* Allow specifying multi_label option for NMS when using torch hub

* Reformat

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

* adding get cropped functionality

* Add target logic in existing functions

* Crops cleanup

* Add dictionary keys: conf, cls, box

* Bug fixes - avoid return after first image

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

* 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

* Add `SPPF()` layer

* Cleanup

* Add credit

* Implement new headers

* Reformat 1

* Reformat 2

* Reformat 3 - math

* Reformat 4 - yaml

* Merge PIL and OpenCV box plotting functions

* Add ASCII check to plot_one_box

* Cleanup

* Cleanup2

* Add yolov5s-ghost.yaml

* Finish C3Ghost

* Add C3Ghost to list

* Add C3Ghost to number of repeats if statement

* Fixes

* Cleanup

* more simpler code for DWConvClass

more simpler code for DWConvClass

* remove DWConv function

* Replace DWConvClass with DWConv

* Add `DWConvClass()`

* Cleanup

* Cleanup2

* Module `super().__init__()`

* remove NMS

* AutoShape PosixPath support

Usage example:

````python
from pathlib import Path

model = ...
file = Path('data/images/zidane.jpg')

results = model(file)
```

* Update common.py

* `Detections()` class `print()` overload

* Update common.py

Fix inference from PIL source.

* Update BGR2RGB ops

* speed improvements

* cleanup

* rotating an image according to its exif tag

* Update common.py

* Update datasets.py

* Update datasets.py

faster

* delete extraneous gpg file

* Update common.py

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

* Added max_det parameters in various places

* 120 character line

* PEP8

* 120 character line

* Update inference default to 1000 instances

* Update inference default to 1000 instances

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

* rename class autoShape -> AutoShape

follow other class naming convention

* rename class autoShape -> AutoShape

follow other classes' naming convention

* rename class autoShape -> AutoShape

* 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>

* 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.

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

* 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>

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

```python
        self.n = len(self.pred)  # number of images (batch size)
```

* Update detections() self.t = tuple()

Fix multiple results.print() bug.

* Update experimental.py

* Update yolo.py

* Add autoShape() speed profiling

* Update common.py

* Create README.md

* Update hubconf.py

* cleanuip

* Update inference default to multi_label=False

* bug fix

* Update plots.py

* Update plots.py

* Update common.py

* Update common.py

* Update common.py

* save results with name

* debug

* save original imgs names

* Update common.py

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