Hello Muhammad_Anas,
It’s great to see your proactive approach to improving your model’s performance!
Regarding the number of annotated images, it is difficult to provide an exact number, as this varies greatly depending on the complexity of the objects and the variability within your dataset. However, datasets in the thousands of images per class are common for good results.
If best.pt is not detecting objects that yolo11n.pt does, ensure your dataset labels correctly correspond to the pretrained model’s classes. You can cross-check using the pretrained weights as a starting point. See YOLO Common Issues - Ultralytics YOLO Docs.
For a precise accuracy measurement, the mAP (mean Average Precision) metrics already provide a percentage-based value reflecting the model’s accuracy. These are standard metrics for object detection models. Val - Ultralytics YOLO Docs
To address overfitting, since you mentioned your dataset is unbalanced, regularly assess the class distribution. A significant imbalance could cause the model to favor the majority class.
I hope these brief points help guide you. Let the Ultralytics team and YOLO community know if you have more questions.