Comparing Computer Vision Models On Custom Data

The blog post by Leo Ueno provides a detailed guide on evaluating the performance of two person detection models using benchmark datasets on Roboflow Universe. It emphasizes the importance of understanding how pre-trained computer vision models perform in specific use cases, which is not always evident from general performance metrics like mean average precision (mAP). The guide outlines steps to find or create an evaluation dataset, using the COCO dataset for demonstration, and employs the supervision package to benchmark models' performance. By importing the roboflow and supervision packages, users can download datasets, run models against them, and calculate their mAP to assess effectiveness. Through this process, the guide illustrates how one model, despite high mAP on its model page, performed poorly with a mAP of 12.77% when tested, underscoring the need to tailor evaluations to specific real-world scenarios. This methodology enables users to compare multiple models efficiently to select the most suitable one for their applications.