Auto-Labeling Data for Object Detection

TL;DR

This paper introduces a method to train object detection models using auto-generated pseudo labels from vision-language models, reducing labeling costs while maintaining competitive performance.

Contribution

It presents a novel approach to eliminate ground truth labels by leveraging pre-trained vision-language models for auto-labeling in object detection tasks.

Findings

Maintains competitive accuracy across multiple datasets

Reduces labeling time and costs significantly

Establishes best practices and benchmarks for auto-labeling

Abstract

Great labels make great models. However, traditional labeling approaches for tasks like object detection have substantial costs at scale. Furthermore, alternatives to fully-supervised object detection either lose functionality or require larger models with prohibitive computational costs for inference at scale. To that end, this paper addresses the problem of training standard object detection models without any ground truth labels. Instead, we configure previously-trained vision-language foundation models to generate application-specific pseudo "ground truth" labels. These auto-generated labels directly integrate with existing model training frameworks, and we subsequently train lightweight detection models that are computationally efficient. In this way, we avoid the costs of traditional labeling, leverage the knowledge of vision-language models, and keep the efficiency of lightweight models for practical application. We perform exhaustive experiments across multiple labeling configurations, downstream inference models, and datasets to establish best practices and set an extensive auto-labeling benchmark. From our results, we find that our approach is a viable alternative to standard labeling in that it maintains competitive performance on multiple datasets and substantially reduces labeling time and costs.