text: Title: How to Improve YOLOv8 Fine-Tuning for Better Generalization Across Different Scenes
  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
  
  I'm fine-tuning yolov8n to use it as a person detector for CCTV cameras. However, when I fine-tune the model using the commands below on data from multiple cameras in different scenes(6000 image), the model seems biased toward the training data. The performance of the fine-tuned model on unseen scenes is worse than the pre-trained yolov8n.pt.
  
  I expected that fine-tuning the model would improve its performance, but instead, it has become biased.
  
  What steps can I take to achieve a model with better generalization across different scenes?
  
  ### Additional
  
  results = model.train(data="/content/drive/MyDrive/SuperVision/FineTuneYolo/data.yaml", epochs=1, device=0, imgsz=1280, patience=20)