* Update p2-p7 `models/hub` variants

* Update common.py

* AutoAnchor camelcase corrections

* Attempt `edgetpu-compiler` autoinstall

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

* Update export.py

* Update export.py

* Add `edgetpu_compiler` checks

* Update export.py

* Update export.py

* Update export.py

* Update export.py

* Update export.py

* Update export.py

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

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

* Update export.py

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

Temporary workaround for TFLite INT8 export.

* Update export.py

* Update export.py

* Edge TPU compiler comment

* 7 to 8 fix

Fix for Webcam stop working suddenly (Issue #6197)

* Add Apple CoreML inference

* Cleanup

* Add `tensorrt>=7.0.0` checks

* Update export.py

* Update common.py

* Update export.py

* fix: TensorRT 7 incompatiable

* Add comment

* Add if: else and comment
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* fix export of TorchScript on mobile

* Cleanup
Co-authored-by: yinrong <yinrong@xiaomi.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Global export sort

* Cleanup

updates:
- [github.com/pre-commit/pre-commit-hooks: v4.0.1 → v4.1.0](https://github.com/pre-commit/pre-commit-hooks/compare/v4.0.1...v4.1.0)
- [github.com/asottile/pyupgrade: v2.23.1 → v2.31.0](https://github.com/asottile/pyupgrade/compare/v2.23.1...v2.31.0)
- [github.com/PyCQA/isort: 5.9.3 → 5.10.1](https://github.com/PyCQA/isort/compare/5.9.3...5.10.1)
- [github.com/PyCQA/flake8: 3.9.2 → 4.0.1](https://github.com/PyCQA/flake8/compare/3.9.2...4.0.1)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* Update export documentation

* Cleanup

* Update export.py

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update README.md

* Update README.md

* Update README.md

* Update train.py

* Update train.py
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* Add models/tf.py for TensorFlow and TFLite export

* Set auto=False for int8 calibration

* Update requirements.txt for TensorFlow and TFLite export

* Read anchors directly from PyTorch weights

* Add --tf-nms to append NMS in TensorFlow SavedModel and GraphDef export

* Remove check_anchor_order, check_file, set_logging from import

* Reformat code and optimize imports

* Autodownload model and check cfg

* update --source path, img-size to 320, single output

* Adjust representative_dataset

* Put representative dataset in tfl_int8 block

* detect.py TF inference

* weights to string

* weights to string

* cleanup tf.py

* Add --dynamic-batch-size

* Add xywh normalization to reduce calibration error

* Update requirements.txt

TensorFlow 2.3.1 -> 2.4.0 to avoid int8 quantization error

* Fix imports

Move C3 from models.experimental to models.common

* Add models/tf.py for TensorFlow and TFLite export

* Set auto=False for int8 calibration

* Update requirements.txt for TensorFlow and TFLite export

* Read anchors directly from PyTorch weights

* Add --tf-nms to append NMS in TensorFlow SavedModel and GraphDef export

* Remove check_anchor_order, check_file, set_logging from import

* Reformat code and optimize imports

* Autodownload model and check cfg

* update --source path, img-size to 320, single output

* Adjust representative_dataset

* detect.py TF inference

* Put representative dataset in tfl_int8 block

* weights to string

* weights to string

* cleanup tf.py

* Add --dynamic-batch-size

* Add xywh normalization to reduce calibration error

* Update requirements.txt

TensorFlow 2.3.1 -> 2.4.0 to avoid int8 quantization error

* Fix imports

Move C3 from models.experimental to models.common

* implement C3() and SiLU()

* Add TensorFlow and TFLite Detection

* Add --tfl-detect for TFLite Detection

* Add int8 quantized TFLite inference in detect.py

* Add --edgetpu for Edge TPU detection

* Fix --img-size to add rectangle TensorFlow and TFLite input

* Add --no-tf-nms to detect objects using models combined with TensorFlow NMS

* Fix --img-size list type input

* Update README.md

* Add Android project for TFLite inference

* Upgrade TensorFlow v2.3.1 -> v2.4.0

* Disable normalization of xywh

* Rewrite names init in detect.py

* Change input resolution 640 -> 320 on Android

* Disable NNAPI

* Update README.me --img 640 -> 320

* Update README.me for Edge TPU

* Update README.md

* Fix reshape dim to support dynamic batching

* Fix reshape dim to support dynamic batching

* Add epsilon argument in tf_BN, which is different between TF and PT

* Set stride to None if not using PyTorch, and do not warmup without PyTorch

* Add list support in check_img_size()

* Add list input support in detect.py

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

* Add int8 quantization support for TensorFlow 2.5

* Add get_coco128.sh

* Remove --no-tfl-detect in models/tf.py (Use tf-android-tfl-detect branch for EdgeTPU)

* Update requirements.txt

* Replace torch.load() with attempt_load()

* Update requirements.txt

* Add --tf-raw-resize to set half_pixel_centers=False

* Remove android directory

* Update README.md

* Update README.md

* Add multiple OS support for EdgeTPU detection

* Fix export and detect

* Export 3 YOLO heads with Edge TPU models

* Remove xywh denormalization with Edge TPU models in detect.py

* Fix saved_model and pb detect error

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Fix pre-commit.ci failure

* Add edgetpu in export.py docstring

* Fix Edge TPU model detection exported by TF 2.7

* Add class names for TF/TFLite in DetectMultibackend

* Fix assignment with nl in TFLite Detection

* Add check when getting Edge TPU compiler version

* Add UTF-8 encoding in opening --data file for Windows

* Remove redundant TensorFlow import

* Add Edge TPU in export.py's docstring

* Add the detect layer in Edge TPU model conversion

* Default `dnn=False`

* Cleanup data.yaml loading

* Update detect.py

* Update val.py

* Comments and generalize data.yaml names
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: unknown <fangjiacong@ut.cn>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* Refactor the code to reduece else

* Update metrics.py

* Cleanup
Co-authored-by: Cmos <gen.chen@ubisoft.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* log best result in summary

* comment added

* add space for `flake8`

* log `best/epoch`

* fix `dimension` for epoch

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

* log `best/` in `utils.logger.__init__`

* fix pre-commit

1. missing whitespace around operator
2.  over-indented

* log best.pt metrics at train end

* update

* Update __init__.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* track batch size after autobatch

* remove redundant import

* Update __init__.py

* Update __init__.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

Slightly more efficient than .to(device)

Consider that the default value is CIOU，adjust the order of judgment could reduce the number of judgments.
And “elif CIoU:” didn't need 'if'.
Co-authored-by: 李杰 <360751194@qq.comqq.com>

* OpenVINO export

* Remove timeout

* Add 3 files

* str

* Constrain opset to 12

* Default ONNX opset to 12

* Make dir

* Make dir

* Cleanup

* Cleanup

* check_requirements(('openvino-dev',))

Fix for https://github.com/ultralytics/yolov5/issues/6038

* support specfiy multiple frozen layers

* fix bug

* Cleanup Freeze section

* Cleanup argument
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Check TensorRT>=8.0.0 version

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ciCo-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Increase `ar_thr` from 20 to 100 for better detection on slender (high aspect ratio) objects (#5556)

* Making `ar_thr` available as a hyperparameter

* Disabling ar_thr as hyperparameter and computing from the dataset instead

* Fixing bug in ar_thr computation

* Fix `ar_thr` to 100

Add __init__() function.