Omit ultralytics/utils/callbacks from coverage (#4345)

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

docs/models/index.md

@@ -44,7 +44,7 @@ This example provides simple inference code for YOLO, SAM and RTDETR models. For
         model.info()
 
         # Train the model on the COCO8 example dataset for 100 epochs
-        results model.train(data='coco8.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
 
         # Run inference with the YOLOv8n model on the 'bus.jpg' image
         results = model('path/to/bus.jpg')

docs/models/rtdetr.md

@@ -46,7 +46,7 @@ You can use RT-DETR for object detection tasks using the `ultralytics` pip packa
         model.info()
 
         # Train the model on the COCO8 example dataset for 100 epochs
-        results model.train(data='coco8.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
 
         # Run inference with the RT-DETR-l model on the 'bus.jpg' image
         results = model('path/to/bus.jpg')

docs/models/yolo-nas.md

@@ -62,7 +62,7 @@ In this example we validate YOLO-NAS-s on the COCO8 dataset.
         model.info()
 
         # Validate the model on the COCO8 example dataset
-        results model.val(data='coco8.yaml')
+        results = model.val(data='coco8.yaml')
 
         # Run inference with the YOLO-NAS-s model on the 'bus.jpg' image
         results = model('path/to/bus.jpg')

docs/models/yolov3.md

@@ -69,7 +69,7 @@ You can use YOLOv3 for object detection tasks using the Ultralytics repository.
         model.info()
 
         # Train the model on the COCO8 example dataset for 100 epochs
-        results model.train(data='coco8.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
 
         # Run inference with the YOLOv3n model on the 'bus.jpg' image
         results = model('path/to/bus.jpg')

docs/models/yolov5.md

@@ -74,7 +74,7 @@ You can use YOLOv5u for object detection tasks using the Ultralytics repository.
         model.info()
 
         # Train the model on the COCO8 example dataset for 100 epochs
-        results model.train(data='coco8.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
 
         # Run inference with the YOLOv5n model on the 'bus.jpg' image
         results = model('path/to/bus.jpg')

docs/models/yolov6.md

@@ -56,7 +56,7 @@ You can use YOLOv6 for object detection tasks using the Ultralytics pip package.
         model.info()
 
         # Train the model on the COCO8 example dataset for 100 epochs
-        results model.train(data='coco8.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
 
         # Run inference with the YOLOv6n model on the 'bus.jpg' image
         results = model('path/to/bus.jpg')

docs/models/yolov8.md

@@ -101,7 +101,7 @@ You can use YOLOv8 for object detection tasks using the Ultralytics pip package.
         model.info()
 
         # Train the model on the COCO8 example dataset for 100 epochs
-        results model.train(data='coco8.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
 
         # Run inference with the YOLOv8n model on the 'bus.jpg' image
         results = model('path/to/bus.jpg')