* W&B feature improvements

This PR add:
* Class to id labels. Now, the caption of bounding boxes will display the class name and the class confidence score.
* The project name is set to "Yolov5" and the run name will be set to opt.logdir

* cleanup

* remove parenthesis on caption

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

* Add wandb metric logging and bounding box debugging

* Improve formatting, readability

* Remove mutliple path for init, improve formatting

* Add wandb params

* Remove typecasting in bbox coordinates and reformat

* Cleanup

* add wandb to requirements.txt

* minor updates to test.py

* general reorg

* reduce --log-imgs to 10

* clean wandb import

* reverse wandb import assert

* add except AssertionError to try import

* move wandb init to all global ranks

* replace print() with logger.info()

* replace print() with logger.info()

* move wandb.init() bug fix

* project PosixPath to basename bug fix

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

* fix/hyper

* Hyp giou check to train.py

* restore general.py

* train.py overwrite fix

* restore general.py and pep8 update

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

* comment

* fix parsing

* fix evolve

* folder

* tqdm

* Update train.py

* Update train.py

* reinstate anchors into meta dict

anchor evolution is working correctly now

* reinstate logger

prefer the single line readout for concise logging, which helps simplify notebook and tutorials etc.

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

* initial commit

* Update general.py

Indent update

* Update general.py

refactor duplicate code

* 200 dpi

* Update train.py with simplified ckpt names

* Return default hyps to hyp.scratch.yaml

Leave line commented for future use once mystery of best finetuning hyps to apply becomes clearer.

* Force test_batch*_pred.jpg replot on final epoch

This will allow you to see predictions final testing run after training completes in runs/exp0

* Class frequency not calculated on resuming training

Calculation of class frequency is not needed when resuming training.
Anchors can still be recalculated whether resuming or not.

* Check rank for autoanchor

* Update train.py

no autoanchor checks on resume

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

* initial commit

* add weight backup dir on resume

* Fix ema error in DDP mode

* Update train.py

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

* single command --resume

* else check files, remove TODO

* argparse.Namespace()

* tensorboard lr

* bug fix in get_latest_run()

* initial commit

* remove yolov3-spp from test.py study

* update study --img range

* update mAP

* cleanup and speed updates

* update README plot

* Add logging setup

* Fix fusing layers message

* Fix logging does not have end

* Add logging

* Change logging to use logger

* Update yolo.py

I tried this in a cloned branch, and everything seems to work fine

* Update yolo.py

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

* Change print to logging

* Clean function set_logging

* Add line spacing

* Change leftover prints to log

* Fix scanning labels output

* Fix rank naming

* Change leftover print to logging

* Reorganized DDP variables

* Fix type error

* Make quotes consistent

* Fix spelling

* Clean function call

* Add line spacing

* Update datasets.py

* Update train.py

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

* initial commit

* move download scripts into data/scripts

* new check_dataset() function in general.py

* move check_dataset() out of with context

* Update general.py

* DDP update

* Update general.py

* Single-source training

* Extract hyperparameters into seperate files

* weight decay scientific notation yaml reader bug fix

* remove import glob

* intersect_dicts() implementation

* 'or' bug fix

* .to(device) bug fix

* train.py --logdir argparser addition

* train.py --logdir argparser addition

* Add support for multi-node DDP

* Remove local_rank confusion

* Fix spacing

* Fix missing imports

* Update detect.py

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

* expand imports

* optimize

* miss

* fix

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* PyTorch have Automatic Mixed Precision (AMP) Training.

* Fixed the problem of inconsistent code length indentation

* Fixed the problem of inconsistent code length indentation

* Mixed precision training is turned on by default