* add object365

* ADD CONVERSION SCRIPT

* fix transcript

* Reformat and simplify

* spelling

* Update get_objects365.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Create GlobalWheat2020.yaml

* Update and rename visdrone.yaml to VisDrone.yaml

* Update GlobalWheat2020.yaml

* Add yolov5/ to sys.path() for *.py subdir exec

* Update export.py

* Improved yolo.py profiling

Improved column order and labelling.

* Update yolo.py

* Update README.md

dependencies:
ImportError: libGL.so.1: cannot open shared object file: No such file or directory
ImportError: libgthread-2.0.so.0: cannot open shared object file: No such file or directory
ImportError: libSM.so.6: cannot open shared object file: No such file or directory
ImportError: libXrender.so.1: cannot open shared object file: No such file or directory

* replace older apt-get with apt

Code commented for now until a better understanding of the issue, and also code is not cross-platform compatible.
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

This fixes a bug where the '>' symbol in python package requirements was not running correctly with subprocess.check_output() commands.

* ACON Activation batch-size 1 bug path

This is not a great solution to https://github.com/nmaac/acon/issues/4 but it's all I could think of at the moment.

WARNING: YOLOv5 models with MetaAconC() activations are incapable of running inference at batch-size 1 properly due to a known bug in https://github.com/nmaac/acon/issues/4 with no known solution.

* Update activations.py

* Update activations.py

* Update activations.py

* Update activations.py

* Add verbose and update print to logging

* Fix positonal param

* Revert auto formatting changes

* Update hubconf.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update download() for tar.gz files

* Update general.py

* Add file_size() function

* Update export.py

* Update export.py for 2 dry runs

* Update export.py

Per https://pytorch.org/tutorials/recipes/script_optimized.html this should improve performance on torchscript models (and maybe coreml models also since coremltools operates on a torchscript model input, though this still requires testing).

* command line option for line thickness and hiding labels

* command line option for line thickness and hiding labels

* command line option for line thickness and hiding labels

* command line option for line thickness and hiding labels

* command line option for line thickness and hiding labels

* command line option for hiding confidence values

* Update detect.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* more explicit function arguments

* fix typo in detect.py

* revert import order

* revert import order

* remove default value

* ACON Activation Function

## 🚀 Feature

There is a new activation function [ACON (CVPR 2021)](https://arxiv.org/pdf/2009.04759.pdf) that unifies ReLU and Swish. 
ACON is simple but very effective, code is here: https://github.com/nmaac/acon/blob/main/acon.py#L19

![image](https://user-images.githubusercontent.com/5032208/115676962-a38dfe80-a382-11eb-9883-61fa3216e3e6.png)

The improvements are very significant:
![image](https://user-images.githubusercontent.com/5032208/115680180-eac9be80-a385-11eb-9c7a-8643db552c69.png)

## Alternatives

It also has an enhanced version meta-ACON that uses a small network to learn beta explicitly, which may influence the speed a bit.

## Additional context

[Code](https://github.com/nmaac/acon) and [paper](https://arxiv.org/pdf/2009.04759.pdf).

* Update activations.py

* VisDrone Dataset Auto-Download

* add visdrone.yaml

* cleanup

* add VisDrone2019-DET-test-dev

* cleanup VOC

* don't resize up in load_image if augmenting

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

* 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.

Fix for #2810
```shell
python detect.py --source 0
```
introduced by YouTube Livestream Detection PR #2752

* 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>