# MnasFPN: Learning Latency-aware Pyramid Architecture for Object Detection on Mobile Devices

arXiv: https://arxiv.org/abs/1912.01106 · HTML: https://mlsanity.com/paper/1912.01106/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 18 of 18)

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- [DREB-Net: Dual-stream Restoration Embedding Blur-feature Fusion Network for High-mobility UAV Object Detection](https://mlsanity.com/paper/2410.17822/) (2024)
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- [Network Space Search for Pareto-Efficient Spaces](https://mlsanity.com/paper/2104.11014/) (2021)
- [RelationRS: Relationship Representation Network for Object Detection in Aerial Images](https://mlsanity.com/paper/2110.06730/) (2021)
- [Virtuoso: Video-based Intelligence for real-time tuning on SOCs](https://mlsanity.com/paper/2112.13076/) (2021)
- [Neural Architecture Adaptation for Object Detection by Searching Channel Dimensions and Mapping Pre-trained Parameters](https://mlsanity.com/paper/2206.08509/) (2022)
- [Multi-Objective Evolutionary for Object Detection Mobile Architectures Search](https://mlsanity.com/paper/2211.02791/) (2022)

## Abstract (arXiv)

Despite the blooming success of architecture search for vision tasks in resource-constrained environments, the design of on-device object detection architectures have mostly been manual. The few automated search efforts are either centered around non-mobile-friendly search spaces or not guided by on-device latency. We propose MnasFPN, a mobile-friendly search space for the detection head, and combine it with latency-aware architecture search to produce efficient object detection models. The learned MnasFPN head, when paired with MobileNetV2 body, outperforms MobileNetV3+SSDLite by 1.8 mAP at similar latency on Pixel. It is also both 1.0 mAP more accurate and 10% faster than NAS-FPNLite. Ablation studies show that the majority of the performance gain comes from innovations in the search space. Further explorations reveal an interesting coupling between the search space design and the search algorithm, and that the complexity of MnasFPN search space may be at a local optimum.