Zero-Shot Quantization for Object Detectors using Off-the-Shelf Generative Models

With an increasing number of Object Detection (OD) modelsbeing deployed on edge devices, Zero-Shot Quantization for OD (ZSQ-OD) aims to quantize these models when access to the original trainingdata is prohibited. Existing research on Zero-Shot Quantization-AwareTraining (QAT) for OD synthesizes training sets through noise optimiza-tion. However, this approach struggles to maintain performance in low-bit regions. In this paper, we introduce GoodQ (Generative off-the-shelfmodels for object detector Quantization), a QAT pipeline that utilizesoff-the-shelf generative models to construct a training set. We first iden-tify three challenges that arise when introducing a generative model tothe ZSQ-OD task: 1) each image contains dense information with mul-tiple instances, 2) the class-wise distribution in the original dataset isimbalanced, and 3) the pseudo-labels assigned to the generated imagescan potentially act as noisy signals during QAT. GoodQ addresses thesechallenges by 1) introducing an Information-Dense Prompting strategyto generate multi-instance images, 2) applying Intrinsic Distribution-Aware Selection to match the pretrained class distribution, and 3) em-ploying Teacher-guided Adaptive Noise Reduction to mitigate noise aris-ing from the QAT process. Our framework achieves state-of-the-art per-formance in low-bit ZSQ (W4A4) and extends quantization to extremebit-widths (W3A3). Furthermore, we conduct an extensive analysis touncover the underlying factors contributing to the efficacy of GoodQ.The constructed dataset is available at GoodQ.