Testing a computer vision system involves evaluating its accuracy, robustness, and usability. Start by validating the model on a separate test dataset, using metrics like precision, recall, and F1 score for classification tasks or mean average precision (mAP) for object detection.
Simulate real-world conditions, such as varying lighting, occlusions, or motion, to test the system’s robustness. Analyze failure cases to identify areas needing improvement.
Conduct user acceptance testing (UAT) to ensure the system meets end-user requirements. Continuous monitoring and retraining based on real-world data help maintain long-term performance.