mlsanityData to 14 Oct 2026

EfficientRep:An Efficient Repvgg-style ConvNets with Hardware-aware Neural Network Design

arXiv 2302.00386 ↗First posted 1 Feb 2023cs.CVcited by 5

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Abstract (arXiv)

We present a hardware-efficient architecture of convolutional neural network, which has a repvgg-like architecture. Flops or parameters are traditional metrics to evaluate the efficiency of networks which are not sensitive to hardware including computing ability and memory bandwidth. Thus, how to design a neural network to efficiently use the computing ability and memory bandwidth of hardware is a critical problem. This paper proposes a method how to design hardware-aware neural network. Based on this method, we designed EfficientRep series convolutional networks, which are high-computation hardware(e.g. GPU) friendly and applied in YOLOv6 object detection framework. YOLOv6 has published YOLOv6N/YOLOv6S/YOLOv6M/YOLOv6L models in v1 and v2 versions.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 18 of its 20 references that are in the map. First posted on arXiv 1 Feb 2023.

Builds on

18 of 20 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  4. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  5. 2014Going Deeper with Convolutionscited by 4,241
  6. 2018YOLOv3: An Incremental Improvementcited by 2,659
  7. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
  8. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  9. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,101
  10. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
  11. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
  12. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 909
6 more
  1. 2019Once-for-All: Train One Network and Specialize it for Efficient Deploymentcited by 787
  2. 2021RepVGG: Making VGG-style ConvNets Great Againcited by 522
  3. 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
  4. 2022YOLOv6: A Single-Stage Object Detection Framework for Industrial Applicationscited by 275
  5. 2022PP-YOLOE: An evolved version of YOLOcited by 75
  6. 2022TRT-ViT: TensorRT-oriented Vision Transformercited by 10

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Cited by year

Citing papers per year: 2023: 2; 2024: 3.

5 papers.

  1. 2023Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithmcited by 12
  2. 2023MFMAN-YOLO: A Method for Detecting Pole-like Obstacles in Complex Environmentcited by 0
  3. 2024Octave-YOLO: Cross frequency detection network with octave convolutioncited by 0
  4. 2024Real-Time Indoor Object Detection based on hybrid CNN-Transformer Approachcited by 0
  5. 2024Fourier Domain Adaptation for Traffic Light Detection in Adverse Weathercited by 0