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For machine learning models trained with limited labeled training data,validation stands to become the main bottleneck to reducing overall annotationcosts. We propose a statistical validation algorithm that accurately estimatesthe F-score of binary classifiers for rare categories, where finding relevantexamples to evaluate on is particularly challenging. Our key insight is thatsimultaneous calibration and importance sampling enables accurate estimateseven in the low-sample regime (< 300 samples). Critically, we also derive anaccurate single-trial estimator of the variance of our method and demonstratethat this estimator is empirically accurate at low sample counts, enabling apractitioner to know how well they can trust a given low-sample estimate. Whenvalidating state-of-the-art semi-supervised models on ImageNet andiNaturalist2017, our method achieves the same estimates of model performancewith up to 10x fewer labels than competing approaches. In particular, we canestimate model F1 scores with a variance of 0.005 using as few as 100 labels.

Low-Shot Validation: Active Importance Sampling for Estimating Classifier Performance on Rare Categories