HyperNut: Hyper Spectral Dataset of Nuts for Unsupervised Defect Detection and Segmentation

This is the hyperspectral dataset for anomaly detection and segmentation purposes in nuts, specfically almonds and pistachios.

Dataset Summary

HyperNut is a hyperspectral dataset for unsupervised defect detection and segmentation in nuts. It contains visible and near-infrared (VIS-NIR) hyperspectral images of almonds and pistachios, captured in the 400–1000 nm wavelength range.

The dataset is designed for anomaly detection settings where only normal samples are available for training, while both normal and defective samples are provided for testing. In addition to image-level defect analysis, HyperNut also includes pixel-level defect masks for segmentation.

HyperNut is intended to support research on:

• hyperspectral anomaly detection
• defect segmentation
• food quality inspection
• VIS-NIR image analysis
• unsupervised and semi-supervised industrial inspection

Supported Tasks and Leaderboards

This dataset can be used for:

• Unsupervised / semi-supervised anomaly detection
• Defect localization
• Defect segmentation
• Band selection and spectral analysis
• Comparison between hyperspectral and RGB-based methods

Dataset Structure

HyperNut contains two sub-datasets:

• Almond
• Pistachio

For each category, the dataset is split into:

• Training set: only normal samples
• Test set: both normal and abnormal samples

Abnormal test samples include different defect types and are accompanied by segmentation masks indicating the defect regions.

Dataset Statistics

Category	Train Normal	Test Normal	Test Abnormal	Defect Groups	Abnormality Types
Almond	100	26	35	6	scratch, broken, rotten, insect, external material, mix
Pistachio	118	36	35	6	branch, shell, stone, insect, external material, mix

Data Instances

Each hyperspectral sample is captured with:

• Spatial resolution: 1024 x 1024
• Spectral range: 400–1000 nm
• Number of spectral bands: 600
• Spectral resolution: approximately 1 nm

The hyperspectral data are stored in:

• an HDR file containing metadata such as wavelength information
• a DAT file containing the hyperspectral cube in 12-bit resolution

For defective test samples, an additional segmentation mask is provided.

A sample may include:

• one or multiple nuts
• background noise
• varying environmental conditions
• non-aligned object layouts

Curation Rationale

HyperNut was created to address the lack of publicly available hyperspectral datasets for anomaly detection and segmentation in realistic inspection settings.

Most existing anomaly detection benchmarks rely on RGB images, which may fail to reveal subtle defects when the anomaly has:

• similar color or texture to the normal object
• material-related differences not visible in RGB
• small spectral changes outside the visible range

Hyperspectral imaging offers a richer representation by capturing hundreds of contiguous bands, making it suitable for detecting both surface-level and material-related abnormalities.

Data Collection Process

Images were collected under real imaging conditions using:

• a SENOP HSC-2 hyperspectral camera
• halogen lamps positioned at approximately 45 degrees
• VIS-NIR spectral coverage from 400 nm to 1000 nm

The setup was designed to capture realistic variation, including:

• environmental noise
• background reflections
• multiple objects per scene
• variability in object arrangement and lighting conditions

This makes the dataset more representative of real-world industrial inspection scenarios than highly controlled single-object acquisitions.

Personal and Sensitive Information

The dataset does not contain personal or sensitive information.

Preprocessing

To use the hyperspectral data effectively, the following preprocessing steps are recommended:

1. Normalization using white and dark reference values
2. Region of Interest (RoI) extraction to isolate the sample from background
3. Noise filtering using the Savitzky–Golay filter
4. Mean centering to reduce offset caused by lighting and environmental variation

In our experiments, Otsu thresholding on the 600 nm band provided effective RoI extraction.

Uses

Direct Use

HyperNut can be used directly for:

• training unsupervised or semi-supervised anomaly detection models
• defect localization and segmentation
• spectral band analysis
• comparing RGB and hyperspectral approaches

Out-of-Scope Use

This dataset is not intended for:

• supervised defect classification with large balanced class labels
• medical, biometric, or human-centered applications
• applications requiring short-wave infrared (SWIR) data beyond 1000 nm

Citation

@conference{visapp26,
author={Afshin Dini and Farnaz Delirie and Esa Rahtu},
title={HyperNut: Hyper Spectral Dataset of Nuts for Unsupervised Defect Detection and Segmentation},
booktitle={Proceedings of the 21st International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP},
year={2026},
pages={177-184},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014063900004084},
isbn={978-989-758-804-4},
issn={2184-4321},
}

License

This model is licensed under the Attribution–NonCommercial 4.0 International License (CC BY-NC 4.0).