📄 Paper published at ACM MMSys 2026 — DOI: 10.1145/3793853.3799806 | arXiv:2603.01944

MobileMold: A Smartphone-Based Microscopy Dataset for Food Mold Detection

Dinh Nam Pham¹, Leonard Prokisch², Bennet Meyer³, Jonas Thumbs⁴

¹TU Berlin, ²University of Regensburg, ³ETH Zurich, ⁴University of Tübingen

MobileMold is a comprehensive dataset comprising 4,941 annotated images for food mold detection, captured using smartphones with various clip-on microscope attachments. The dataset addresses the growing need for accessible, low-cost food safety monitoring by leveraging smartphone-based microscopy. This enables research and development in computer vision applications for mold detection on various food surfaces. The mold-detection-baseline repository contains additional resources and scripts for data preprocessing and model training. The published paper contains detailed information about the dataset collection and application.

Dataset Overview

Total Images: 4,941
Annotations: Food Type and Mold Label
Food Types: 11 categories (carrot, orange, creamcheese, tomato, toast, raspberry, mixed bread, blackberry, blueberry, cheese, onion)
Microscope Types: 3 different clip-on smartphone microscopes (30x-100x magnification)
Smartphones: Images captured with 3 different smartphone models

Download

Kaggle
Hugging Face
Zenodo

FreshLens Mobile App

The freshlens-app repository contains a Flutter-based mobile app designed for consumer-facing demonstrations and can be used in conjunction with a hosted model. Using a smartphone microscope attachment, users can capture or import images of food. The app then displays the probability that the food is moldy.

Citation

If you use the MobileMold dataset in your research, please cite:

@inproceedings{Pham2026MobileMold,
  author    = {Pham, Dinh Nam and Prokisch, Leonard and Meyer, Bennet and Thumbs, Jonas},
  title     = {MobileMold: A Smartphone-Based Microscopy Dataset for Food Mold Detection},
  year      = {2026},
  isbn      = {9798400724817},
  publisher = {Association for Computing Machinery},
  address   = {New York, NY, USA},
  url       = {https://doi.org/10.1145/3793853.3799806},
  doi       = {10.1145/3793853.3799806},
  booktitle = {Proceedings of the ACM Multimedia Systems Conference 2026},
  pages     = {402--408},
  numpages  = {7},
  keywords  = {Dataset, Smartphone, Food, Mold, Microscope, Mobile, Fungal},
  series    = {MMSys '26}
}