Update README.md

README.md

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-# 🎭 SDXL-Deepfake-Detector
+---
+tags:
+- image-classification
+- computer-vision
+- deepfake-detection
+- fine-tuned
+license: mit
+datasets:
+- 140k-real-and-fake-faces
+metrics:
+- accuracy
+---
 
-**A high-performance deep learning model fine-tuned for the binary classification of real vs. fake faces, specifically targeting synthesized images, including those potentially generated by powerful models like SDXL.**
+# 🎭 SDXL-Deepfake-Detector
 
-This project was developed by **[Sadra Milani Moghadam](https://sadramilani.ir)**.
+**A high-performance deep learning model fine-tuned for the binary classification of real vs. fake faces, designed for images potentially generated by advanced synthesis models.**
 
----
+This project was developed by **[Sadra Milani Moghadam](https://sadramilani.ir/)**.
 
 ## 🚀 Model Description
 
-The **SDXL-Deepfake-Detector** is a specialized image classification model designed to distinguish between authentic human faces and synthetically generated (deepfake) faces. It leverages the power of transfer learning to provide robust, high-accuracy detection.
+The **SDXL-Deepfake-Detector** is a specialized image classification model engineered to distinguish between authentic human faces and synthetically generated (deepfake) faces. The model was trained using **transfer learning** principles to provide robust and reliable detection.
 
 ### Key Features & Performance
 
-| Feature | Value / Metric | Notes |
+| Metric | Value | Notes |
 | :--- | :--- | :--- |
-| **Task** | Binary Image Classification | Real Face vs. Deepfake Face |
-| **Dataset Used** | [140K Real and Fake Faces (Kaggle)](https://www.kaggle.com/datasets/xhlulu/140k-real-and-fake-faces) | Comprehensive dataset of 140,000 high-quality images. |
-| **Test Accuracy** | **0.91** (91%) | Demonstrates strong performance in distinguishing image authenticity. |
-| **Hardware Used** | NVIDIA RTX 3060 (12GB VRAM) | Optimized training time due to suitable GPU hardware. |
+| **Task** | Binary Image Classification | Real Face (0) vs. Deepfake Face (1) |
+| **Dataset** | [140K Real and Fake Faces (Kaggle)](https://www.kaggle.com/datasets/xhlulu/140k-real-and-fake-faces) | A large dataset containing 140,000 high-quality images. |
+| **Test Accuracy** | **0.91** (91%) | Achieved on the independent test set. |
+| **Hardware** | NVIDIA RTX 3060 (12GB VRAM) | Training performed on suitable hardware for efficiency. |
 
 ---
 
 ## 💻 Usage with Hugging Face Transformers
 
-You can easily load and run this model for inference using the Hugging Face `transformers` library in Python.
+To load and use the model in your Python environment, use the standard `transformers` and `PIL` libraries.
+
+### Installation
+
+```bash
+pip install transformers torch pillow
 
 ---
 license: mit