| --- |
| 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). |
| |
