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Data from: Automated taxonomic identification of insects with expert-level accuracy using effective feature transfer from convolutional networks

Cite this dataset

Valan, Miroslav et al. (2019). Data from: Automated taxonomic identification of insects with expert-level accuracy using effective feature transfer from convolutional networks [Dataset]. Dryad.


Rapid and reliable identification of insects is important in many contexts, from the detection of disease vectors and invasive species to the sorting of material from biodiversity inventories. Because of the shortage of adequate expertise, there has long been an interest in developing automated systems for this task. Previous attempts have been based on laborious and complex handcrafted extraction of image features, but in recent years it has been shown that sophisticated convolutional neural networks (CNNs) can learn to extract relevant features automatically, without human intervention. Unfortunately, reaching expert-level accuracy in CNN identifications requires substantial computational power and huge training datasets, which are often not available for taxonomic tasks. This can be addressed using feature transfer: a CNN that has been pretrained on a generic image classification task is exposed to the taxonomic images of interest, and information about its perception of those images is used in training a simpler, dedicated identification system. Here, we develop an effective method of CNN feature transfer, which achieves expert-level accuracy in taxonomic identification of insects with training sets of 100 images or less per category. Specifically, we extract rich representations of intermediate to high-level image features from the CNN architecture VGG16 pretrained on the ImageNet dataset. This information is fed into a linear support vector machine classifier, which is trained on the target problem. We tested the performance of our approach on two types of challenging taxonomic tasks: (1) identifying insects to higher groups when they are likely to belong to subgroups that have not been seen previously; and (2) identifying visually similar species that are difficult to separate even for experts. For the first task, our approach reaches > 92 % accuracy on one dataset (884 face images of 11 families of Diptera, all specimens representing unique species), and > 96 % accuracy on another (2936 dorsal habitus images of 14 families of Coleoptera, over 90 % of specimens belonging to unique species). For the second task, our approach outperforms a leading taxonomic expert on one dataset (339 images of three species of the Coleoptera genus Oxythyrea; 97 % accuracy), and both humans and traditional automated identification systems on another dataset (3845 images of nine species of Plecoptera larvae; 98.6 % accuracy). Reanalyzing several biological image identification tasks studied in the recent literature, we show that our approach is broadly applicable and provides significant improvements over previous methods, whether based on dedicated CNNs, CNN feature transfer, or more traditional techniques. Thus, our method, which is easy to apply, can be highly successful in developing automated taxonomic identification systems even when training datasets are small and computational budgets limited.

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