* Implement yaml.safe_load()

* yaml.safe_dump()

* Update detect.py

* Update detect.py

* Update greetings.yml

* Update cropping

* cleanup

* Update increment_path()

* Update common.py

* Update detect.py

* Update detect.py

* Update detect.py

* Update common.py

* cleanup

* Update detect.py

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

* Ensure dynamic export works succesfully, onnx simplifier optional

* Update export.py

* add dashes

Co-authored-by: Tim <tim.stokman@hal24k.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update FUNDING.yml

* move FUNDING.yml to ./github

* PyTorch Hub cv2 .save() .show() bug fix

cv2.rectangle() was failing on non-contiguous np array inputs. This checks for contiguous arrays and applies is necessary:
```python
imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
```

* Update plots.py

```python
assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to plot_on_box() input image.'
```

* Update hubconf.py

Expand CI tests to OpenCV image.

* ONNX Simplifier

Add ONNX Simplifier to ONNX export pipeline in export.py. Will auto-install onnx-simplifier if onnx is installed but onnx-simplifier is not.

* Update general.py

* add files

* Update README.md

* Update README.md

* Update restapi.py

pretrained=True and model.eval() are used by default when loading a model now, so no need to call them manually.

* PEP8 reformat

* PEP8 reformat

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

* torch.jit.trace(model, img, strict=False)

* Update check_file()

* Update hubconf.py

* Update README.md

* Youtube livestream detection

* dependancy update to auto install pafy

* Remove print

* include youtube_dl in deps

* PEP8 reformat

* youtube url check fix

* reduce lines

* add comment

* update check_requirements

* stream framerate fix

* Update README.md

* cleanup

* PEP8

* remove cap.retrieve() failure code

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

* wandb_logging PEP8 reformat

* Update wandb_utils.py

* Update README with collapsable notes.

* cleanup

* center table

Slight speedup.

* PyTorch Hub model.save() increment as runs/hub/exp

This chane will align PyTorch Hub results saving with the existing unified results saving directory structure of

runs/
  /train
  /detect
  /test
  /hub
    /exp
    /exp2
    ...

* cleanup

* Set resume flag to false

* Check existance of val dataset

* yolotr

* transformer block

* Remove bias in Transformer

* Remove C3T

* Remove a deprecated class

* put the 2nd LayerNorm into the 2nd residual block

* move example model to models/hub, rename to -transformer

* Add module comments and TODOs

* Remove LN in Transformer

* Add comments for Transformer

* Solve the problem of MA with DDP

* cleanup

* cleanup find_unused_parameters

* PEP8 reformat

Co-authored-by: DingYiwei <846414640@qq.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update README with Tips for Best Results tutorial

* Update README.md

* Add label smoothing option

* Correct data type

* add_log

* Remove log

* Add log

* Update loss.py

remove comment (too versbose)

Co-authored-by: phattran <phat.tranhoang@cyberlogitec.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

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

* Add opencv-contrib-python to requirements.txt

* Update requirements.txt

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

* Speed profiling improvements

* Update torch_utils.py

deepcopy() required to avoid adding elements to model.

* Update torch_utils.py

When using tqdm, sometimes it can't print in one line and roll to next line.

I think this should help speed up CUDA inference, as currently models may be running in FP32 inference mode on CUDA devices unnecesarily.

Add stderr=subprocess.STDOUT to catch error messages.

* W&B resume ddp from run link fix

* Native DDP W&B support for training, resuming