Methods › Computer Vision › Light-weight neural networks › MobileDet

MobileDet

2 papers tagged archive 2025-07-28

Introduced by Yunyang Xiong et al. in MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

MobileDet is an object detection model developed for mobile accelerators. MobileDets uses regular convolutions extensively on EdgeTPUs and DSPs, especially in the early stage of the network where depthwise convolutions tend to be less efficient. This helps boost the latency-accuracy trade-off for object detection on accelerators, provided that they are placed strategically in the network via neural architecture search. By incorporating regular convolutions in the search space and directly optimizing the network architectures for object detection, an efficient family of object detection models is obtained.

PaperSource

Papers archive 2025-07-28

2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

8 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Neural Architecture Search2
Object2
Object Detection2
object-detection2
CPU1
GPU1
Image Classification1
image-classification1

Usage over time archive 2025-07-28

Papers per year tagged with MobileDet: 2020 to 2022, peak 1 1 0 2020: 1 paper 2020 2021: 0 papers 2021 2022: 1 paper 2022
Papers per year the archive tags with this method, by the paper's archive date (2 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Light-weight neural networksObject Detection Models

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