Spaces:
Runtime error
Runtime error
File size: 2,521 Bytes
67af4ca 9d267dd 67af4ca 18e87d1 67af4ca 9d267dd 67af4ca 03b3e60 5f8a9c5 03b3e60 a52c644 03b3e60 5f8a9c5 03b3e60 ff9dc4e 5f8a9c5 e4bc4cb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 |
---
title: AI Profile Picture Classification
emoji: π
colorFrom: red
colorTo: green
sdk: gradio
sdk_version: 4.22.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: Currently enhancing a pre-trained Transformer model to disti
---
# AI vs. Real Profile Picture Classification
## Overview
This project hosts a fine-tuned version of the [Nahrawy/AIorNot](https://huggingface.co/Nahrawy/AIorNot) model on Hugging Face's Model Hub, specifically adapted for distinguishing between AI-generated and real **profile pictures**. Utilizing transfer learning, the model has been fine-tuned on a diverse dataset comprising both real and synthetically generated face images to achieve high accuracy in classification tasks.
## Model Description
The Nahrawy/AIorNot model, originally designed for image classification tasks, has been fine-tuned to specifically address the challenge of distinguishing AI-generated images from real ones. This adaptation makes it particularly suitable for applications in social media moderation, digital forensics, and any domain where such differentiation is critical.
## Dataset
The model was fine-tuned on a combined dataset consisting of [20000 real](https://huggingface.co/datasets/student/celebA) and [8892 AI-generated profile pictures](https://www.kaggle.com/datasets/pablobedolla/this-person-does-not-exist-data). The dataset was split into training and validation sets, ensuring a comprehensive learning and evaluation process.
## Performance
The model's performance metrics, such as accuracy and F1 score, are reported as follows:
Accuracy: ...%
F1 Score: ...%
## Limitations and Bias
While the model demonstrates robust performance across diverse datasets, it's important to acknowledge potential limitations and biases inherent in the training data. Users are encouraged to evaluate the model in their specific contexts and consider these factors when interpreting the results.
## Contributing
We welcome contributions to improve the model and its documentation. Whether you have suggestions for new features, bug reports, or requests, please feel free to open an issue or submit a pull request.
## License
Apache 2.0
## Acknowledgements
Special thanks to Houston Community College (HCC) for the opportunity to learn Computer Vision, Professor McManus for the knowledge shared and brilliant idea of this Mid-Term project, the creators of the Nahrawy/AIorNot model, and the Hugging Face team for providing the infrastructure that makes this project possible. |