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.

本文介绍了一种基于 MKLDNN 加速策略的轻量级 CPU 网络 PP-LCNet，这种网络在多项任务上得到了改进。通过技术上的提升，PP-LCNet 在分类任务中的准确性大大优于之前的网络结构，并在计算机视觉的下游任务中表现出色。该研究使用的所有实验都是基于 PaddlePaddle 实现的，并提供相应的代码和预训练模型。