mlsanityData to 7 Oct 2026

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

arXiv 1704.04861 ↗First posted 17 Apr 2017cs.CVcited by 3,827

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Cited by 36 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.

Key facts. Cited by 3,827 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Apr 2017.

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Citing papers per year: 2018: 1; 2019: 1; 2020: 1; 2021: 859; 2022: 740; 2023: 718; 2024: 636; 2025: 564; 2026: 307.

3,827 papers.

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  1. 2022A ConvNet for the 2020scited by 3,545
  2. 2021MLP-Mixer: An all-MLP Architecture for Visioncited by 1,441
  3. 2021Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNetcited by 1,096
  4. 2021CLIP-Adapter: Better Vision-Language Models with Feature Adapterscited by 1,021
  5. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  6. 2024VMamba: Visual State Space Modelcited by 955
  7. 2021CvT: Introducing Convolutions to Vision Transformerscited by 946
  8. 2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 798
  9. 2021PVT v2: Improved Baselines with Pyramid Vision Transformercited by 749
  10. 2021BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-Viewcited by 711
  11. 2023ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoderscited by 703
  12. 2021MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformercited by 636
38 more
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  2. 2021CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windowscited by 526
  3. 2021RepVGG: Making VGG-style ConvNets Great Againcited by 520
  4. 2024YOLOv10: Real-Time End-to-End Object Detectioncited by 508
  5. 2022InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutionscited by 479
  6. 2023Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectivescited by 477
  7. 2022MaxViT: Multi-Axis Vision Transformercited by 476
  8. 2021A White Paper on Neural Network Quantizationcited by 453
  9. 2022PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategiescited by 432
  10. 2021Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modelingcited by 429
  11. 2021Dynamic Neural Networks: A Surveycited by 405
  12. 2021ResNet strikes back: An improved training procedure in timmcited by 365
  13. 2022Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classificationcited by 355
  14. 2021Towards Real-World Blind Face Restoration with Generative Facial Priorcited by 347
  15. 2023Faster Segment Anything: Towards Lightweight SAM for Mobile Applicationscited by 345
  16. 2022Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuningcited by 292
  17. 2021Distilling Knowledge via Knowledge Reviewcited by 285
  18. 2022Matryoshka Representation Learningcited by 268
  19. 2021Pruning and Quantization for Deep Neural Network Acceleration: A Surveycited by 259
  20. 2022Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphscited by 258
  21. 2021Revisiting Skeleton-based Action Recognitioncited by 256
  22. 2021Knowledge distillation: A good teacher is patient and consistentcited by 255
  23. 2021I-BERT: Integer-only BERT Quantizationcited by 250
  24. 2021Understanding Robustness of Transformers for Image Classificationcited by 250
  25. 2021End-to-end Interpretable Neural Motion Plannercited by 240
  26. 2022EfficientFormer: Vision Transformers at MobileNet Speedcited by 240
  27. 2021Coordinate Attention for Efficient Mobile Network Designcited by 239
  28. 2024LocalMamba: Visual State Space Model with Windowed Selective Scancited by 235
  29. 2021Mobile-Former: Bridging MobileNet and Transformercited by 234
  30. 2021LocalViT: Analyzing Locality in Vision Transformerscited by 228
  31. 2021AdaViT: Adaptive Vision Transformers for Efficient Image Recognitioncited by 228
  32. 2024Rotary Position Embedding for Vision Transformercited by 226
  33. 2021Co-Scale Conv-Attentional Image Transformerscited by 224
  34. 2021WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognitioncited by 221
  35. 2021ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Biascited by 216
  36. 2022Visual Attention Networkcited by 210
  37. 2021HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editingcited by 208
  38. 2022UniFormer: Unifying Convolution and Self-attention for Visual Recognitioncited by 206

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