Task-Specific Zero-shot Quantization-Aware Training for Object Detection
Authors: name: Changhao Li, name: Xinrui Chen, name: Ji Wang, name: Kang Zhao, name: Jianfei Chen

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 .