Exploring the effect of training set size and number of categories on ice crystal classification through a contrastive semi-supervised learning algorithm
A contrastive semi-supervised learning (CSSL) algorithm for ice crystal classification that reduces manual labeling effort by 90% (154 hours saved) while maintaining high accuracy, achieving 89.6% accuracy with only 25% of labeled data.
Jun 27, 2025
IceDetectNet: a rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme
A rotated object detection algorithm (IceDetectNet) with a multi-label classification scheme for classifying components of aggregated ice crystals, achieving 92% accuracy for aggregate/non-aggregate detection and 86% for basic habit classification.
Dec 19, 2024