Data-Free Low-Bit Quantization for Remote Sensing Object Detection

Abstract

Convolutional neural networks (CNNs) have been extensively used in remote sensing (RS) detection models. Although the memory size of detection models is massive, quantization can offer access to using these models in satellite embedded devices. However, for privacy reasons, institutions in charge of quantification operations may not obtain the original images. Based on this circumstance, current data-based quantization methods are no longer applicable, and data-free methods have poor performance for low-bit quantization. To address this problem, we propose a data-free quantization method for the CNN-based RS detection model. First, we use a generative adversarial network (GAN) to generate fake scene images. These images represent the global contextual information of each category. Second, we quantify the full-precision pretrained detection network. Finally, we train the quantized model to mimic the performance of the full-precision model by the proposed alternate training strategy with the generated fake scene images. We apply our method on CenterNet with a ResNet-18 backbone and evaluate the quantized model on the NWPU VHR-10 and DOTA datasets. The results show that our 5 bit quantized detection network obtains 94.1% mAP on NWPU VHR-10 and compresses the memory size to 0.158 times that of the full-precision network. Experiments verify that our data-free scene generation quantization algorithm maintains high performance with a large model compression ratio.