* Update README.md

* Changing FLOPS to FLOPs.

Co-authored-by: BuildTools <unconfigured@null.spigotmc.org>

* Check CoreML models.train() mode

* Update export.py

* 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 yolo.py

* Update export.py

* fix export grid

* Update export.py, remove detect export attribute

* rearrange if order

* remove --grid, default inplace=False

* rename exp_dynamic to onnx_dynamic, comment

* replace bs with 1 in anchor_grid[i] index 0

* Update export.py

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

* Added support for fp16 (half) to export.py

* minimize code additions

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

* Added flag to enable/disable all inplace and assignment operations

* Removed shape print statements

* Scope Detect/Model import to avoid circular dependency

* PEP8

* create _descale_pred()

* replace lost space

* replace list with tuple

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

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

* Update export.py

* Improved yolo.py profiling

Improved column order and labelling.

* Update yolo.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>

* Add file_size() function

* Update export.py

* Update export.py for 2 dry runs

* Update export.py

* 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>

* 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

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

* Update check_file()

* Update hubconf.py

* Update README.md

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

* option for skip last layer and cuda export support

* added parameter device

* fix import

* cleanup 1

* cleanup 2

* opt-in grid

--grid will export with grid computation, default export will skip grid (same as current)

* default --device cpu

GPU export causes ONNX and CoreML errors.

Co-authored-by: Jan Hajek <jan.hajek@gmail.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* EMA bug fix 2

* update