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Updated: Jan 15, 2026

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Computer Vision Models for Detecting Large Cigars on Social Media.

Grace Kong1, Shuvam Keshari2, Rachel R Ouellette1

  • 1Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.

Nicotine & Tobacco Research : Official Journal of the Society for Research on Nicotine and Tobacco
|October 13, 2025
PubMed
Summary

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A new computer vision model accurately identifies large cigars on social media, offering a scalable tool for tobacco monitoring and research. This technology aids in understanding marketing strategies and informing public health policies.

Area of Science:

  • Digital Health
  • Computer Vision
  • Public Health

Background:

  • Large cigars are heavily promoted on social media, necessitating automated monitoring methods.
  • Existing methods for tracking tobacco products online are limited in scalability.
  • There is a critical need to monitor large cigar content across digital platforms.

Purpose of the Study:

  • To train and validate a computer vision model for automated large cigar detection.
  • To assess the model's accuracy in distinguishing large cigars from other objects.
  • To establish a scalable solution for monitoring understudied tobacco products online.

Main Methods:

  • Utilized the You Only Look Once Version 7 (YOLOv7) computer vision model.
  • Trained the model on 876 large cigar images and 1526 non-cigar images from Reddit.

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  • Evaluated performance using recall, precision, and F1 scores, with data split for training and testing.

    • Main Results:

    • The computer vision model achieved high accuracy with a recall of 0.98, precision of 0.99, and F1 score of 0.98.
    • The model demonstrated 97% accuracy in distinguishing large cigars from similarly shaped objects.
    • Effectively minimized false positives, correctly identifying large cigars in diverse contexts.

    Conclusions:

    • The developed computer vision model accurately detects large cigars on social media, serving as a scalable automated monitoring tool.
    • This technology can enhance tobacco product identification, support research on usage patterns and marketing, and inform regulatory policies.
    • The model represents a novel approach to tobacco monitoring, crucial for public health initiatives and preventing initiation.