Pith. sign in

REVIEW 1 cited by

PP-LCNet: A Lightweight CPU Convolutional Neural Network

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2109.15099 v1 pith:BSPL7ZM3 submitted 2021-09-17 cs.CV

Cheng Cui , Tingquan Gao , Shengyu Wei , Yuning Du , Ruoyu Guo , Shuilong Dong , Bin Lu , Ying Zhou
show 5 more authors
Xueying Lv Qiwen Liu Xiaoguang Hu Dianhai Yu Yanjun Ma
This is my paper · ORCID
classification cs.CV
keywords networklightweightmodelspp-lcnetaccuracytasksaccelerationalmost
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MoENAS: Mixture-of-Expert based Neural Architecture Search for jointly Accurate, Fair, and Robust Edge Deep Neural Networks

    cs.LG 2025-02 reject novelty 6.0 of 10

    MoENAS, a mixture-of-experts neural architecture search, produces MobileViTv2 variants with reported accuracy, fairness, robustness, and generalization gains over state-of-the-art edge DNNs on person classification.

Pith tools