---
license: mit
task_categories:
- image-classification
tags:
- computer-vision
- manufacturing
- synthetic-data
- industrial-inspection
pretty_name: Nut Defect Classification (Synthetic)
size_categories:
- n<1K
---

<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](https://opensource.org/licenses/MIT).