2021/02/03 by Raj Prateek Kosaraju, Kosaraju, Raj Prateek
Biochemistry, Genetics and Molecular Biology · Environmental Science · #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Identification and Quantification in Food #Species Distribution and Climate Change
paper · pdf · doi:10.48550/arxiv.2102.01863
openalex publication_date 2021/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.