Remove Hindi links

Author: UltralyticsAssistant

classify/train.py

@@ -76,7 +76,7 @@
 
 
 def train(opt, device):
-    """Trains a YOLOv5 model, managing datasets, model optimization, logging, and saving checkpoints."""
+    """Trains a YOLOv5 model on a dataset, managing data loading, optimization, logging, and checkpoint saving."""
     init_seeds(opt.seed + 1 + RANK, deterministic=True)
     save_dir, data, bs, epochs, nw, imgsz, pretrained = (
         opt.save_dir,
@@ -311,9 +311,7 @@ def lf(x):
 
 
 def parse_opt(known=False):
-    """Parses command line arguments for YOLOv5 training including model path, dataset, epochs, and more, returning
-    parsed arguments.
-    """
+    """Parses command line arguments for YOLOv5 training, returning parsed arguments."""
     parser = argparse.ArgumentParser()
     parser.add_argument("--model", type=str, default="yolov5s-cls.pt", help="initial weights path")
     parser.add_argument("--data", type=str, default="imagenette160", help="cifar10, cifar100, mnist, imagenet, ...")
@@ -367,8 +365,17 @@ def main(opt):
 def run(**kwargs):
     """
     Executes YOLOv5 model training or inference with specified parameters, returning updated options.
-
-    Example: from yolov5 import classify; classify.train.run(data=mnist, imgsz=320, model='yolov5m')
+    
+    Args:
+        **kwargs (Any): Arbitrary keyword arguments to override default training or inference options.
+    
+    Returns:
+        (argparse.Namespace): Updated options after parsing and applying overrides.
+    
+    Examples:
+        Run YOLOv5 training with custom parameters
+        >>> from yolov5 import classify
+        >>> classify.train.run(data='mnist', imgsz=320, model='yolov5m')
     """
     opt = parse_opt(True)
     for k, v in kwargs.items():