Commit
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Parent(s):
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Add acne detection model and related files
Browse files- README.md +59 -3
- assets/README.md +0 -0
- config.json +13 -0
- requirements.txt +5 -0
README.md
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# Acne Detection Model
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## Overview
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This repository contains a YOLOv8-based model trained for detecting acne on African and dark skin tones. The model is designed to be inclusive, focusing on diverse datasets to improve dermatological applications' accuracy across various skin types.
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## Model Details
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- **Model Type:** YOLOv8
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- **Architecture:** YOLOv8
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- **Version:** 1.0
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## Usage
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### Installation
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```bash
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pip install -r requirements.txt
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```
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### Model Loading
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```python
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from ultralytics import YOLO
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# Load the model
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model = YOLO("acne.pt")
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# Perform inference
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result = model.detect_acne(image_path="path/to/test_image.jpg")
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print(result)
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```
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Replace `"acne.pt"` with the correct model weights file.
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## License
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This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
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## Acknowledgments
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- Data labeling by [Amina Shiga](https://www.linkedin.com/in/amina-shiga-07000522a)
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- Model Training by [Nathaniel Handan](https://www.linkedin.com/in/nathanielhandan/)
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## Contributing
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We welcome contributions from the community. If you find any issues or have suggestions, please open an [issue](https://github.com/Tinny-Robot/acne-detection/issues) or submit a pull request.
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## Contact
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For inquiries, please contact [Your Name](mailto:[email protected]).
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## References
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- Ultralytics YOLOv8: https://github.com/ultralytics/ultralytics
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- VGG Image Annotator (VIA): https://www.robots.ox.ac.uk/~vgg/software/via/
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assets/README.md
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File without changes
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config.json
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{
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"model_type": "yolov8",
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"model_name": "acne",
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"architecture": "yolov8",
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"version": "1.0",
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"description": "Acne Detection Model",
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"author": "Tinny-Robot",
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"license": "apache-2.0",
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"task": "Object Detection",
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"framework": "Ultralytics YOLOv8",
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"tags": ["acne", "skin", "object detection", "yolov8"]
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}
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requirements.txt
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torch==1.8.1
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torchvision==0.9.1
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numpy==1.19.5
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opencv-python==4.5.1.48
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ultralytics
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