params.yaml

# Enable or disable data augmentation for training
AUGMENTATION: True

# Specify the input image size as required by the VGG16 model
IMAGE_SIZE: [224, 224, 3]  # Height, Width, Channels (RGB)

# Define the batch size for training
BATCH_SIZE: 16

# Indicate whether to include the top classification layer of the model
INCLUDE_TOP: False  # Useful when using transfer learning without the top layer

# Number of epochs for training the model
EPOCHS: 5

# Number of classes in the classification task
# CLASSES: 2  # Example: binary classification (e.g., 0 and 1)
CLASSES: 4 # Example: MULTI-CLASS classification (e.g., 0,1,2,3)

# Pre-trained weights to use for the model
WEIGHTS: imagenet  # This indicates using ImageNet weights for transfer learning

# Set the learning rate for the optimizer
LEARNING_RATE: 0.01  # Controls how much to adjust weights during training