An active learning model to classify animal species in Hong Kong
Gareth Lamb Ching Hei Lo Jin Wu Calvin K. F. Lee
Camera traps are used by ecologists globally as an efficient and non-invasive
method to monitor animals. While it is time-consuming to manually label the
collected images, recent advances in deep learning and computer vision has made
it possible to automating this process [1]. A major obstacle to this is the
generalisability of these models when applying these images to independently
collected data from other parts of the world [2]. Here, we use a deep active
learning workflow [3], and train a model that is applicable to camera trap
images collected in Hong Kong.