Insect Classification With Heirarchical Deep Convolutional Neural Networks Convolutional Neural Networks for Visual Recognition CS 231 N
Classification of insect species is is a particularly difficult challenge because of the high degree of similarity of the appearance between distinct species. Given the visual commonalities across different levels of taxonomic hierarchies (i.e. class, order, family, etc. insect classifcation provides a unique opportunity to apply emerging techniques used in Hierarchical Deep CNNs (HD CNNs) Inspired by the success of recent work on HD CNNs, our pipeline for designing, training and testing a hierarchical architecture achieves promising results considering the particularly messy nature of our data set. Working with 217,657 insect images from 277 unique classes, we achieved a top 5 misclassification rate of 22.54% and a top 10 misclassification rate of