Train, Validation, Test Split for Machine Learning

The concept of the train, validation, and test split is crucial in machine learning to prevent model overfitting and ensure accurate evaluation in computer vision projects. The training set, typically comprising 70-80% of the data, is used to fit the model, while the validation set, about 10-20%, helps gauge its performance during training, guiding adjustments and early stopping. The test set, also around 10%, evaluates the model's final performance in a real-world scenario, ensuring it hasn't been tailored to the validation metrics. Effective data preprocessing and augmentation are essential, with augmentations applied only to the training set to enhance its size, while preprocessing standardizes data across all splits. Common pitfalls include train/test bleed, where similar images appear in different splits, and overemphasis on either training or validation/test metrics, potentially skewing evaluation outcomes. Roboflow offers tools to manage these processes, automatically handling issues like duplicates, ensuring the integrity of the train, validation, and test splits crucial for robust model deployment.