The well-documented, species-rich, and diverse group of ants (Formicidae) are important 1 ecological bioindicators for species richness, ecosystem health, and biodiversity, but ant 2 species identification is complex and requires specific knowledge. In the past few years, 3 insect identification from images has seen increasing interest and success, with processing 4 speed improving and costs lowering. Here we propose deep learning (in the form of a 5 convolutional neural network (CNN)) to classify ants at species level using AntWeb 6 images. We used an Inception-ResNet-V2-based CNN to classify ant images, and three 7 shot types with 10,204 images for 97 species, in addition to a multi-view approach, for 8 training and testing the CNN while also testing a worker-only set and an AntWeb 9 protocol-deviant test set. Top 1 accuracy reached 62% -81%, top 3 accuracy 80% -92%, 10 and genus accuracy 79% -95% on species classification for different shot type approaches. 11The head shot type outperformed other shot type approaches. Genus accuracy was broadly 12 similar to top 3 accuracy. Removing reproductives from the test data improved accuracy 13 only slightly. Accuracy on AntWeb protocol-deviant data was very low. In addition, we 14 make recommendations for future work concerning image threshold, distribution, and 15 quality, multi-view approaches, metadata, and on protocols; potentially leading to higher 16 accuracy with less computational effort. BOER & VOS