  We introduce Dynamic Mobile-Former(DMF), maximizes the capabilities of
dynamic convolution by harmonizing it with efficient operators.Our Dynamic
MobileFormer effectively utilizes the advantages of Dynamic MobileNet
(MobileNet equipped with dynamic convolution) using global information from
light-weight attention.A Transformer in Dynamic Mobile-Former only requires a
few randomly initialized tokens to calculate global features, making it
computationally efficient.And a bridge between Dynamic MobileNet and
Transformer allows for bidirectional integration of local and global
features.We also simplify the optimization process of vanilla dynamic
convolution by splitting the convolution kernel into an input-agnostic kernel
and an input-dependent kernel.This allows for optimization in a wider kernel
space, resulting in enhanced capacity.By integrating lightweight attention and
enhanced dynamic convolution, our Dynamic Mobile-Former achieves not only high
efficiency, but also strong performance.We benchmark the Dynamic Mobile-Former
on a series of vision tasks, and showcase that it achieves impressive
performance on image classification, COCO detection, and instanace
segmentation.For example, our DMF hits the top-1 accuracy of 79.4% on
ImageNet-1K, much higher than PVT-Tiny by 4.3% with only 1/4
FLOPs.Additionally,our proposed DMF-S model performed well on challenging
vision datasets such as COCO, achieving a 39.0% mAP,which is 1% higher than
that of the Mobile-Former 508M model, despite using 3 GFLOPs less
computations.Code and models are available at https://github.com/ysj9909/DMF
