Update README.md

README.md

@@ -1,3 +1,63 @@
----
-license: mit
----
+---
+license: mit
+tags:
+- deepfake-detection
+- computer-vision
+- efficientnet
+---
+
+# XADE Deepfake Detector
+
+EfficientNet-B4 model trained for deepfake detection as part of the XADE (eXplainable Automated Deepfake Evaluation) thesis project.
+
+## Model Details
+
+- **Architecture:** EfficientNet-B4
+- **Task:** Binary classification (real vs. fake faces)
+- **Training Dataset:** 140k Real and Fake Faces
+- **Test Accuracy:** 98.86%
+- **AUC-ROC:** 99.94%
+
+## Performance
+
+| Metric | Value |
+|--------|-------|
+| Accuracy | 98.86% |
+| Precision | 98.44% |
+| Recall | 99.28% |
+| F1-Score | 98.86% |
+
+## Usage
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+# Download model
+model_path = hf_hub_download(
+    repo_id="YOUR_USERNAME/xade-deepfake-detector",
+    filename="best_model.pt"
+)
+
+# Load model
+checkpoint = torch.load(model_path)
+# ... (load into your model class)
+```
+
+## Training Details
+
+- Samples: 100,000 training, 20,000 validation
+- Epochs: 10 (early stopping)
+- Optimizer: AdamW
+- Learning rate: 0.001
+- Batch size: 64
+
+## Citation
+```bibtex
+@misc{xade2026,
+  author = {Viktor Ahnström, Viktor Carlsson},
+  title = {XADE: Cross-Platform Explainable Deepfake Detection Using Vision-Language Models},
+  year = {2026},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/YOUR_USERNAME/xade-deepfake-detector}}
+}
+```