PP-LCNet: A Lightweight CPU Convolutional Neural Network

We propose a lightweight CPU network based on the MKLDNN acceleration strategy, named PP-LCNet, which improves the performance of lightweight models on multiple tasks. This paper lists technologies which can improve network accuracy while the latency is almost constant. With these improvements, the...

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Bibliographic Details
Published in:arXiv.org 2021-09
Main Authors: Cui, Cheng, Gao, Tingquan, Shengyu Wei, Du, Yuning, Guo, Ruoyu, Dong, Shuilong, Lu, Bin, Zhou, Ying, Lv, Xueying, Liu, Qiwen, Hu, Xiaoguang, Yu, Dianhai, Ma, Yanjun
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Computer vision
Lightweight
Object recognition
Semantic segmentation
Online Access:Get full text
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Summary:We propose a lightweight CPU network based on the MKLDNN acceleration strategy, named PP-LCNet, which improves the performance of lightweight models on multiple tasks. This paper lists technologies which can improve network accuracy while the latency is almost constant. With these improvements, the accuracy of PP-LCNet can greatly surpass the previous network structure with the same inference time for classification. As shown in Figure 1, it outperforms the most state-of-the-art models. And for downstream tasks of computer vision, it also performs very well, such as object detection, semantic segmentation, etc. All our experiments are implemented based on PaddlePaddle. Code and pretrained models are available at PaddleClas.
ISSN:2331-8422