Rejecting low-confidence predictions from softmax classifiers raises automated herbarium label accuracy from ~86% to near-human level at reduced coverage, enabling a 600,000-specimen flowering-time analysis.
Toward a large-scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras,
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Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process
Rejecting low-confidence predictions from softmax classifiers raises automated herbarium label accuracy from ~86% to near-human level at reduced coverage, enabling a 600,000-specimen flowering-time analysis.