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?
 
@@ -40,6 +40,7 @@ keywords: object counting, regions, YOLOv8, computer vision, Ultralytics, effici
 
         ```python
         import cv2
+
         from ultralytics import solutions
 
         cap = cv2.VideoCapture("Path/to/video/file.mp4")
@@ -52,7 +53,7 @@ keywords: object counting, regions, YOLOv8, computer vision, Ultralytics, effici
         # pass region as dictionary
         region_points = {
             "region-01": [(50, 50), (250, 50), (250, 250), (50, 250)],
-             "region-02": [(640, 640), (780, 640), (780, 720), (640, 720)]
+            "region-02": [(640, 640), (780, 640), (780, 720), (640, 720)],
         }
 
         # Video writer

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):
-    """
-    Model information.
-
-    imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320].
-    """
+    """Print and return detailed model information layer by layer."""
     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(
-            f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}"
-        )
+        LOGGER.info(f"{'layer':>5}{'name':>40}{'gradient':>10}{'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"
-                % (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std(), p.dtype)
+                f"{i:>5g}{name:>40s}{p.requires_grad!r:>10}{p.numel():>12g}{str(list(p.shape)):>20s}"
+                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", "")