New DetectMultiBackend() class (#5549)

* New `DetectMultiBackend()` class

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* pb to pt fix

* Cleanup

* explicit apply_classifier path

* Cleanup2

* Cleanup3

* Cleanup4

* Cleanup5

* Cleanup6

* val.py MultiBackend inference

* warmup fix

* to device fix

* pt fix

* device fix

* Val cleanup

* COCO128 URL to assets

* half fix

* detect fix

* detect fix 2

* remove half from DetectMultiBackend

* training half handling

* training half handling 2

* training half handling 3

* Cleanup

* Fix CI error

* Add torchscript _extra_files

* Add TorchScript

* Add CoreML

* CoreML cleanup

* New `DetectMultiBackend()` class

* pb to pt fix

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Cleanup

* explicit apply_classifier path

* Cleanup2

* Cleanup3

* Cleanup4

* Cleanup5

* Cleanup6

* val.py MultiBackend inference

* warmup fix

* to device fix

* pt fix

* device fix

* Val cleanup

* COCO128 URL to assets

* half fix

* detect fix

* detect fix 2

* remove half from DetectMultiBackend

* training half handling

* training half handling 2

* training half handling 3

* Cleanup

* Fix CI error

* Add torchscript _extra_files

* Add TorchScript

* Add CoreML

* CoreML cleanup

* revert default to pt

* Add Usage examples

* Cleanup val

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

utils/general.py

@@ -785,7 +785,8 @@ def print_mutation(results, hyp, save_dir, bucket):
 
 
 def apply_classifier(x, model, img, im0):
-    # Apply a second stage classifier to yolo outputs
+    # Apply a second stage classifier to YOLO outputs
+    # Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval()
     im0 = [im0] if isinstance(im0, np.ndarray) else im0
     for i, d in enumerate(x):  # per image
         if d is not None and len(d):