Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+---
+---
+language: en
+tags:
+- image-classification
+- computer-vision
+- deep-learning
+- face-detection
+- resnet
+datasets:
+- custom
+license: mit
+---
+
+# ResNet-based Face Classification Model 🎭
+
+This model is trained to distinguish between real human faces and AI-generated faces using a ResNet-based architecture.
+
+## Model Description 📝
+
+### Model Architecture
+- Deep CNN with residual connections based on ResNet architecture
+- Input shape: (224, 224, 3)
+- Multiple residual blocks with increasing filter sizes [64, 128, 256, 512]
+- Global average pooling
+- Dense layers with dropout for classification
+- Binary output with sigmoid activation
+
+### Task
+Binary classification to determine if a face image is real (human) or AI-generated.
+
+### Framework and Training
+- Framework: TensorFlow
+- Training Device: GPU
+- Training Dataset: Custom dataset of real and AI-generated faces
+- Validation Metrics:
+  - Accuracy: 52.45%
+  - Loss: 0.7246
+
+## Intended Use 🎯
+
+### Primary Intended Uses
+- Research in deepfake detection
+- Educational purposes in deep learning
+- Face authentication systems
+
+### Out-of-Scope Uses
+- Production-level face verification
+- Legal or forensic applications
+- Stand-alone security systems
+
+## Training Procedure 🔄
+
+### Training Details
+```python
+optimizer = Adam(learning_rate=0.0001)
+loss = 'binary_crossentropy'
+metrics = ['accuracy']
+```
+
+### Training Hyperparameters
+- Learning rate: 0.0001
+- Batch size: 32
+- Dropout rate: 0.5
+- Architecture:
+  - Initial conv: 64 filters, 7x7
+  - Residual blocks: [64, 128, 256, 512] filters
+  - Dense layer: 256 units
+
+## Evaluation Results 📊
+
+### Performance Metrics
+- Validation Accuracy: 52.45%
+- Validation Loss: 0.7246
+
+### Limitations
+- Performance is only slightly better than random chance
+- May struggle with high-quality AI-generated images
+- Limited testing on diverse face datasets
+
+## Usage 💻
+
+```python
+from tensorflow.keras.models import load_model
+import cv2
+import numpy as np
+
+# Load the model
+model = load_model('face_classification_model1')
+
+# Preprocess image
+def preprocess_image(image_path):
+    img = cv2.imread(image_path)
+    img = cv2.resize(img, (224, 224))
+    img = img / 255.0
+    return np.expand_dims(img, axis=0)
+
+# Make prediction
+image = preprocess_image('face_image.jpg')
+prediction = model.predict(image)
+is_real = prediction[0][0] > 0.5
+```
+
+
+## Ethical Considerations 🤝
+
+This model is designed for research and educational purposes only. Users should:
+- Obtain proper consent when processing personal face images
+- Be aware of potential biases in face detection systems
+- Consider privacy implications when using face analysis tools
+- Not use this model for surveillance or harmful applications
+
+## Technical Limitations ⚠️
+
+1. Current performance limitations:
+   - Accuracy only slightly above random chance
+   - May require ensemble methods for better results
+   - Limited testing on diverse datasets
+
+2. Recommended improvements:
+   - Extended training with larger datasets
+   - Implementation of data augmentation
+   - Hyperparameter optimization
+   - Transfer learning from pre-trained models
+
+## Citation 📚
+
+```bibtex
+@software{face_classification_model1,
+  author = {Your Name},
+  title = {Face Classification Model using ResNet Architecture},
+  year = {2024},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/arsath-sm/face_classification_model1}
+}
+```
+
+## Contributors 👥
+- Arsath S.M
+- Faahith K.R.M
+- Arafath M.S.M
+
+University of Jaffna
+
+## License 📄
+This model is licensed under the MIT License.