无需优化的检测数据蒸馏框架，显著提升小规模数据集精度
OD3: Optimization-free Dataset Distillation for Object Detection
- 通过迭代放置与预训练观察器筛选，无须优化生成检测用合成数据
- 在COCO上压缩至1%时，mAP50比之前最佳方法高出14%以上
- 适合追求高效检测训练的开发者和研究者使用
大规模神经网络在大规模数据集上的训练需要大量计算资源，尤其对于目标检测等密集预测任务。尽管数据蒸馏（DD）已被提出以从大集合中合成紧凑数据集来缓解这一问题，但现有工作大多集中于图像分类，目标检测场景仍缺乏探索。本文提出OD3，一种专为对象检测设计的无优化数据蒸馏框架。该方法包含两个阶段：首先，基于合适位置迭代地将物体实例放置到合成图像中；其次，利用预训练观察模型筛选并剔除低置信度物体。我们在MS COCO和PASCAL VOC两个主流检测数据集上进行实验，压缩率范围为0.25%至5%。相较于现有唯一针对检测的数据蒸馏方法及传统核心集选择方法，OD3表现更优，创下新纪录，在压缩率为1.0%时，COCO mAP50超越前人最佳方法超过14%。代码已公开于：https://github.com/VILA-Lab/OD3。
原文摘要 · Abstract (English)
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 suitable 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 COCO mAP50 at a compression ratio of 1.0%. Code is available at: https://github.com/VILA-Lab/OD3.
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