Extending Dataset Pruning to Object Detection: A Variance-based Approach

TL;DR

This paper extends dataset pruning techniques from image classification to object detection by introducing a variance-based scoring method, VPS, which effectively identifies informative samples and improves detection performance on PASCAL VOC and MS COCO.

Contribution

It presents the first principled approach to dataset pruning for object detection, addressing key challenges and proposing the Variance-based Prediction Score (VPS) for sample selection.

Findings

VPS outperforms prior pruning methods in mAP on benchmark datasets.

Informative sample selection is more critical than dataset size or class balance.

Pruning enhances detection performance while reducing dataset complexity.

Abstract

Dataset pruning -- selecting a small yet informative subset of training data -- has emerged as a promising strategy for efficient machine learning, offering significant reductions in computational cost and storage compared to alternatives like dataset distillation. While pruning methods have shown strong performance in image classification, their extension to more complex computer vision tasks, particularly object detection, remains relatively underexplored. In this paper, we present the first principled extension of classification pruning techniques to the object detection domain, to the best of our knowledge. We identify and address three key challenges that hinder this transition: the Object-Level Attribution Problem, the Scoring Strategy Problem, and the Image-Level Aggregation Problem. To overcome these, we propose tailored solutions, including a novel scoring method called Variance-based Prediction Score (VPS). VPS leverages both Intersection over Union (IoU) and confidence scores to effectively identify informative training samples specific to detection tasks. Extensive experiments on PASCAL VOC and MS COCO demonstrate that our approach consistently outperforms prior dataset pruning methods in terms of mean Average Precision (mAP). We also show that annotation count and class distribution shift can influence detection performance, but selecting informative examples is a more critical factor than dataset size or balance. Our work bridges dataset pruning and object detection, paving the way for dataset pruning in complex vision tasks.