mlsanityData to 7 Oct 2026

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

arXiv 2603.01944 ↗First posted 2 Mar 2026cs.CVcited by 0

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Abstract (arXiv)

Smartphone clip-on microscopes turn everyday devices into low-cost, portable imaging systems that can even reveal fungal structures at the microscopic level, enabling mold inspection beyond unaided visual checks. In this paper, we introduce MobileMold, an open smartphone-based microscopy dataset for food mold detection and food classification. MobileMold contains 4,941 handheld microscopy images spanning 11 food types, 4 smartphones, 3 microscopes, and diverse real-world conditions. Beyond the dataset release, we establish baselines for (i) mold detection and (ii) food-type classification, including a multi-task setting that predicts both attributes. Across multiple pretrained deep learning architectures and augmentation strategies, we obtain near-ceiling performance (accuracy = 0.9954, F1 = 0.9954, MCC = 0.9907), validating the utility of our dataset for detecting food spoilage. To increase transparency, we complement our evaluation with saliency-based visual explanations highlighting mold regions associated with the model's predictions. MobileMold aims to contribute to research on accessible food-safety sensing, mobile imaging, and exploring the potential of smartphones enhanced with attachments.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 33 references that are in the map. First posted on arXiv 2 Mar 2026.

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References · 14

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2 more
  1. 2022MaxViT: Multi-Axis Vision Transformercited by 476
  2. 2021TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentationcited by 225

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