  Network quantization allows inference to be conducted using low-precision
arithmetic for improved inference efficiency of deep neural networks on edge
devices. However, designing aggressively low-bit (e.g., 2-bit) quantization
schemes on complex tasks, such as object detection, still remains challenging
in terms of severe performance degradation and unverifiable efficiency on
common hardware. In this paper, we propose an Accurate Quantized object
Detection solution, termed AQD, to fully get rid of floating-point computation.
To this end, we target using fixed-point operations in all kinds of layers,
including the convolutional layers, normalization layers, and skip connections,
allowing the inference to be executed using integer-only arithmetic. To
demonstrate the improved latency-vs-accuracy trade-off, we apply the proposed
methods on RetinaNet and FCOS. In particular, experimental results on MS-COCO
dataset show that our AQD achieves comparable or even better performance
compared with the full-precision counterpart under extremely low-bit schemes,
which is of great practical value. Source code and models are available at:
https://github.com/ziplab/QTool
