Evaluation Metrics for Object Detection

Working with object detection in deep learning can sometimes be difficult but it is extremely fun. And it feels even more rewarding when we are able to solve the detection problem on our custom dataset. But when working with a custom dataset, how do we know that our object detection algorithm has reached its optimal performance? How do we know that going through a few more epochs won't lead to overfitting of the deep learning algorithm? In fact, these questions are really important when working to competitive datasets like PASCAL VOC or MS COCO dataset as well. These competition datasets have pretty stringent object detection evaluation metrics.