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Kinzaaa/nut_defect_detection

Nut Defect Classification Synthetic Industrial Quality Inspection Dataset Nut Defect Classification (Synthetic Dataset) This dataset is a synthetic collection of industrial nut images designed for image classification tasks, specifically focusing on defect detection in manufacturing pipelines. It serves as a benchmark and training resource for computer vision algorithms used in quality assurance.… See the full description on the dataset page: https://huggingface.co/datasets/Kinzaaa/nut_defect_detection.

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<div style="background: linear-gradient(135deg, #1e293b 0%, #0f172a 100%); padding: 30px; border-radius: 16px; border: 1px solid #334155; text-align: center; font-family: system-ui, -apple-system, sans-serif; color: #f8fafc; margin-bottom: 30px; box-shadow: 0 10px 15px -3px rgba(0, 0, 0, 0.3);"> <div style="display: inline-flex; align-items: center; justify-content: center; width: 80px; height: 80px; background: linear-gradient(135deg, #f59e0b 0%, #d97706 100%); border-radius: 50%; margin-bottom: 20px; box-shadow: 0 0 20px rgba(245, 158, 11, 0.4);"> <!-- SVG Hexagon Nut Icon --> <svg width="44" height="44" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"> <path d="M12 2L4 7V17L12 22L20 17V7L12 2Z" stroke="#ffffff" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/> <circle cx="12" cy="12" r="4" stroke="#ffffff" stroke-width="2"/> <path d="M12 8V6" stroke="#ffffff" stroke-width="2"/> <path d="M12 18V16" stroke="#ffffff" stroke-width="2"/> <path d="M8.5 10L6.5 9" stroke="#ffffff" stroke-width="2"/> <path d="M17.5 14L15.5 13" stroke="#ffffff" stroke-width="2"/> <path d="M8.5 14L6.5 15" stroke="#ffffff" stroke-width="2"/> <path d="M17.5 10L15.5 11" stroke="#ffffff" stroke-width="2"/> </svg> </div> <h1 style="margin: 0; font-size: 28px; font-weight: 800; letter-spacing: -0.025em; background: linear-gradient(to right, #fbbf24, #f59e0b); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">Nut Defect Classification</h1> <p style="margin: 8px 0 0 0; color: #94a3b8; font-size: 14px; text-transform: uppercase; letter-spacing: 0.1em;">Synthetic Industrial Quality Inspection Dataset</p> </div>

Nut Defect Classification (Synthetic Dataset)

This dataset is a synthetic collection of industrial nut images designed for image classification tasks, specifically focusing on defect detection in manufacturing pipelines. It serves as a benchmark and training resource for computer vision algorithms used in quality assurance.

Dataset Structure

The dataset contains a total of 261 images categorized into two classes:

  • —`defect`: Images representing industrial nuts with defects (e.g., structural, surface flaws).
  • —`non_defect`: Images representing normal, non-defective nuts.

The dataset includes a metadata.csv mapping image paths to their respective labels, as well as a dataset-metadata.json describing the dataset details.

Directory Layout

├── README.md
├── dataset-metadata.json
├── metadata.csv
├── synthetic_defect/
│   ├── synth_defect_1.png
│   └── ...
└── synthetic_non_defect/
    ├── synth_non_defect_1.png
    └── ...

Data Fields

The metadata.csv contains the following fields:

  • —file_name: Path to the image file relative to the root directory (e.g. synthetic_defect/synth_defect_1.png).
  • —label: Class label (defect or non_defect).

Use Cases

  • —Quality Control & Automation: Training models to detect defect products on assembly lines.
  • —Anomaly Detection: Evaluating unsupervised or semi-supervised anomaly detection methods.
  • —Synthetic Data Research: Analyzing the transferability of synthetic datasets to real-world scenarios.

Licensing

Licensed under the MIT License.