Training Parameters Configuration

Correctly configuring training parameters is essential for obtaining high-performing models tailored to your specific needs.

Basic Parameters

• Number of epochs: Number of times the algorithm goes through the entire dataset. A typical value is between 50 and 200 epochs.
• Batch size: Number of samples processed before the model parameters are updated. Common values are 8, 16, 32, or 64.
• Learning rate: Controls how quickly the model adapts to the data. Typical values are 0.001, 0.0001.

Advanced Parameters

• Data augmentation: Techniques to artificially increase the diversity of training data.
• Optimization strategies: Choice of optimization algorithm (SGD, Adam, etc.).
• Loss function: Defines how to measure the prediction error of the model.
• Transfer learning: Using pre-trained models as a starting point.

Recommendations

For beginners, we recommend starting with:

• 50 epochs
• A batch size of 16
• A learning rate of 0.001
• The Adam optimizer
• Basic data augmentation (rotation, zoom, flip)

Then adjust these parameters based on the performance observed on your validation set.