Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
 title: AI Image Detector
-emoji: 📉
+emoji: 🔍
 colorFrom: green
 colorTo: red
 sdk: gradio
@@ -10,4 +10,50 @@ pinned: false
 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# AI Image Detector
+
+Detect whether an image is **AI-generated** or a **real photograph** using deep learning.
+
+## Model Architecture
+
+- **Base Model:** MobileNetV2 (pretrained on ImageNet)
+- **Transfer Learning:** Custom classification head with:
+  - Global Average Pooling
+  - Batch Normalization
+  - Dense layer (256 units) with L2 regularization
+  - Dropout (0.7)
+  - Sigmoid output for binary classification
+
+## Training Data
+
+The model was trained on a combined dataset of ~128,000 images:
+- **CIFAKE Dataset:** 100,000 images (50k real, 50k AI-generated)
+- **Tiny GenImage Dataset:** 28,000 additional images from various AI generators including:
+  - Midjourney
+  - Stable Diffusion
+  - BigGAN
+  - ADM
+  - GLIDE
+  - VQDM
+  - Wukong
+
+## Usage
+
+Upload any image to the interface, and the model will predict whether it's:
+- **Real Image:** A genuine photograph
+- **AI-Generated:** Created by an AI model
+
+## Files
+
+- `app.py` - Main Gradio application
+- `requirements.txt` - Python dependencies
+- `transfer_model.keras` - Trained model weights
+
+## License
+
+MIT License
+
+## Acknowledgments
+
+- CIFAKE Dataset: https://www.kaggle.com/datasets/birdy654/cifake-real-and-ai-generated-synthetic-images
+- Tiny GenImage Dataset: https://www.kaggle.com/datasets/yangsangtai/tiny-genimage