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arsath-sm
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face_classification_model1

Image Classification
English
computer-vision
deep-learning
face-detection
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face_classification_model1
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---
license: mit
language:
- en
tags:
- image-classification
- computer-vision
- deep-learning
- face-detection
---
---
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.