OD³: Optimization-free Dataset Distillation for Object Detection

Salwa K. Al Khatib, Ahmed Elhagry, Shitong Shao, Zhiqiang Shen

TL;DR: We introduce OD³, a novel optimization-free data distillation framework specifically designed for object detection.

Abstract: Training large neural networks on large-scale datasets requires substantial computational resources, particularly for dense prediction tasks such as object detection. Although dataset distillation (DD) has been proposed to alleviate these demands by synthesizing compact datasets from larger ones, most existing work focuses solely on image classification, leaving the more complex detection setting largely unexplored. In this paper, we introduce **OD³**, a novel optimization-free data distillation framework specifically designed for object detection. Our approach involves two stages: first, a candidate selection process in which object instances are iteratively placed in synthesized images based on their optimal locations, and second, a candidate screening process using a pre-trained observer model to remove low-confidence objects. We perform our data synthesis framework on MS COCO and PASCAL VOC, two popular detection datasets, with compression ratios ranging from 0.25% to 5%. Compared to the prior solely existing dataset distillation method on detection and conventional core set selection methods, **OD³** delivers superior accuracy, establishes new state-of-the-art results, surpassing prior best method by more than 14% on MS COCO at a compression ratio of 1.0%. The code is in the supplementary material.

Primary Area: Applications->Computer Vision

Keywords: dataset distillation, object detection, data-centric AI, efficient machine learning

Submission Number: 2292