Task-Specific Zero-shot Quantization-Aware Training for Object Detection

• URL: http://arxiv.org/abs/2507.16782v1
• Date: Tue, 22 Jul 2025 17:28:29 GMT
• Title: Task-Specific Zero-shot Quantization-Aware Training for Object Detection
• Authors: Changhao Li, Xinrui Chen, Ji Wang, Kang Zhao, Jianfei Chen,
• Abstract summary: Quantization is a key technique to reduce network size and computational complexity.<n>ZSQ addresses this by using synthetic data generated from pre-trained models.<n>We propose a novel task-specific ZSQ framework for object detection networks.
• Score: 6.715774951295021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantization is a key technique to reduce network size and computational complexity by representing the network parameters with a lower precision. Traditional quantization methods rely on access to original training data, which is often restricted due to privacy concerns or security challenges. Zero-shot Quantization (ZSQ) addresses this by using synthetic data generated from pre-trained models, eliminating the need for real training data. Recently, ZSQ has been extended to object detection. However, existing methods use unlabeled task-agnostic synthetic images that lack the specific information required for object detection, leading to suboptimal performance. In this paper, we propose a novel task-specific ZSQ framework for object detection networks, which consists of two main stages. First, we introduce a bounding box and category sampling strategy to synthesize a task-specific calibration set from the pre-trained network, reconstructing object locations, sizes, and category distributions without any prior knowledge. Second, we integrate task-specific training into the knowledge distillation process to restore the performance of quantized detection networks. Extensive experiments conducted on the MS-COCO and Pascal VOC datasets demonstrate the efficiency and state-of-the-art performance of our method. Our code is publicly available at: https://github.com/DFQ-Dojo/dfq-toolkit .

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