Task-Specific Zero-shot Quantization-Aware Training for Object Detection
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 .
6880477600f4b5a05f2fbddf
Changhao Li
claimed_verified
2025-07-23T08:36:33.936Z
660026d9573abbcdb975aa13
https://cdn-avatars.huggingface.co/v1/production/uploads/660026d9573abbcdb975aa13/XktfFxTMC_d1xJ4pDJLum.jpeg
Changhao
user
lichangh20
6880477600f4b5a05f2fbde0
Xinrui Chen
6880477600f4b5a05f2fbde1
Ji Wang
6880477600f4b5a05f2fbde2
Kang Zhao
6880477600f4b5a05f2fbde3
Jianfei Chen
Task-Specific Zero-shot Quantization-Aware Training for Object Detection
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 .
A novel task-specific zero-shot quantization framework for object detection networks uses synthetic data to maintain performance without real training data.
quantization zero-shot quantization ZSQ object detection synthetic data pre-trained models bounding box category sampling knowledge distillation MS-COCO Pascal VOC