Update README.md

README.md

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+
 metrics:
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 - accuracy
 - f1
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+
+---
+
+# NotUrFace-AI: Deepfake Detection Model
+
+## Model Details
+
+### Model Description
+
+NotUrFace-AI is a deepfake detection model designed to classify video content as real or fake. It processes video frames using **TensorFlow** and applies advanced machine learning techniques to identify synthetic or manipulated media. This is a passion project aimed at combating deepfake proliferation. The model is particularly useful for:
+
+- **Social media content moderation**
+- **Digital forensics**
+- **Research in deepfake detection and AI ethics**
+
+**Developer:** Sarvansh Pachori  
+**Model Type:** Deepfake detection (video-based classification)  
+**Finetuned from:** XceptionNet (pretrained)  
+
+### Model Sources
+
+- **Repository:** [GitHub Repository](https://github.com/username/NotUrFace-AI)  
+- **Demo:** [Hugging Face Space](https://huggingface.co/spaces/sarvansh/NotUrFace-AI)  
+
+## Usage
+
+### Direct Use
+
+- Classifying videos as real or fake for research, moderation, or forensic purposes.
+
+### Downstream Use
+
+- The model can be fine-tuned with additional deepfake datasets for improved detection on specific video types.
+
+### Out-of-Scope Use
+
+- The model is not intended for legal decision-making or high-stakes scenarios where absolute certainty is required.
+
+## Bias, Risks, and Limitations
+
+- Accuracy may vary depending on dataset bias and the quality of input videos.
+- False positives or false negatives can occur, requiring human verification for critical applications.
+- It may struggle with detecting highly sophisticated, unseen deepfake techniques.
+
+### Recommendations
+
+- Users should validate outputs in real-world applications before making critical decisions.
+- Future improvements may include training on a more diverse dataset to reduce bias.
+
+## Getting Started
+
+Use the following code snippet to get started:
+
+```python
+from transformers import AutoModelForImageClassification, AutoTokenizer
+
+model = AutoModelForImageClassification.from_pretrained("sarvansh/NotUrFace-AI")
+tokenizer = AutoTokenizer.from_pretrained("sarvansh/NotUrFace-AI")
+```
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+The model was tested on unseen samples from the FaceForensics++ and CelebDFv2 datasets.
+
+#### Metrics
+
+- **Accuracy**: Measures correct classifications.
+- **F1 Score**: Balances precision and recall.
+
+### Results
+
+| Metric              | Value  |
+| ------------------- | ------ |
+| Training Accuracy   | 98.44% |
+| Validation Accuracy | 97.05% |
+| Test Accuracy       | 95.93% |
+
+**Disclaimer:** These results were obtained using the FaceForensics++ and CelebDFv2 datasets. Performance in real-world scenarios may vary.
+
+### Tips for Best Performance
+
+- The model works best with videos that have **proper lighting**.
+- It only analyzes the **first 1-1.5 seconds** of a video, so ensure the clip is appropriately selected for evaluation.
+
+## Model Architecture and Objective
+
+- **Feature Extraction:** XceptionNet (pretrained on ImageNet) to extract spatial features.
+- **Temporal Analysis:** LSTM layers to analyze frame dependencies.
+- **Classification:** Fully connected layers for final binary classification.
+
+## Citation
+
+If using this model in research, please cite:
+
+**BibTeX:**
+
+```
+@article{noturface-ai,
+  author = {Sarvansh Pachori},
+  title = {NotUrFace-AI: Deepfake Detection Model},
+  year = {2024},
+  journal = {Hugging Face Model Hub},
+  url = {https://huggingface.co/sarvansh/NotUrFace-AI}
+}
+```
+
+## Contact Information
+
+For any issues, improvements, or inquiries, contact:
+
+- **Author:** Sarvansh Pachori  
+- **Email:** [sarvansh.pachori45@gmail.com](mailto:sarvansh.pachori45@gmail.com)  
+- **GitHub Issues:** [GitHub Repository](https://github.com/sarvansh30/NotUrFace-AI)  
+
+---
+