text: Title: The prediction results of the segmentation model are strange
  Body: ### Search before asking
  
  - [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/orgs/ultralytics/discussions) and found no similar questions.
  
  
  ### Question
  
  ## Background  
  To obtain contour data of a specific object, I created a segmentation model using the following steps:  
  
  1. Generated a mask for the object using `sam2.1_b.pt` and created labels using `result.masks.xyn.pop()`.  
  ```python
  normalized_contour = result.masks.xyn.pop()
  
  with open(os.path.join(output_label_path, img_file.replace('.jpg', '.txt')), 'w') as f:
      class_id = 0
      points_str = " ".join([f"{x:.4f} {y:.4f}" for x, y in normalized_contour])
      output_str = f"{class_id} {points_str}"
      f.write(output_str) 
  ```
  
  2. Fine-tuned `yolo11n-seg.pt` using the generated labels.
  ```python
  model = YOLO('yolo11n-seg.pt')
  model.train(data="dataset.yaml", epochs=1000, patience=50, batch=16, imgsz=1024, device=0)
  ```
  
  3. Performed predictions using the fine-tuned model (`best.pt`).
  ```python
  result = model(img_path, device=0)[0]
  ```
  
  ## Isuue
  The prediction results are shown in the image below.
  When examining the boundary between the mask and the bounding box (circled areas), certain parts of the mask protrude in a square-like shape.
  
  ## Question
  
  Why is this happening?
  Are there any possible solutions to this issue?
  
  ![Image](https://github.com/user-attachments/assets/06e27401-b00b-458f-86a7-84f588ffc465)
  
  
  ### Additional
  
  _No response_