---
license: cc-by-4.0
task_categories:
- object-detection
tags:
- computer-vision
- object-detection
- synthetic-data
- defect-detection
- industrial-inspection
- yolo
- mvtec
pretty_name: Synthetic MVTec AD - Defect Detection

configs:
- config_name: default
  data_files:
  - split: train
    path: metadata_train.jsonl

dataset_info:
  features:
  - name: file_name
    dtype: image
  - name: label_file
    dtype: string
  - name: object_name
    dtype: string
  - name: split
    dtype: string
  - name: width
    dtype: int64
  - name: height
    dtype: int64
  - name: objects
    dtype: string
---

# Synthetic MVTec AD – Defect Detection Dataset by [AnywayLabs.ai](https://anywaylabs.ai?utm_source=huggingface&utm_medium=community&utm_campaign=organic)

## Need a custom synthetic dataset for your own defect detection use case?

This dataset is an open-source sample of our synthetic data generation work at AnywayLabs.

If you're working on:
- industrial defect detection
- visual inspection
- supervised anomaly detection
- hard-to-collect defect classes
- synthetic data for computer vision training

[You can request a custom synthetic dataset here](https://anywaylabs.ai/configure?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=mvtec_ad_defect_detection), or email: contact@anywaylabs.ai

## Dataset Summary

This dataset contains fully synthetic defect images for industrial inspection, inspired by the MVTec AD dataset generated by [AnywayLabs.ai](anywaylabs.ai).

Unlike the original MVTec AD dataset, which is designed for anomaly detection (train on good, test on defects), this dataset reformulates the problem as a supervised object detection task with labeled defect instances.

The dataset focuses on realistic defect simulation across industrial objects and is designed to generalize to real-world defect detection scenarios.

### Key Features

- Fully synthetic defect dataset  
- Supervised object detection formulation  
- YOLO-format annotations  
- Validated on real MVTec AD defect images  
- Object-specific datasets (e.g., transistor, bottle, cable)  

---

## Synthetic vs Real Comparison
<table>
  <tr>
    <th>Synthetic</th>
    <th>Real</th>
  </tr>
  <tr>
    <td><img src="cable/images/train/cable_bent_wire_1.png" width="300"/></td>
    <td><img src="cable/images/val/bent_wire_001.png" width="300"/></td>
  </tr>
  <tr>
    <td><img src="metal-nut/images/train/metal_nut_bent_1.png" width="300"/></td>
    <td><img src="metal-nut/images/val/bent_001.png" width="300"/></td>
  </tr>
  <tr>
    <td><img src="screw/images/train/screw_manipulated_front_0.png" width="300"/></td>
    <td><img src="screw/images/val/manipulated_front_000.png" width="300"/></td>
  </tr>
  <tr>
    <td><img src="transistor/images/train/bent_7_png.rf.def15e4ffd2448372c8f9c62923bc22f.jpg" width="300"/></td>
    <td><img src="transistor/images/val/bent_lead_000.png" width="300"/></td>
  </tr>
</table>


## Dataset Structure

### Format

- Task: Object Detection (Defect Detection)  
- Annotation Format: YOLO (normalized bounding boxes)  
- Image Resolution: 640 x 640  

### Directory Structure

    object_name/
      images/
        train/
        val/
      labels/
        train/
        val/
      data.yaml

### Example (Transistor Dataset)

- Number of Classes: 4  

Classes:
- bent_lead  
- cut_lead  
- damaged_case  
- misplaced  

### Splits

- Train: Synthetic defect images  
- Validation: Real defect images (from MVTec AD, in full setup)  

---

## Data Generation

The dataset is generated using a AnywayLabs.ai's proprietary synthetic dataset generation framework.

### Capabilities

- Defect-level control:
  - Type (e.g., cut, bend, damage)
  - Size and severity
  - Placement on object  

- Scene-level control:
  - Lighting conditions  
  - Surface texture variation  
  - Material consistency  

- Structural consistency:
  - Object geometry preserved  
  - Realistic defect integration (not simple overlays)  

### Design Philosophy

Instead of replicating real data exactly, the dataset is designed to:

- Expand the defect distribution beyond limited real samples  
- Introduce controlled variation across defect appearance  
- Improve robustness to unseen real-world defects  

---

## Annotation Process

- Bounding boxes are generated for each defect instance  
- Labels follow YOLO format:

    class_id x_center y_center width height

- Bounding box tightness is manually corrected  
- Annotation consistency is maintained across all samples  

---

## Limitations

- Real validation datasets are small and not fully representative  
- Performance varies across object categories and defect complexity  
- Bounding boxes may not capture fine-grained defect shapes (compared to segmentation)  

---

## Usage

### Recommended Use Cases

- Defect detection model training  
- Synthetic pretraining before fine-tuning on real data  
- Industrial inspection system development  

### Compatible Frameworks

- Ultralytics YOLO (recommended)  
- Detectron2 (after conversion)  
- Any YOLO-compatible pipeline  

---

## Comparison with MVTec AD

| Feature | MVTec AD | This Dataset |
|--------|----------|-------------|
| Training Data | Mostly "good" images | Defect images |
| Task Type | Anomaly Detection | Object Detection |
| Labels | Segmentation masks (test only) | Bounding boxes |
| Defect Count | ~25 per class | Scalable |
| Training Paradigm | Unsupervised | Fully supervised |

---

## Need a custom synthetic dataset for your own defect detection use case?

This dataset is an open-source sample of our synthetic data generation work at AnywayLabs.

If you're working on:
- industrial defect detection
- visual inspection
- supervised anomaly detection
- hard-to-collect defect classes
- synthetic data for computer vision training

[You can request a custom synthetic dataset here](https://anywaylabs.ai/configure?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=mvtec_ad_defect_detection), or email: contact@anywaylabs.ai

## Citation

If you use this dataset, please cite:

Synthetic MVTec AD Dataset – [AnywayLabs.ai](https://anywaylabs.ai?utm_source=huggingface&utm_medium=community&utm_campaign=organic)