Launch: Verified Model Metrics

Selecting the appropriate computer vision model for production necessitates precise measurement and comparison of model performance using standardized metrics like Mean Average Precision (mAP). These metrics ensure reliable evaluations by quantifying detection accuracy and object localization, enabling informed decisions for real-world applications. Roboflow addresses the need for unbiased evaluations by introducing Verified Model Metrics, calculated using open-source code and independent test sets, marked by a Verified badge in their app for transparency. This approach contrasts with self-reported metrics that may not accurately reflect production performance due to different validation settings. The COCO Evaluation framework, widely adopted since its introduction by Microsoft, provides a rigorous basis for evaluating detection models, yet research highlights that improvements on COCO don’t always translate to real-world performance, as seen with models like YOLOv11. By adopting standardized and verified metrics, such as those provided by Roboflow using the Supervision framework, users can confidently assess model performance and choose models that generalize well beyond benchmark-specific gains.