  We present tensor networks for feature extraction and refinement of
classifier performance. These networks can be initialised deterministically and
have the potential for implementation on near-term intermediate-scale quantum
(NISQ) devices. Feature extraction proceeds through a direct combination and
compression of images amplitude-encoded over just $\log N_{\text{pixels}}$
qubits. Performance is refined using `Quantum Stacking', a deterministic method
that can be applied to the predictions of any classifier regardless of
structure, and implemented on NISQ devices using data re-uploading. These
procedures are applied to a tensor network encoding of data, and benchmarked
against the 10 class MNIST and fashion MNIST datasets. Good training and test
accuracy are achieved without any variational training.
