Datasets:

industoai
/

HyperNut

@@ -57,3 +57,1111 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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LICENSE ADDED Viewed

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+Copyright (c) 2025 IndustoAI
+Contact: admin@industoai.com
+This project is licensed under the Creative Commons
+Attribution–NonCommercial 4.0 International License (CC BY-NC 4.0).
+You are free to:
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+- Adapt — remix, transform, and build upon the material.
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+liability with respect to your use of the material to the fullest extent permitted by law.
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+https://creativecommons.org/licenses/by-nc/4.0/legalcode

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 ---
-license: cc-by-nc-4.0
 ---

 ---
+language:
+- en
+license: cc-by-4.0
+task_categories:
+- image-classification
+- image-segmentation
+- anomaly-detection
+pretty_name: HyperNut
+size_categories:
+- n<1K
+tags:
+- hyperspectral-imaging
+- anomaly-detection
+- anomaly-segmentation
+- defect-detection
+- food-quality
+- nuts
+- vis-nir
+- computer-vision
 ---
+# HyperNut: Hyper Spectral Dataset of Nuts for Unsupervised Defect Detection and Segmentation
+This is the hyperspectral dataset for anomaly detection and segmentation purposes in8 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
+```bibtex
+@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).