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In this paper, we study the problem of `test-driving' a detector, i.e.allowing a human user to get a quick sense of how well the detector generalizesto their specific requirement. To this end, we present the first system thatestimates detector performance interactively without extensive ground truthingusing a human in the loop. We approach this as a problem of estimatingproportions and show that it is possible to make accurate inferences on theproportion of classes or groups within a large data collection by observingonly $5-10\%$ of samples from the data. In estimating the false detections (forprecision), the samples are chosen carefully such that the overallcharacteristics of the data collection are preserved. Next, inspired by its usein estimating disease propagation we apply pooled testing approaches toestimate missed detections (for recall) from the dataset. The estimates thusobtained are close to the ones obtained using ground truth, thus reducing theneed for extensive labeling which is expensive and time consuming.

Interactively Test Driving an Object Detector: Estimating Performance on Unlabeled Data