Datasets:
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license: cc-by-nc-4.0
task_categories:
- image-classification
tags:
- biology
- chemistry
size_categories:
- 1K<n<10K
---
# MobileMold: A Smartphone-Based Microscopy Dataset for Food Mold Detection
A smartphone-microsope-based dataset with 4941 annotated images for food mold detection
<div align="left">
<a href="https://mobilemold.github.io/dataset/"><img src="https://img.shields.io/static/v1?label=Website&message=MobileMold&color=purple"></a>  
<a href="https://doi.org/10.1145/3793853.3799806"><img src="https://img.shields.io/static/v1?label=Paper&message=ACM%20MMSys&color=black"></a>  
<a href="https://arxiv.org/abs/2603.01944"><img src="https://img.shields.io/static/v1?label=Preprint&message=arXiv&color=red&logo=arxiv"></a>  
<a href="https://huggingface.co/datasets/nphamdinh/mobilemold"><img src="https://img.shields.io/static/v1?label=Dataset&message=HuggingFace&color=yellow"></a>  
<a href="https://www.kaggle.com/datasets/namphamdinh/mobilemold"><img src="https://img.shields.io/static/v1?label=Dataset&message=Kaggle&color=blue"></a>  
<a href="https://doi.org/10.5281/zenodo.18782230"><img src="https://img.shields.io/static/v1?label=Dataset&message=Zenodo&color=green"></a>  
</div>
## 🌟 About MobileMold
**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.
[ACM MMSys] Paper: https://doi.org/10.1145/3793853.3799806
Preprint: https://arxiv.org/abs/2603.01944
Project Page: https://mobilemold.github.io/dataset/
---
### 📊 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
---
### 📁 Dataset Structure
```
MobileMold/
├── metadata.csv # Complete dataset metadata (4,941 entries)
├── train_metadata.csv # Training split metadata
├── val_metadata.csv # Validation split metadata
├── test_metadata.csv # Test split metadata
├── original/ # Original microscope images (as captured)
│ ├── L10 - 48.jpeg
│ ├── L10 - 25.jpeg
│ ├── L10 - 161.jpeg
│ └── ... (4,941 files total)
└── cropped_resized/ # Preprocessed images (same filenames)
├── L10 - 48.jpeg # Cropped to mold region & resized
├── L10 - 25.jpeg
├── L10 - 161.jpeg
└── ... (4,941 files, 1:1 mapping to original/)
```
---
### 📊 Dataset Composition
### Image Versions
1. **`original/`** - Raw images as captured by smartphone microscopes
- Various resolutions (depending on smartphone and microscope)
- Full field-of-view including background
- Unprocessed image data
2. **`cropped_resized/`** - Processed images
- Cropped to focus on mold regions
- Resized to consistent dimensions
- Same filenames as original folder
### Metadata Format
Each CSV file contains the following columns:
| Column | Description | Values/Examples |
|--------|-------------|-----------------|
| `filename` | Image filename (same in both folders) | `L10 - 48.jpeg` |
| `mold` | Binary indicator of mold presence | `True` / `False` |
| `food` | Type of food in image | `carrot`, `bread`, `cheese`, `tomato`, etc. |
| `phone` | Smartphone model used | `iPhone SE 2nd Generation`, etc. |
| `microscope` | Clip-on microscope model | `Apexel 100x`, etc. |
**Example metadata entry:**
```csv
filename,mold,food,phone,microscope
L10 - 48.jpeg,True,carrot,iPhone SE 2nd Generation,Apexel 100x
```
## FreshLens Mobile App
The [freshlens-app](https://github.com/MobileMold/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 this useful for your research, please cite this as:
```
@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}
}
```
## 📄 License
This dataset is available under the terms of the **[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)** |