Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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
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.

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The categories of this paper are: gr-qc
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The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
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The categories of this paper are: hep-ex
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The categories of this paper are: nlin
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The categories of this paper are: q-alg
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The categories of this paper are: cond-mat
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The categories of this paper are: adap-org
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The categories of this paper are: q-bio
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-ex
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The categories of this paper are: math
20
The categories of this paper are: acc-phys
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The categories of this paper are: quant-ph