  Large Scale image classification is a challenging problem within the field of
computer vision. As the real world contains billions of different objects,
understanding the performance of popular techniques and models is vital in
order to apply them to real world tasks. In this paper, we evaluate techniques
and popular CNN based deep learning architectures to perform large scale
species classification on the dataset from iNaturalist 2019 Challenge. Methods
utilizing dataset pruning and transfer learning are shown to outperform models
trained without either of the two techniques. The ResNext based classifier
outperforms other model architectures over 10 epochs and achieves a top-one
validation error of 0.68 when classifying amongst the 1,010 species.
