* Update GitHub issues templates

* pre-commit fixes

Co-authored-by: pre-commit <pre-commit@example.com>

* define pre-commit

* add CI code

* configure

* apply pre-commit

* fstring

* apply MD

* pre-commit

* Update torch_utils.py

* Update print strings

* notes

* Cleanup code-format.yml

* Update setup.cfg

* Update .pre-commit-config.yaml

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

* Autobatch

* fix mem

* fix mem2

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update train.py

* print result

* Cleanup print result

* swap fix in call

* to 64

* use total

* fix

* fix

* fix

* fix

* fix

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Cleanup printing

* Update final printout

* Update autobatch.py

* Update autobatch.py

* Update autobatch.py

* Minor fixes of the git checkout new branch

* Use em dash to quote

* Revert the change of git checkout

* Maybe we should up-to-date with the upstream/master?

* Add nano model to the download list

* Add possibility to also download the "*6" models

* Cleanup

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

* take EXIF orientation tags into account when fixing corrupt images

* fit 120 char

* sort imports

* Update local exif_transpose comment

We have a local inplace version that is faster than the official as the image is not copied. AutoShape() uses this for Hub models, but here it is not important as the datasets.py usage is infrequent (AutoShape() it is applied every image).

* Update datasets.py

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

* take image files with uppercase extensions into account in autosplit

* case fix

* Refactor implementation

Removes additional variable (capital variable names are also only for global variables), and uses the same methodology as implemented earlier in datasets.py L409.

* Remove redundant rglob characters

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

Update token with workflows permissions

* Add `on_fit_epoch_end` callback

* Add results to train

* Update __init__.py

* Autofix duplicate labels

PR changes duplicate label handling from report error and ignore image-label pair to report warning and autofix image-label pair. 

This should fix this common issue for users and allow everyone to get started and get a model trained faster and easier than before.

* sign fix

* Cleanup

* Increment cache version

* all to any fix

* Update Objects365.yaml

Download the official Objects365 validation set and convert the labels

* Enforce 4-space indent, reformat and cleanup

* shorten list comprehension

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

* Add hyp.scratch-med.yaml

* Update hyp.scratch-med.yaml

* Highlight contributors in README

* Adjust Contact section

* Update README.md

* Improved check_suffix() robustness to `''` and `""`

* Cleanup

* fix dpp with wandb disabled

* PyCharm reformat

* Fix

* fix

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

* fix dpp with wandb disabled

* PyCharm reformat

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

* Add train class filter feature to datasets.py

Allows for training on a subset of total classes if `include_class` list is defined on datasets.py L448:
```python
        include_class = []  # filter labels to include only these classes (optional)
```

* segments fix

* Check `'onnxruntime-gpu' if torch.has_cuda`

* fix indent

* Update P5 models

* Update P6 models

* Update with GFLOPs and Params

* Update with GFLOPs and Params

* Update README

* Update

* Update README

* Update

* Update

* Add times

* Update README

* Update results

* Update results

* Update results

* Update hyps

* Update plots

* Update plots

* Update README.md

* Add nano models to hubconf.py

* update detect.py in order to support torch script

This change assumes the torchscrip file was previously saved with `export.py`

* update `detect.py` for torchscript support

Simple update for torchscript support. Assumes the torchscript file has been generated with `export.py`

* Cleanup

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

* Add OpenCV DNN option for ONNX inference

Usage:

```bash
python detect.py --weights yolov5s.onnx  # ONNX Runtime inference
python detect.py --weights yolov5s.onnx -dnn  # OpenCV DNN inference
```

* DNN prediction to tensor

* Update detect.py

* refactor anchors and anchor_grid in Detect Layer

* fix CI failures by adding compatibility

* fix tf failure

* fix different devices errors

* Cleanup

* fix anchors overwriting issue

* better refactoring

* Remove self.anchor_grid shape check (redundant with self.grid check)

Also PEP8 / 120 line width

* Convert _make_grid() from static to dynamic method

* Remove anchor_grid.to(device)

clone() should already clone to same device as self.anchors

* fix different devices error

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

* use os.path.relpath instead of relative_to

* use os.path.relpath instead of relative_to

* Remove os.path from val.py

* Remove os.path from train.py

* Update detect.py import to os

* Update export.py import to os

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

* Update val.py `pad = 0.0 if task == speed else 0.5`

* Cleanup

* fix different devices bug

* extend _apply() instead of to() for a general fix

* Only apply if Detect() is last layer

Co-authored-by: Jebastin Nadar <njebastin10@gmail.com>

* Indent fix

* Add comment to yolo.py

* Add comment to common.py

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

Co-authored-by: qiningonline <qiningonline@gmail.com>