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Data is the engine of modern computer vision, which necessitates collectinglarge-scale datasets. This is expensive, and guaranteeing the quality of thelabels is a major challenge. In this paper, we investigate efficient annotationstrategies for collecting multi-class classification labels for a largecollection of images. While methods that exploit learnt models for labelingexist, a surprisingly prevalent approach is to query humans for a fixed numberof labels per datum and aggregate them, which is expensive. Building on priorwork on online joint probabilistic modeling of human annotations andmachine-generated beliefs, we propose modifications and best practices aimed atminimizing human labeling effort. Specifically, we make use of advances inself-supervised learning, view annotation as a semi-supervised learningproblem, identify and mitigate pitfalls and ablate several key design choicesto propose effective guidelines for labeling. Our analysis is done in a morerealistic simulation that involves querying human labelers, which uncoversissues with evaluation using existing worker simulation methods. Simulatedexperiments on a 125k image subset of the ImageNet100 show that it can beannotated to 80% top-1 accuracy with 0.35 annotations per image on average, a2.7x and 6.7x improvement over prior work and manual annotation, respectively.Project page: https://fidler-lab.github.io/efficient-annotation-cookbook

Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets