---
license: agpl-3.0
library_name: ultralytics
pipeline_tag: object-detection
tags:
- yolov8
- computer-vision
- object-detection
- technical-catalogs
- industrial-automation
- hotspot-detection
---

# Machine Catalog Hotspot Detector

A custom-trained YOLOv8s object detection model developed to detect hotspot regions in industrial machinery technical catalogs.

## Overview

Industrial machinery catalogs often contain complex exploded-view diagrams, numbered components, and regions of interest used for spare parts identification.

This project explores the use of deep learning to automatically detect hotspot regions within technical catalog pages, supporting automated catalog digitization and downstream document processing workflows.

## Model Information

| Property | Description |
|---|---|
| Architecture | YOLOv8s |
| Framework | Ultralytics / PyTorch |
| Task | Object Detection |
| Detection Class | Hotspot |
| Model Format | PyTorch (.pt) |
| Base Model | yolov8s.pt |

## Intended Applications

- Automated hotspot detection in technical catalog pages
- Industrial spare parts catalog digitization
- Technical document analysis
- Computer vision research and experimentation
- Preprocessing for industrial parts identification systems

## Quick Start

Install the required dependency:

```bash
pip install ultralytics
```

Load the model directly from Hugging Face:

```python
from ultralytics import YOLO
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="suleymankolenoglu/MachineCatalogHotspotDetector",
    filename="best.pt"
)

model = YOLO(model_path)

results = model.predict(
    source="catalog_page.jpg",
    conf=0.25,
    save=True
)
```

## Training

The model was fine-tuned from the pretrained YOLOv8s architecture for a custom single-class hotspot detection task.

## Evaluation

The model was tested during development on technical catalog images.

Formal evaluation metrics have not yet been published. Quantitative benchmarks may be added in future updates.

## Limitations

- Performance may vary across different catalog layouts and image resolutions.
- Small or visually ambiguous hotspot regions may be difficult to detect.
- Generalization to previously unseen catalog styles has not been systematically evaluated.
- The model is not intended for safety-critical applications.

## Future Improvements

- Publish quantitative evaluation results
- Add example predictions
- Develop an interactive Hugging Face Spaces demonstration
- Evaluate generalization across different technical catalog formats

## License

AGPL-3.0. Use and redistribution are subject to applicable Ultralytics licensing terms.

## Acknowledgments

Built using the Ultralytics YOLOv8 framework.