  This paper focuses on developing modern, efficient, lightweight models for
dense predictions while trading off parameters, FLOPs, and performance.
Inverted Residual Block (IRB) serves as the infrastructure for lightweight
CNNs, but no counterpart has been recognized by attention-based studies. This
work rethinks lightweight infrastructure from efficient IRB and effective
components of Transformer from a unified perspective, extending CNN-based IRB
to attention-based models and abstracting a one-residual Meta Mobile Block
(MMB) for lightweight model design. Following simple but effective design
criterion, we deduce a modern Inverted Residual Mobile Block (iRMB) and build a
ResNet-like Efficient MOdel (EMO) with only iRMB for down-stream tasks.
Extensive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks
demonstrate the superiority of our EMO over state-of-the-art methods, e.g.,
EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass equal-order
CNN-/Attention-based models, while trading-off the parameter, efficiency, and
accuracy well: running 2.8-4.0x faster than EdgeNeXt on iPhone14.
