pythonic way

* fix: enforce binding addresses order

* fix: enforce binding addresses order

* `try: except (..., wandb.errors.UsageError)`

* bug fix

When OpenCV retrieving image fail, original code would modify source images **inplace**, which may result in plotting bounding boxes on a black image. That is, before inference, source image `im0s[i]` is OK, but after inference before `Process predictions`,  `im0s[i]` may have been changed.

Video errors may occur.

* Update val.py

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shallow copy modify originals

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* Update AutoShape()

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* Update

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* Update

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* Update hubconf.py

* Update common.py

* smart param selection

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Export multiple models in series by adding additional `*.pt` files to the `--weights` argument, i.e.:

```bash
python export.py --include tflite --weights yolov5n.pt  # export 1 model
python export.py --include tflite --weights yolov5n.pt yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.pt  # export 5 models
```

* Update export.py

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Fix for ultralytics#5916

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…#5920)

This reverts commit 360eec6.

…#5937)

* Find out a bug. When set batch_size = -1 to use the autobatch.

reproduce:

* Fix type conflict

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* fix .gitignore not tracking existing folders

fix .gitignore so that the files that are in the repository are actually being tracked.

Everything in the data/ folder is ignored, which also means the subdirectories are ignored. Fix so that the subdirectories and their contents are still tracked.

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Replace 'training_result' with 'best_fitness' in strip_optimizer() to match key with ckpt from train.py

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* fix imgsz bug

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Add __init__() function.

…h aspect ratio) objects (ultralytics#5556)

* Making `ar_thr` available as a hyperparameter

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Fix for ultralytics#6038

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Consider that the default value is CIOU，adjust the order of judgment could reduce the number of judgments.
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Slightly more efficient than .to(device)

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ValueError: all the input arrays must have same number of dimensions

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* detect.py TF inference

* weights to string

* weights to string

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TensorFlow 2.3.1 -> 2.4.0 to avoid int8 quantization error

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TensorFlow 2.3.1 -> 2.4.0 to avoid int8 quantization error

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* sys.path.append('./') to run from yolov5/

* Add int8 quantization support for TensorFlow 2.5

* Add get_coco128.sh

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* Update requirements.txt

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* Remove android directory

* Update README.md

* Update README.md

* Add multiple OS support for EdgeTPU detection

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updates:
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- [github.com/asottile/pyupgrade: v2.23.1 → v2.31.0](asottile/pyupgrade@v2.23.1...v2.31.0)
- [github.com/PyCQA/isort: 5.9.3 → 5.10.1](PyCQA/isort@5.9.3...5.10.1)
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* Global export sort

* Cleanup

* fix export of TorchScript on mobile

* Cleanup

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* fix: TensorRT 7 incompatiable

* Add comment

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* Add `tensorrt>=7.0.0` checks

* Update export.py

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Fix for Webcam stop working suddenly (Issue ultralytics#6197)

* Edge TPU compiler comment

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* TFLite `--int8` 'flatbuffers==1.12' fix

Temporary workaround for TFLite INT8 export.

* Update export.py

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* TFLite `--int8` 'flatbuffers==1.12' fix 2

Reorganizes ultralytics#6216 fix to update before `tensorflow` import so no restart required.

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Attempt to install edgetpu-compiler dependency if missing on Linux.

* Update export.py

* Update export.py

* Update p2-p7 `models/hub` variants

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* Fix cmd string on tfjs export

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* Add `is_kaggle()` function

Return True if environment is Kaggle Notebook.

* Remove root loggers only if is_kaggle() == True

* Update general.py

* Fix device count check()

* Update torch_utils.py

* Update torch_utils.py

* Update hubconf.py

* Fixing bug multi-gpu training

This solves this issue: ultralytics#6297 (comment)

* Update torch_utils.py for pep8

* `select_device()` cleanup

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* Update torch_utils.py

* Update torch_utils.py

* Update torch_utils.py

* Update torch_utils.py

* Fix `train.py` parameter groups desc error

* Cleanup

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* Remove `dataset_stats()` autodownload capability

@kalenmike security update per Slack convo

* Update datasets.py

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Minor style changes.

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New environment variable `VERBOSE`

…#6324)

* log best.pt metrics at train end

* update

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21.12 generates dockerhub errors so rolling back to 21.10 with latest pytorch install. Not sure if this torch install will work on non-GPU dockerhub autobuild so this is an experiment.