This quantum image classification tutorial demonstrates how to train a utility-scale quantum image classifier with the BlueQubit SDK using the Pauli-Path Simulation (PPS) pipeline and Simultaneous Perturbation Stochastic Approximation (SPSA). Each image is split into blocks and encoded as state-preparation circuits with BlockImageLoader, a parameterized trainer ansatz with single-qubit Pauli-Z observables acts as the feature extractor, and a linear classification head is trained end-to-end with SPSA. A small two-class real-data subset of the Honda Scenes Dataset (clear-day vs snow-day driving scenes) keeps the run to a few minutes while showing how the same method scales to 60+ qubit circuits, where forward state-vector simulation is intractable.