title: EMOv2: Pushing 5M Vision Model Frontier
summary: This work focuses on developing parameter-efficient and lightweight models
  for dense predictions while trading off parameters, FLOPs, and performance. Our
  goal is to set up the new frontier of the 5M magnitude lightweight model on
  various downstream tasks. Inverted Residual Block (IRB) serves as the
  infrastructure for lightweight CNNs, but no counterparts have been recognized
  by attention-based design. Our work rethinks the lightweight infrastructure of
  efficient IRB and practical components in Transformer from a unified
  perspective, extending CNN-based IRB to attention-based models and abstracting
  a one-residual Meta Mobile Block (MMBlock) for lightweight model design.
  Following neat but effective design criterion, we deduce a modern Improved
  Inverted Residual Mobile Block (i2RMB) and improve a hierarchical Efficient
  MOdel (EMOv2) with no elaborate complex structures. Considering the
  imperceptible latency for mobile users when downloading models under 4G/5G
  bandwidth and ensuring model performance, we investigate the performance upper
  limit of lightweight models with a magnitude of 5M. Extensive experiments on
  various vision recognition, dense prediction, and image generation tasks
  demonstrate the superiority of our EMOv2 over state-of-the-art methods, e.g.,
  EMOv2-1M/2M/5M achieve 72.3, 75.8, and 79.4 Top-1 that surpass equal-order
  CNN-/Attention-based models significantly. At the same time, EMOv2-5M equipped
  RetinaNet achieves 41.5 mAP for object detection tasks that surpasses the
  previous EMO-5M by +2.6. When employing the more robust training recipe, our
  EMOv2-5M eventually achieves 82.9 Top-1 accuracy, which elevates the
  performance of 5M magnitude models to a new level. Code is available at
  https://github.com/zhangzjn/EMOv2.
paper_authors._id: 67592785adabd5ddff254756
paper_authors.name: Jiangning Zhang
paper_authors._id: 67592785adabd5ddff254757
paper_authors.name: Teng Hu
paper_authors._id: 67592785adabd5ddff254758
paper_authors.name: Haoyang He
paper_authors.status: admin_assigned
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paper_authors.fullname: haoyang He
paper_authors.type: user
paper_authors.user: hhy724
paper_authors._id: 67592785adabd5ddff254759
paper_authors.name: Zhucun Xue
paper_authors._id: 67592785adabd5ddff25475a
paper_authors.name: Yabiao Wang
paper_authors._id: 67592785adabd5ddff25475b
paper_authors.name: Chengjie Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-18T13:47:37.742Z
paper_authors._id: 65729087379284b904a6d81d
paper_authors.avatarUrl: /avatars/0e7968b1d9221d523a54e3ac787e449d.svg
paper_authors.fullname: Chengjie Wang
paper_authors.type: user
paper_authors.user: chengjie-wang
paper_authors._id: 67592785adabd5ddff25475c
paper_authors.name: Yong Liu
paper_authors._id: 67592785adabd5ddff25475d
paper_authors.name: Xiangtai Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-11T09:19:46.630Z
paper_authors._id: 63958b4414513eaf9029ebf1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/U1g5H071pWRswGAG9UTpo.png
paper_authors.fullname: Xiangtai Li
paper_authors.type: user
paper_authors.user: LXT
paper_authors._id: 67592785adabd5ddff25475e
paper_authors.name: Dacheng Tao
paper_title: EMOv2: Pushing 5M Vision Model Frontier
paper_summary: This work focuses on developing parameter-efficient and lightweight models
  for dense predictions while trading off parameters, FLOPs, and performance. Our
  goal is to set up the new frontier of the 5M magnitude lightweight model on
  various downstream tasks. Inverted Residual Block (IRB) serves as the
  infrastructure for lightweight CNNs, but no counterparts have been recognized
  by attention-based design. Our work rethinks the lightweight infrastructure of
  efficient IRB and practical components in Transformer from a unified
  perspective, extending CNN-based IRB to attention-based models and abstracting
  a one-residual Meta Mobile Block (MMBlock) for lightweight model design.
  Following neat but effective design criterion, we deduce a modern Improved
  Inverted Residual Mobile Block (i2RMB) and improve a hierarchical Efficient
  MOdel (EMOv2) with no elaborate complex structures. Considering the
  imperceptible latency for mobile users when downloading models under 4G/5G
  bandwidth and ensuring model performance, we investigate the performance upper
  limit of lightweight models with a magnitude of 5M. Extensive experiments on
  various vision recognition, dense prediction, and image generation tasks
  demonstrate the superiority of our EMOv2 over state-of-the-art methods, e.g.,
  EMOv2-1M/2M/5M achieve 72.3, 75.8, and 79.4 Top-1 that surpass equal-order
  CNN-/Attention-based models significantly. At the same time, EMOv2-5M equipped
  RetinaNet achieves 41.5 mAP for object detection tasks that surpasses the
  previous EMO-5M by +2.6. When employing the more robust training recipe, our
  EMOv2-5M eventually achieves 82.9 Top-1 accuracy, which elevates the
  performance of 5M magnitude models to a new level. Code is available at
  https://github.com/zhangzjn/EMOv2.
paper_ai_keywords: Inverted Residual Block (IRB) Meta Mobile Block (MMBlock) Improved Inverted Residual Mobile Block (i2RMB) Hierarchical Efficient Model (EMOv2) Top-1 accuracy mAP (mean Average Precision) RetinaNet