An active learning model to classify animal species in Hong Kong
CoRR(2024)
摘要
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.
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