mlsanityData to 7 Oct 2026

PP-LCNet: A Lightweight CPU Convolutional Neural Network

arXiv 2109.15099 ↗First posted 17 Sep 2021cs.CVcited by 27

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 26 of its 31 references that are in the map. First posted on arXiv 17 Sep 2021.

Builds on

26 of 31 references.

References · 26

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  5. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  6. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  7. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  8. 2017Squeeze-and-Excitation Networkscited by 4,763
  9. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  10. 2014Going Deeper with Convolutionscited by 4,231
  11. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  12. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
14 more
  1. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
  2. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  3. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,194
  4. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,132
  5. 2014Two-Stream Convolutional Networks for Action Recognition in Videoscited by 1,067
  6. 2019Once-for-All: Train One Network and Specialize it for Efficient Deploymentcited by 778
  7. 2018FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Searchcited by 480
  8. 2019GhostNet: More Features from Cheap Operationscited by 410
  9. 2016Richer Convolutional Features for Edge Detectioncited by 141
  10. 2019MixConv: Mixed Depthwise Convolutional Kernelscited by 108
  11. 2014Salient Object Detection: A Surveycited by 98
  12. 2019Blockwisely Supervised Neural Architecture Search with Knowledge Distillationcited by 81
  13. 2021Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve Backbonescited by 15
  14. 2018Learning from Large-scale Noisy Web Data with Ubiquitous Reweighting for Image Classificationcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 6; 2023: 3; 2024: 8; 2025: 3; 2026: 5.

27 papers.

Sort:

By area:

  1. 2025PaddleOCR 3.0 Technical Reportcited by 136
  2. 2023RepViT: Revisiting Mobile CNN From ViT Perspectivecited by 123
  3. 2022PP-OCRv3: More Attempts for the Improvement of Ultra Lightweight OCR Systemcited by 105
  4. 2022Castling-ViT: Compressing Self-Attention via Switching Towards Linear-Angular Attention at Vision Transformer Inferencecited by 28
  5. 2021PP-PicoDet: A Better Real-Time Object Detector on Mobile Devicescited by 25
  6. 2024The 3D-PC: a benchmark for visual perspective taking in humans and machinescited by 16
  7. 2026PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Taskscited by 12
  8. 2022Dite-HRNet: Dynamic Lightweight High-Resolution Network for Human Pose Estimationcited by 10
  9. 2023Lightweight Full-Convolutional Siamese Trackercited by 8
  10. 2024Scaling Laws for Black box Adversarial Attackscited by 8
  11. 2026PP-OCRv5: A Specialized 5M-Parameter Model Rivaling Billion-Parameter Vision-Language Models on OCR Taskscited by 8
  12. 2022Vision-Based Activity Recognition in Children with Autism-Related Behaviorscited by 7
15 more
  1. 2024TC-OCR: TableCraft OCR for Efficient Detection & Recognition of Table Structure & Contentcited by 5
  2. 2025AnchorFormer: Differentiable Anchor Attention for Efficient Vision Transformercited by 3
  3. 2021PP-ShiTu: A Practical Lightweight Image Recognition Systemcited by 2
  4. 2023High-order Spatial Interactions Enhanced Lightweight Model for Optical Remote Sensing Image-based Small Ship Detectioncited by 2
  5. 2024Thermal Image-based Fault Diagnosis in Induction Machines via Self-Organized Operational Neural Networkscited by 2
  6. 2025MoENAS: Mixture-of-Expert based Neural Architecture Search for jointly Accurate, Fair, and Robust Edge Deep Neural Networkscited by 2
  7. 2024depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researcherscited by 1
  8. 2024PdfTable: A Unified Toolkit for Deep Learning-Based Table Extractioncited by 1
  9. 2022Light-YOLOv5: A Lightweight Algorithm for Improved YOLOv5 in Complex Fire Scenarioscited by 0
  10. 2022Tech Report: One-stage Lightweight Object Detectorscited by 0
  11. 2024FilterViT and DropoutViTcited by 0
  12. 2024Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deploymentcited by 0
  13. 2026A Multistage Extraction Pipeline for Long Scanned Financial Documents: An Empirical Study in Industrial KYC Workflowscited by 0
  14. 2026ShellfishNet: A Domain-Specific Benchmark for Visual Recognition of Marine Molluscscited by 0
  15. 2026How to Choose Your Teacher for Fine Grained Image Recognitioncited by 0