MobileMold: A Smartphone-Based Microscopy Dataset for Food Mold Detection
Paper
• 2603.01944 • Published
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 249, in _split_generators
raise ValueError(
ValueError: `file_name` or `*_file_name` must be present as dictionary key (with type string) in metadata files
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
MobileMold: A Smartphone-Based Microscopy Dataset for Food Mold Detection
A smartphone-microsope-based dataset with 4941 annotated images for food mold detection
🌟 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.
📊 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
original/- Raw images as captured by smartphone microscopes- Various resolutions (depending on smartphone and microscope)
- Full field-of-view including background
- Unprocessed image data
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:
filename,mold,food,phone,microscope
L10 - 48.jpeg,True,carrot,iPhone SE 2nd Generation,Apexel 100x
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 this useful for your research, please cite this as:
@article{Pham2026MobileMold,
author = {Dinh Nam Pham and
Leonard Prokisch and
Bennet Meyer and
Jonas Thumbs},
title = {MobileMold: A Smartphone-Based Microscopy Dataset for Food Mold Detection},
journal = {arXiv eprint arXiv:2603.01944},
year = {2026},
}
📄 License
This dataset is available under the terms of the CC BY-NC 4.0
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