From 3cd401f0998de3138bd15f84d89d5915b908683e Mon Sep 17 00:00:00 2001
From: UltralyticsAssistant <web@ultralytics.com>
Date: Sun, 25 Aug 2024 00:52:30 +0200
Subject: [PATCH] Remove Hindi links

---
 segment/train.py | 27 ++++++++++++++-------------
 1 file changed, 14 insertions(+), 13 deletions(-)

diff --git a/segment/train.py b/segment/train.py
index 379fed0b2f14..43fa6c44e60d 100644
--- a/segment/train.py
+++ b/segment/train.py
@@ -96,11 +96,7 @@
 
 
 def train(hyp, opt, device, callbacks):
-    """
-    Trains the YOLOv5 model on a dataset, managing hyperparameters, model optimization, logging, and validation.
-
-    `hyp` is path/to/hyp.yaml or hyp dictionary.
-    """
+    """Trains a YOLOv5 segmentation model on a dataset, handling hyperparameters, optimization, and validation."""
     (
         save_dir,
         epochs,
@@ -541,11 +537,7 @@ def lf(x):
 
 
 def parse_opt(known=False):
-    """
-    Parses command line arguments for training configurations, returning parsed arguments.
-
-    Supports both known and unknown args.
-    """
+    """Parses command line arguments for YOLOv5 training configurations, supporting both known and unknown arguments."""
     parser = argparse.ArgumentParser()
     parser.add_argument("--weights", type=str, default=ROOT / "yolov5s-seg.pt", help="initial weights path")
     parser.add_argument("--cfg", type=str, default="", help="model.yaml path")
@@ -748,9 +740,18 @@ def main(opt, callbacks=Callbacks()):
 
 def run(**kwargs):
     """
-    Executes YOLOv5 training with given parameters, altering options programmatically; returns updated options.
-
-    Example: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
+    Executes YOLOv5 training with given parameters, altering options programmatically.
+    
+    Args:
+        **kwargs (Any): Keyword arguments to override default training options.
+    
+    Returns:
+        (argparse.Namespace): Updated options after parsing and applying keyword arguments.
+    
+    Examples:
+        Run YOLOv5 training with custom data and image size
+        >>> import train
+        >>> train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
     """
     opt = parse_opt(True)
     for k, v in kwargs.items():