Datasets:
File size: 3,608 Bytes
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license: mit
task_categories:
- image-classification
tags:
- computer-vision
- manufacturing
- synthetic-data
- industrial-inspection
pretty_name: Nut Defect Classification (Synthetic)
size_categories:
- n<1K
---
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<!-- SVG Hexagon Nut Icon -->
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</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).
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