What are Anchor Boxes in Object Detection?

Anchor boxes are essential components in object detection models, enabling them to accurately predict and localize multiple objects within an image by serving as a starting point for bounding box predictions. These models, like EfficientDet and YOLO, use anchor boxes to hypothesize locations for objects, which are then refined through regression and classification processes. The model's performance can be significantly enhanced by carefully tuning anchor boxes, especially when dealing with irregularly shaped objects. For instance, YOLOv5 can auto-adjust anchor box distributions based on the training set, which is beneficial for datasets with objects that deviate from standard shapes found in common datasets like COCO. Customizing anchor boxes, particularly for datasets with unique object shapes such as tall or wide objects, can help improve the model's accuracy in localizing true objects in the image, making anchor box configuration a crucial step in developing robust object detection models.