PP-LCNet: A Lightweight CPU Convolutional Neural Network
This work provides a more efficient network for CPU-based applications in computer vision, though it appears incremental as it builds on existing acceleration strategies.
The authors tackled the problem of improving accuracy for lightweight convolutional neural networks on CPU without increasing latency, achieving state-of-the-art performance in classification and downstream tasks like object detection and semantic segmentation.
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.