  Prototypical-part models are a popular interpretable alternative to black-box
deep learning models for computer vision. However, they are difficult to train,
with high sensitivity to hyperparameter tuning, inhibiting their application to
new datasets and our understanding of which methods truly improve their
performance. To facilitate the careful study of prototypical-part networks
(ProtoPNets), we create a new framework for integrating components of
prototypical-part models -- ProtoPNeXt. Using ProtoPNeXt, we show that applying
Bayesian hyperparameter tuning and an angular prototype similarity metric to
the original ProtoPNet is sufficient to produce new state-of-the-art accuracy
for prototypical-part models on CUB-200 across multiple backbones. We further
deploy this framework to jointly optimize for accuracy and prototype
interpretability as measured by metrics included in ProtoPNeXt. Using the same
resources, this produces models with substantially superior semantics and
changes in accuracy between +1.3% and -1.5%. The code and trained models will
be made publicly available upon publication.
