iNaturalist 2018 Species Classification Competition

By Oisin Mac Aodha and Grant Van Horn

Computer vision will play a crucial role in visual search, self-driving cars, medicine and many other applications. Success will hinge on collecting and labeling large labeled datasets which will be used to train and test new algorithms.

One area that has seen great advances over the last five years is image classification i.e. determining automatically what objects are present in an image. Existing image classification datasets have an equal number of images for each class. However, the real world is long tailed: only a small percentage of classes are likely to be observed; most classes are infrequent or rare. As an example, photographs of plant and animal species are heavily imbalanced: a few species are abundant, while most species are rare.

It is estimated that the natural world contains several million species. Accurately monitoring species in the wild is an important step for measuring the health of the environment. However, most species will occur only rarely and experts will only recognize a few of them. This makes automatic species classification in images a really interesting test case for computer vision.

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