* fix: disable usage of root logger

`logging.basicConfig` configures Python's root logger. This prohibits
fine control of logging, overwrites logging configuration done outside
the package, and is not best practice. Instead, the used logger is now
configured directly, and the root logger is untouched.

Example:
    If yolov5 is used as part of another project with some sophisticated
    logging, the internal `logging.basicConfig` call overwrites all the
    external configuration.

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

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

* Update general.py

* Update general.py

* Comment kaggle

* Uncomment kaggle

Co-authored-by: Maximilian Strobel <Maximilian.Strobel@infineon.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* New `DetectMultiBackend()` class

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

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

* pb to pt fix

* Cleanup

* explicit apply_classifier path

* Cleanup2

* Cleanup3

* Cleanup4

* Cleanup5

* Cleanup6

* val.py MultiBackend inference

* warmup fix

* to device fix

* pt fix

* device fix

* Val cleanup

* COCO128 URL to assets

* half fix

* detect fix

* detect fix 2

* remove half from DetectMultiBackend

* training half handling

* training half handling 2

* training half handling 3

* Cleanup

* Fix CI error

* Add torchscript _extra_files

* Add TorchScript

* Add CoreML

* CoreML cleanup

* New `DetectMultiBackend()` class

* pb to pt fix

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

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

* Cleanup

* explicit apply_classifier path

* Cleanup2

* Cleanup3

* Cleanup4

* Cleanup5

* Cleanup6

* val.py MultiBackend inference

* warmup fix

* to device fix

* pt fix

* device fix

* Val cleanup

* COCO128 URL to assets

* half fix

* detect fix

* detect fix 2

* remove half from DetectMultiBackend

* training half handling

* training half handling 2

* training half handling 3

* Cleanup

* Fix CI error

* Add torchscript _extra_files

* Add TorchScript

* Add CoreML

* CoreML cleanup

* revert default to pt

* Add Usage examples

* Cleanup val

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

* Clip Objects365 autodownload labels (#5214)

Fixes out of bounds labels that seem to affect ~10% of images in dataset.

* Inplace ops

* Add optional dataset.yaml `path` attribute

@KalenMike

* pass locals to python scripts

* handle lists

* update coco128.yaml

* Capitalize first letter

* add test key

* finalize GlobalWheat2020.yaml

* finalize objects365.yaml

* finalize SKU-110K.yaml

* finalize SKU-110K.yaml

* finalize VisDrone.yaml

* NoneType fix

* update download comment

* voc to VOC

* update

* update VOC.yaml

* update VOC.yaml

* remove dashes

* delete get_voc.sh

* force coco and coco128 to ../datasets

* Capitalize Argoverse_HD.yaml

* Capitalize Objects365.yaml

* update Argoverse_HD.yaml

* coco segments fix

* VOC single-thread

* update Argoverse_HD.yaml

* update data_dict in test handling

* create root

* SKU-110K CVPR2019 Dataset Auto-Download

This PR adds the **SKU-110K CVPR2019 Dataset** https://github.com/eg4000/SKU110K_CVPR19 to the list of YOLOv5 🚀  auto-download list.

- [x] [COCO](https://cocodataset.org/#home)
- [x] [COCO128](https://www.kaggle.com/ultralytics/coco128)
- [x] [VOC](http://host.robots.ox.ac.uk/pascal/VOC/)
- [x] [Argoverse-HD](http://www.cs.cmu.edu/~mengtial/proj/streaming/)
- [x] [VisDrone2019-DET](https://github.com/VisDrone/VisDrone-Dataset)
- [x] [GlobalWheat-2020](http://www.global-wheat.com/)
- [ ] [KITTI](https://www.kaggle.com/twaldo/kitti-object-detection)
- [x] [SKU110](https://github.com/eg4000/SKU110K_CVPR19) (grocery store items)
- [ ] [CityScapes](https://www.cityscapes-dataset.com/)
- [x] [Objects365](https://www.objects365.org/overview.html)
- [ ] [OpenImages](https://storage.googleapis.com/openimages/web/index.html)

```yaml
# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19
# Train command: python train.py --data SKU-110K.yaml
# Default dataset location is next to YOLOv5:
#   /parent_folder
#     /datasets/SKU-110K
#     /yolov5
```

* Update SKU-110K.yaml

* Update SKU-110K.yaml