The lightweight face detection models were developed to match the specifications of edge devices. This makes them suitable for use in real-life applications where the high GPU might not be available. This study compares the performance of two lightweight object detection models, YOLOv7-tiny and YOLOv8n, for face detection applications. Both models were trained on WIDERFACE dataset, and their performance was evaluated using mean average precision (mAP). Regarding model size, YOLOv8n is lighter than YOLOv7-tiny with only 3.01 million parameters, making it more efficient for deployment on resource-constrained devices. In terms of accuracy, the results showed that YOLOv7-tiny achieved a mAP50 of 69.8% and a mAP50-95 of 36.2%, while YOLOv8n achieved a mAP50 of 37% and an mAP50-95 of 67.6%


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    YOLOv7-Tiny and YOLOv8n Evaluation for Face Detection


    Additional title:

    Lect. Notes Electrical Eng.



    Conference:

    International Conference on Robotics, Vision, Signal Processing and Power Applications ; 2021 April 05, 2021 - April 06, 2021



    Publication date :

    2024-03-31


    Size :

    7 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





    Lightweight and Efficient Tiny-Object Detection Based on Improved YOLOv8n for UAV Aerial Images

    Min Yue / Liqiang Zhang / Juan Huang et al. | DOAJ | 2024

    Free access

    UAV-based Real-Time Face Detection using YOLOv7

    Samma, Hussein / Al-Azani, Sadam / El-Ferik, Sami | Elsevier | 2025

    Free access


    Improved Highway Vehicle Detection Algorithm for YOLOv8n

    Feng, Xiaoxiao / Ren, Anhu / Qi, Hua | IEEE | 2023