Not All mAPs are Equal and How to Test Model Robustness

In a detailed exploration of model robustness, Mateo Rojas-Carulla, CTO at Lakera AI, discusses the complexities of model selection for production deployment, emphasizing that similar test metrics, such as mean Average Precision (mAP), do not guarantee identical model performance in real-world applications. The blog post highlights the importance of robustness testing, using tools like Lakera’s MLTest, to evaluate how models handle deviations from their training data, which can significantly impact their generalization abilities in production. By examining models trained with different augmentation strategies on Roboflow, the analysis reveals that augmentations do not always yield improved model performance and can sometimes degrade it, underscoring the need for thorough evaluation of these strategies. The findings suggest that while augmentations can enhance robustness, their effects must be carefully validated to ensure that they contribute positively to a model's ability to cope with the variances encountered in production environments.