Also disable `/weights` dir creation when evolving as no weights are saved and empty folder causes user expectations of weights.

* Add suffix checks

* Cleanup

* Cleanup2

* Cleanup3

* added callbacks

* added back callback to main

* added save_dir to callback output

* reduced code count

* updated callbacks

* added default callback class to main, added missing parameters to on_model_save

* Glenn updates

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

* Fixed 'meta' and 'hyp' may out of order when using evolve

* Update gitignore

* Remove assert

* debug0

* trace=not opt.sync

* sync to sync_bn fix

* Cleanup

* Add Annotator() class

* Download Arial

* 2x for loop

* Cleanup

* tuple 2 list

* max_size=1920

* bold logging results to

* tolist()

* im = annotator.im

* PIL save in detect.py

* Smart asarray in detect.py

* revert to cv2.imwrite

* Cleanup

* Return result asarray

* Add `Profile()` profiler

* CamelCase Timeout

* Resize after mosaic

* pillow>=8.0.0

* daemon imwrite

* Add cv2 support

* Remove plot_wh_methods and plot_one_box

* pil=False for hubconf.py annotations

* im.shape bug fix

* colorstr common.py

* join daemons

* Update t.daemon

* Removed daemon saving

* Initial commit

* Update

* Add EarlyStopping feature

* Add comment

* Cleanup

* Cleanup2

* debug

* debug2

* debug3

* debug3

* debug4

* debug5

* debug6

* debug7

* debug8

* debug9

* debug10

* debug11

* debug12

* Cleanup

* Add TODO for known DDP issue

* Implement new headers

* Reformat 1

* Reformat 2

* Reformat 3 - math

* Reformat 4 - yaml

* Update evolution to CSV format

* Update

* Update

* Update

* Update

* Update

* reset args

* reset args

* reset args

* plot_results() fix

* Cleanup

* Cleanup2

* Add cache-on-disk and cache-directory to cache images on disk

* Fix load_image with cache_on_disk

* Add no_cache flag for load_image

* Revert the parts('logging' and a new line) that do not need to be modified

* Add the assertion for shapes of cached images

* Add a suffix string for cached images

* Fix boundary-error of letterbox for load_mosaic

* Add prefix as cache-key of cache-on-disk

* Update cache-function on disk

* Add psutil in requirements.txt

* Update train.py

* Cleanup1

* Cleanup2

* Skip existing npy

* Include re-space

* Export return character fix

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

* added callbacks

* Update callbacks.py

* Update train.py

* Update val.py

* Fix CamlCase add staticmethod

* Refactor logger into callbacks

* Cleanup

* New callback on_val_image_end()

* Add curves and results images to TensorBoard

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

* Add freeze as an argument

I train on different platforms and sometimes I want to freeze some layers. I have to go into the code and change it and also keep track of how many layers I froze on what platform. Please add the number of layers to freeze as an argument in future versions thanks.

* Update train.py

* Update train.py

* Cleanup

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

* PyCharm reformat

* YAML reformat

* Markdown reformat

* Improve docstrings and run names

* default wandb login prompt with timeout

* return key

* Update api_key check logic

* Properly support zipped dataset feature

* update docstring

* Revert tuorial change

* extend changes to log_dataset

* add run name

* bug fix

* bug fix

* Update comment

* fix import check

* remove unused import

* Hardcore .yaml file extension

* reduce code

* Reformat using pycharm

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

* Train from `--data path/to/dataset.zip` feature

* Update dataset_stats()

* cleanup

* cleanup2

* Update download script headers

* cleanup

* bug fix attempt

* bug fix attempt2

* bug fix attempt3

* cleanup

* New CSV Logger

* cleanup

* move batch plots into Logger

* rename comment

* Remove total loss from progress bar

* mloss :-1 bug fix

* Update plot_results()

* Update plot_results()

* plot_results bug fix

* Update loggers

* Config

* Update val.py

* cleanup

* fix1

* fix2

* fix3 and reformat

* format sweep.py

* Logger() class

* cleanup

* cleanup2

* wandb package import fix

* wandb package import fix2

* txt fix

* fix4

* fix5

* fix6

* drop wandb into utils/loggers

* fix 7

* rename loggers/wandb_logging to loggers/wandb

* Update message

* Update message

* Update message

* cleanup

* Fix x axis bug

* fix rank 0 issue

* cleanup

* Refactor train.py

* Update imports

* Update imports

* Update optimizer

* cleanup

* Add `--sync-bn` known issue

* Update train.py

Fix for `KeyError: 'anchors'` error when start hyperparameter evolution:
```bash
python train.py --evolve
```

```bash
Traceback (most recent call last):
  File "E:\yolov5\train.py", line 623, in <module>
    hyp[k] = max(hyp[k], v[1])  # lower limit
KeyError: 'anchors'
```

* evolve command accepts argument for number of generations

* evolve generations argument used in evolve for loop

* evolve argument boolean fixes

* default to 300 evolve generations

* Update train.py

Co-authored-by: John San Soucie <jsansoucie@whoi.edu>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Copy-paste augmentation initial commit

* if any segments

* Add obscuration rejection

* Add copy_paste hyperparameter

* Update comments

For seed=0 on single-GPU.

* Add yolov5/ to path

* rename functions to run()

* cleanup

* rename fix

* CI fix

* cleanup find models/export.py

* Allow config cahnge

* Allow val change in wandb config

* Don't resume transfer learning runs

* Add entity in log dataset

* Update train.py explicit arguments

* Update train.py

* Add run method

* Eliminate `total_batch_size` variable

* cleanup

* Update train.py

* Update DDP for `torch.distributed.run`

* Add LOCAL_RANK

* remove opt.local_rank

* backend="gloo|nccl"

* print

* print

* debug

* debug

* os.getenv

* gloo

* gloo

* gloo

* cleanup

* fix getenv

* cleanup

* cleanup destroy

* try nccl

* return opt

* add --local_rank

* add timeout

* add init_method

* gloo

* move destroy

* move destroy

* move print(opt) under if RANK

* destroy only RANK 0

* move destroy inside train()

* restore destroy outside train()

* update print(opt)

* cleanup

* nccl

* gloo with 60 second timeout

* update namespace printing