用手机显微镜拍食物霉菌，数据集+模型全开源。
MobileMold: A Smartphone-Based Microscopy Dataset for Food Mold Detection
- 用4款手机3种镜头拍11类食物霉菌图像
- 模型准确率达99.5%，接近理论上限
- 适合做食品安全检测、移动端成像研究
智能手机夹式显微镜可将日常设备变为低成本便携成像系统，甚至能揭示真菌微观结构，实现肉眼无法察觉的霉变检测。本文提出MobileMold，一个面向食物霉菌检测与分类的开放手机显微镜数据集，包含4,941张手持显微图像，覆盖11种食物类型、4款智能手机、3种显微镜及多样真实环境条件。除数据集发布外，我们建立了霉菌检测与食物分类的基准模型，包括同时预测两类属性的多任务设置。在多个预训练深度学习架构与增强策略下，模型达到近天花板性能（准确率0.9954，F1值0.9954，马氏相关系数0.9907），验证了该数据集在食品腐败检测中的有效性。为提升透明度，我们采用基于显著性图的可视化解释，突出模型判断所依赖的霉菌区域。MobileMold旨在推动可及性食品安全感知、移动成像及附加设备赋能智能手机的研究。
原文摘要 · Abstract (English)
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.
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