How does the concept of Intersection over Union (IoU) improve the evaluation of object detection models compared to using quadratic loss?
Wednesday, 22 May 2024 by EITCA Academy
Intersection over Union (IoU) is a critical metric in the evaluation of object detection models, offering a more nuanced and precise measure of performance compared to traditional metrics such as quadratic loss. This concept is particularly valuable in the field of computer vision, where accurately detecting and localizing objects within images is paramount. To understand
- Published in Artificial Intelligence, EITC/AI/ADL Advanced Deep Learning, Advanced computer vision, Advanced models for computer vision, Examination review
Tagged under: Artificial Intelligence, Bounding Box, Evaluation Metrics, IoU, Loss Functions, Object Detection