Task Completed: Model Training and Transfer Learning

This week was focused on training the selected CNN model using the collected and preprocessed datasets. The process involved:

Data Splitting:

  • The dataset was divided into:
    • Training: 70%
    • Validation: 15%
    • Testing: 15%

Transfer Learning:

  • Used pre-trained ResNet50 weights.
  • Fine-tuned the final layers for our specific deepfake classification task.

Training Parameters:

  • Learning Rate: 0.0001
  • Optimizer: Adam
  • Loss Function: Binary Cross-Entropy
  • Epochs: 20 (initial testing)
  • Batch Size: 32

The model was trained using TensorFlow/Keras, and training progress was monitored through loss and accuracy metrics.
Early stopping and model checkpointing techniques were applied to avoid overfitting and preserve the best-performing model.

The training results showed encouraging performance, with accuracy reaching above 90% on the validation dataset after tuning.