Open AccessA Dynamic Balance Quantization Method for YOLOv3
Author(s) -
Hua Yang,
Lixin Yu,
Xiao Chun Meng,
Zhiyong Qin,
Boxu Chen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1848/1/012157
Subject(s) - quantization (signal processing) , computer science , inference , segmentation , retraining , artificial intelligence , deep learning , artificial neural network , floating point , algorithm , computer vision , international trade , business
This paper describes a quantization method for pre-trained deep CNN models, and has achieved very good results in YOLOv3. The weights are quantized to int8 and the biases are quantized to int16, by contrasting the float inference result, the mAP loss is less than 0.5 %. While running neural nets on hardware, 8-bit fixed-point quantization is the key to efficient inference. However, it is a very difficult task for running a very deep network on 8-bit hardware, often resulting in a significant decrease in accuracy or spending a lot of time on retraining the network. Our method uses dynamic fixed-point quantization and adds a small amount of bit-shifts to balance the accuracy of each layer in a YOLO network. At the same time, it further shows that this method can also be extended to other computer vision architectures and tasks, such as semantic segmentation and image classification.