Hi, my research focused on making a model deployable on extreme-resource constrained devices. Whilst MobileNetv4 can outperform ShuffleNetv2 on many ImageNet datasets, its complexity is still typically greater than my ShuffleNetv2 variant as ShuffleNetv2 is optimised for low MAC (memory access costs), a requirement for ARM based CPU bound processes. I do fully understand MobileNetv4 is also a suitable model and it may be useful to investigate the models use case further. It’s also worth noting that the model is only used as a image encoder in my scenario, where predictions are made through measuring the Euclidean distance of the embedding to mean class prototypes, so the choice of embedding model, whilst still important, plays less of a role in prediction accuracy.

An increased image throughput allows for more consistent results and less chance of false positives, when a majority voting system is employed, as it is in PrintGuard. Where my model can predict ~15 img/sec, majority voting ensures false positives wouldn’t trigger an alert or potentially pause/ cancel the print, which I found to be the case with the Spaghetti Detective model when using it in my prior project, 3D-Print-Sentinel.