Deep CNNs for large scale species classification
This work provides an evaluation of existing CNN techniques for large-scale species classification, which is an incremental contribution for computer vision researchers working on biodiversity applications.
This paper evaluates deep CNN architectures for large-scale species classification using the iNaturalist 2019 Challenge dataset. The ResNext classifier, when combined with dataset pruning and transfer learning, achieved a top-one validation error of 0.68 across 1,010 species.
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.