mAP, Precision and Recall scores are provided for all models trained with Roboflow Train, and through the notebooks in the Roboflow Model Zoo.

This old machine learning adage conveys a salient machine learning point: unless input data is of high quality, model accuracy — even with the best computer vision architectures — will suffer. Understanding what preprocessing and augmentation are at their core enables data scientists to get the most out of their input data.

Through the dataset assembly process, our laser focus might miss a whole host of edge cases that we would naturally consider out-of-scope for our model. While out-of-scope instances are intuitive to us, our model has no way of knowing anything beyond the scope of what it has been shown.