Untangling the ice microphysical interactions within deep convective clouds presents an ongoing issue. Cumulonimbus have implications for localised precipitation and global radiative feedbacks. <em>In situ</em> flight campaign data is informative of these interactions and can consolidate our understanding. Identifying ice particle habits illustrates the evolving cloud on the micro-scale. In particular, the development and growth of graupel continues to be the least understood hydrometeor in numerical models. Consequently, in the ever-evolving machine learning landscape, a multitude of instrument and dataset specific ice habit identification algorithms are becoming commonplace. Here, we complete a key step of independently assessing several generalised and open source algorithms, to better understand their suitability for wider uptake. Evaluation and application of generalised convolutional neural networks (CNN), created by Jaffeux et al. (2025) has been undertaken on unseen two-dimensional stereo (2D-S) and High Volume Particle Spectrometer (HVPS) images from the Deep Convective Microphysics EXperiment (DCMEX). Models were not re-tuned to the dataset. Jaffeux et al.'s global CNN tested with human labelled 2D-S images obtained an accuracy of 72 % and F1 score (harmonic mean of precision and recall) of 70 %. While for HVPS images, the HVPS-specific CNN had an accuracy of 86 % and F1 score of 73 %, which was only marginally better than the global model. Then scaling up CNN application to the whole DCMEX dataset, graupel concentrations were inferred from rimed particle classification. The models constructed by Jaffeux et al. (2025) present an accessible, accurate and adjustable approach for particle identification of optical array probe images.