mAP

"mAP" (mean Average Precision) is a widely used metric for evaluating the performance of object detection and instance segmentation models, aiming to quantify the accuracy of both object localization and classification. Current research focuses on improving mAP scores through advancements in model architectures (e.g., incorporating probabilistic maps, leveraging large language models for map generation, and employing meta-learning techniques), and developing alternative metrics that address limitations of mAP, such as its insensitivity to duplicate predictions. These efforts are crucial for advancing various applications, including autonomous navigation, robotics, and medical image analysis, where accurate and robust object detection and segmentation are essential.