  Achieving a universally high accuracy in object detection is quite
challenging, and the mainstream focus in the industry currently lies on
detecting specific classes of objects. However, deploying one or multiple
object detection networks requires a certain amount of GPU memory for training
and storage capacity for inference. This presents challenges in terms of how to
effectively coordinate multiple object detection tasks under
resource-constrained conditions. This paper introduces a lightweight
fine-tuning strategy called Calibration side tuning, which integrates aspects
of adapter tuning and side tuning to adapt the successful techniques employed
in transformers for use with ResNet. The Calibration side tuning architecture
that incorporates maximal transition calibration, utilizing a small number of
additional parameters to enhance network performance while maintaining a smooth
training process. Furthermore, this paper has conducted an analysis on multiple
fine-tuning strategies and have implemented their application within ResNet,
thereby expanding the research on fine-tuning strategies for object detection
networks. Besides, this paper carried out extensive experiments using five
benchmark datasets. The experimental results demonstrated that this method
outperforms other compared state-of-the-art techniques, and a better balance
between the complexity and performance of the finetune schemes is achieved.
