Fix region-counting indents (#17835)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/guides/region-counting.md

@@ -4,7 +4,7 @@ description: Learn how to use Ultralytics YOLOv8 for precise object counting in
 keywords: object counting, regions, YOLOv8, computer vision, Ultralytics, efficiency, accuracy, automation, real-time, applications, surveillance, monitoring
 ---
 
-# Object Counting in Different Regions using Ultralytics YOLOv8 🚀
+# Object Counting in Different Regions using Ultralytics YOLO 🚀
 
 ## What is Object Counting in Regions?
 
@@ -39,44 +39,45 @@ keywords: object counting, regions, YOLOv8, computer vision, Ultralytics, effici
     === "Python"
 
         ```python
-         import cv2
+        import cv2
-         from ultralytics import solutions
 
-         cap = cv2.VideoCapture("Path/to/video/file.mp4")
+        from ultralytics import solutions
-         assert cap.isOpened(), "Error reading video file"
-         w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
 
-         # Define region points
+        cap = cv2.VideoCapture("Path/to/video/file.mp4")
-         # region_points = [(20, 400), (1080, 400), (1080, 360), (20, 360)] # Pass region as list
+        assert cap.isOpened(), "Error reading video file"
+        w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
 
-         # pass region as dictionary
+        # Define region points
-         region_points = {
+        # region_points = [(20, 400), (1080, 400), (1080, 360), (20, 360)] # Pass region as list
-             "region-01": [(50, 50), (250, 50), (250, 250), (50, 250)],
-             "region-02": [(640, 640), (780, 640), (780, 720), (640, 720)]
-         }
 
-         # Video writer
+        # pass region as dictionary
-         video_writer = cv2.VideoWriter("region_counting.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
+        region_points = {
+            "region-01": [(50, 50), (250, 50), (250, 250), (50, 250)],
+            "region-02": [(640, 640), (780, 640), (780, 720), (640, 720)],
+        }
 
-         # Init Object Counter
+        # Video writer
-         region = solutions.RegionCounter(
+        video_writer = cv2.VideoWriter("region_counting.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
-             show=True,
-             region=region_points,
-             model="yolo11n.pt",
-         )
 
-         # Process video
+        # Init Object Counter
-         while cap.isOpened():
+        region = solutions.RegionCounter(
-             success, im0 = cap.read()
+            show=True,
-             if not success:
+            region=region_points,
-                 print("Video frame is empty or video processing has been successfully completed.")
+            model="yolo11n.pt",
-                 break
+        )
-             im0 = region.count(im0)
-             video_writer.write(im0)
 
-         cap.release()
+        # Process video
-         video_writer.release()
+        while cap.isOpened():
-         cv2.destroyAllWindows()
+            success, im0 = cap.read()
+            if not success:
+                print("Video frame is empty or video processing has been successfully completed.")
+                break
+            im0 = region.count(im0)
+            video_writer.write(im0)
+
+        cap.release()
+        video_writer.release()
+        cv2.destroyAllWindows()
         ```
 
 !!! tip "Ultralytics Example Code"

ultralytics/cfg/__init__.py

@@ -11,7 +11,6 @@ import cv2
 
 from ultralytics.utils import (
     ASSETS,
-    ASSETS_URL,
     DEFAULT_CFG,
     DEFAULT_CFG_DICT,
     DEFAULT_CFG_PATH,

ultralytics/models/yolo/detect/train.py

@@ -146,5 +146,5 @@ class DetectionTrainer(BaseTrainer):
         """Get batch size by calculating memory occupation of model."""
         train_dataset = self.build_dataset(self.trainset, mode="train", batch=16)
         # 4 for mosaic augmentation
-        max_num_obj = max(len(l["cls"]) for l in train_dataset.labels) * 4
+        max_num_obj = max(len(label["cls"]) for label in train_dataset.labels) * 4
         return super().auto_batch(max_num_obj)

ultralytics/utils/torch_utils.py

@@ -301,28 +301,22 @@ def fuse_deconv_and_bn(deconv, bn):
 
 
 def model_info(model, detailed=False, verbose=True, imgsz=640):
-    """
+    """Print and return detailed model information layer by layer."""
-    Model information.
-
-    imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320].
-    """
     if not verbose:
         return
     n_p = get_num_params(model)  # number of parameters
     n_g = get_num_gradients(model)  # number of gradients
     n_l = len(list(model.modules()))  # number of layers
     if detailed:
-        LOGGER.info(
+        LOGGER.info(f"{'layer':>5}{'name':>40}{'gradient':>10}{'parameters':>12}{'shape':>20}{'mu':>10}{'sigma':>10}")
-            f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}"
-        )
         for i, (name, p) in enumerate(model.named_parameters()):
             name = name.replace("module_list.", "")
             LOGGER.info(
-                "%5g %40s %9s %12g %20s %10.3g %10.3g %10s"
+                f"{i:>5g}{name:>40s}{p.requires_grad!r:>10}{p.numel():>12g}{str(list(p.shape)):>20s}"
-                % (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std(), p.dtype)
+                f"{p.mean():>10.3g}{p.std():>10.3g}{str(p.dtype):>15s}"
             )
 
-    flops = get_flops(model, imgsz)
+    flops = get_flops(model, imgsz)  # imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320]
     fused = " (fused)" if getattr(model, "is_fused", lambda: False)() else ""
     fs = f", {flops:.1f} GFLOPs" if flops else ""
     yaml_file = getattr(model, "yaml_file", "") or getattr(model, "yaml", {}).get("yaml_file", "")