# Quantization and Training of Low Bit-Width Convolutional Neural Networks for Object Detection

Source: [Quantization and Training of Low Bit-Width Convolutional Neural Networks for Object Detection](<https://www.sota2.com/research/paper/68fa639c1e209131ce9ff9b7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:44:17.219Z

Authors: Penghang Yin, Shuai Zhang, Yingyong Qi, Jack Xin

Published: 2016-12-19T05:54:18.000Z

## Abstract

We present LBW-Net, an efficient optimization based method for quantization and training of the low bit-width convolutional neural networks (CNNs). Specifically, we quantize the weights to zero or powers of two by minimizing the Euclidean distance between full-precision weights and quantized weights during backpropagation. We characterize the combinatorial nature of the low bit-width quantization problem. For 2-bit (ternary) CNNs, the quantization of $N$ weights can be done by an exact formula in $O(N\\log N)$ complexity. When the bit-width is three and above, we further propose a semi-analytical thresholding scheme with a single free parameter for quantization that is computationally inexpensive. The free parameter is further determined by network retraining and object detection tests. LBW-Net has several desirable advantages over full-precision CNNs, including considerable memory savings, energy efficiency, and faster deployment. Our experiments on PASCAL VOC dataset show that compared with its 32-bit floating-point counterpart, the performance of the 6-bit LBW-Net is nearly lossless in the object detection tasks, and can even do better in some real world visual scenes, while empirically enjoying more than 4$\\times$ faster deployment.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1612.06052>)

[PDF](<https://arxiv.org/pdf/1612.06052.pdf>)