EMOv2: Pushing 5M Vision Model Frontier
Authors: name: Jiangning Zhang; name: Teng Hu; name: Haoyang He; name: Zhucun Xue; name: Yabiao Wang; name: Chengjie Wang; name: Yong Liu; name: Xiangtai Li; name: Dacheng Tao
Published: 2024-12-09 17:12:22

Abstract: 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.