How to Improve the Accuracy of Your Computer Vision Model: A Guide

Computer vision projects require iterative development, where each model version builds upon the last until satisfactory performance is achieved for deployment. Key strategies for improving model accuracy include clearly defining the problem, starting with a simple model that identifies one or two features, and expanding complexity as confidence grows. Collecting relevant, well-labeled data that represents the deployment environment is crucial, alongside using preprocessing steps and selectively applying augmentations. Evaluating model performance through metrics like precision, recall, and mean average precision (mAP) is essential, while tools such as confusion matrices and vector analysis help diagnose performance issues. Manual testing in production-like environments and active learning to gather real-world data ensure models remain accurate over time. Techniques like shadow deployment and using Label Assist for annotation can facilitate ongoing model improvement and adaptation to changing environmental conditions.