Update README.md

README.md

@@ -1,52 +1,41 @@
----
-tags:
-  - image-classification
-  - computer-vision
-  - deepfake-detection
-  - fine-tuned
-license: mit
-datasets:
-  - 140k-real-and-fake-faces
-metrics:
-  - accuracy
----
+# 🎭 SDXL-Deepfake-Detector  
+### Detecting AI-Generated Faces with Precision and Purpose  
 
-# 🎭 SDXL-Deepfake-Detector
+> *"Not just another classifier — a tool for digital truth."*  
+> Developed by **[Sadra Milani Moghaddam](https://sadramilani.ir/)**
 
-**A high-performance deep learning model for binary classification of real versus synthetically generated (deepfake) human faces.**
-
-Developed by **[Sadra Milani Moghaddam](https://sadramilani.ir/)**, this model is designed to detect faces generated by state-of-the-art synthesis models—including those based on SDXL and similar architectures—while maintaining strong generalization across diverse image sources.
+---
 
-## 🚀 Model Overview
+## 🌍 Why This Matters  
+As generative AI (like SDXL, DALL·E, and Midjourney) becomes more accessible, the line between real and synthetic media blurs — especially for vulnerable communities. This project started as a technical experiment but evolved into a **privacy-aware, open-source defense** against visual misinformation, with a focus on **ethical AI deployment**.
 
-**SDXL-Deepfake-Detector** is a fine-tuned image classification model built using transfer learning. It leverages a pre-trained vision backbone and is optimized specifically for distinguishing authentic human faces from AI-generated forgeries.
+---
 
-### Key Features
+## 🚀 Model Overview  
 
-- **Task**: Binary image classification (Real = 0, Deepfake = 1)  
-- **Training Dataset**: [140k Real and Fake Faces (Kaggle)](https://www.kaggle.com/datasets/xhlulu/140k-real-and-fake-faces)  
-- **Test Accuracy**: **91%** on an independent hold-out test set  
-- **Hardware Used for Training**: NVIDIA RTX 3060 (12GB VRAM)  
-- **License**: [MIT](https://opensource.org/licenses/MIT)  
+**SDXL-Deepfake-Detector** is a fine-tuned vision transformer that classifies human faces as **Real (0)** or **AI-Generated (1)**. Trained on the [140k Real and Fake Faces](https://www.kaggle.com/datasets/xhlulu/140k-real-and-fake-faces) dataset, it achieves **91% accuracy** on held-out test data and generalizes well across diverse synthesis methods.
 
-This model is suitable for integration into media forensics pipelines, content moderation systems, or any application requiring reliable deepfake detection at the image level.
+### ✅ Key Highlights
+- **Architecture**: Fine-tuned Vision Transformer (ViT) via Hugging Face `transformers`
+- **Dataset**: 140k balanced real/fake face images
+- **License**: [MIT](https://opensource.org/licenses/MIT) — free for research and commercial use
+- **Hardware**: Trained on a single NVIDIA RTX 3060 (12GB VRAM) — proving high impact doesn’t require massive resources
 
 ---
 
-## 💻 Usage
-
-You can easily load and run inference with this model using the Hugging Face `transformers` library.
-
-### Installation
+## 💻 Quick Start
 
+### Dependencies
 ```bash
 pip install transformers torch pillow
 ```
+### Python Script
 ```python
 import argparse
 from transformers import AutoModelForImageClassification, AutoFeatureExtractor
 from PIL import Image
 import torch
+import os
 
 def main():
     parser = argparse.ArgumentParser(
@@ -55,41 +44,39 @@ def main():
     parser.add_argument("--image", type=str, required=True, help="Path to the input image file")
     args = parser.parse_args()
 
+    # Validate image path
+    if not os.path.isfile(args.image):
+        raise FileNotFoundError(f"Image file not found: {args.image}")
+
+    # Load model and feature extractor from Hugging Face Hub
     model_name = "SADRACODING/SDXL-Deepfake-Detector"
+    print(f"Loading model '{model_name}'...")
     model = AutoModelForImageClassification.from_pretrained(model_name)
     feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
 
+    # Set device (GPU if available)
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     model.to(device)
     model.eval()
+    print(f"Running on device: {device}")
 
+    # Load and preprocess image
     image = Image.open(args.image).convert("RGB")
     inputs = feature_extractor(images=image, return_tensors="pt").to(device)
 
+    # Inference
     with torch.no_grad():
         outputs = model(**inputs)
-
+    
     logits = outputs.logits
     predicted_class_idx = logits.argmax(-1).item()
     predicted_label = model.config.id2label[predicted_class_idx]
 
-    print(f"Predicted class index: {predicted_class_idx}")
-    print(f"Predicted label: {predicted_label}")
+    # Output
+    print(f"Prediction Result")
+    print(f"Class Index: {predicted_class_idx}")
+    print(f"Label      : {predicted_label}")
 
 if __name__ == "__main__":
     main()
-```
-
-```bash
-python predict.py --image path/to/face_image.jpg
-```
-## 📄 License
-
-This model is released under the [MIT License](https://opensource.org/licenses/MIT), allowing for both commercial and non-commercial use with proper attribution.
-
-## 🙌 Acknowledgements
-
-- **Dataset**: [140k Real and Fake Faces](https://www.kaggle.com/datasets/xhlulu/140k-real-and-fake-faces) by xhlulu  
-- **Framework**: [Hugging Face Transformers](https://huggingface.co/docs/transformers)
-- **Github**: [SDXL-Deepfake-Detector](https://github.com/SadraCoding/SDXL-Deepfake-Detector)
-- **Developer**: [Sadra Milani Moghaddam](https://sadramilani.ir/)
+```