Papers › MobileNetV4 -- Universal Models for the Mobile Ecosystem
MobileNetV4 -- Universal Models for the Mobile Ecosystem
Danfeng Qin, Chas Leichner, Manolis Delakis, Marco Fornoni, Shixin Luo, Fan Yang, Weijun Wang, Colby Banbury, Chengxi Ye, Berkin Akin, Vaibhav Aggarwal, Tenghui Zhu, Daniele Moro, Andrew Howard
We present the latest generation of MobileNets, known as MobileNetV4 (MNv4), featuring universally efficient architecture designs for mobile devices. At its core, we introduce the Universal Inverted Bottleneck (UIB) search block, a unified and flexible structure that merges Inverted Bottleneck (IB), ConvNext, Feed Forward Network (FFN), and a novel Extra Depthwise (ExtraDW) variant. Alongside UIB, we present Mobile MQA, an attention block tailored for mobile accelerators, delivering a significant 39% speedup. An optimized neural architecture search (NAS) recipe is also introduced which improves MNv4 search effectiveness. The integration of UIB, Mobile MQA and the refined NAS recipe results in a new suite of MNv4 models that are mostly Pareto optimal across mobile CPUs, DSPs, GPUs, as well as specialized accelerators like Apple Neural Engine and Google Pixel EdgeTPU - a characteristic not found in any other models tested. Finally, to further boost accuracy, we introduce a novel distillation technique. Enhanced by this technique, our MNv4-Hybrid-Large model delivers 87% ImageNet-1K accuracy, with a Pixel 8 EdgeTPU runtime of just 3.8ms.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | MNv4-Hybrid-L | Top 1 Accuracy | 83.4% | #426 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MNv4-Conv-L | Top 1 Accuracy | 82.9% | #485 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MNv4-Hybrid-M | Top 1 Accuracy | 80.7% | #684 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MNv4-Conv-M | Top 1 Accuracy | 79.9% | #729 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MNv4-Conv-S | Top 1 Accuracy | 73.8% | #984 of 1060 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: MobileNet-V4
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