An active learning model to classify animal species in Hong Kong
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Abstract (arXiv)
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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References · 5
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014How transferable are features in deep neural networks?cited by 1,225
- 2019Efficient Pipeline for Camera Trap Image Reviewcited by 79
- 2022A Review of Deep Transfer Learning and Recent Advancementscited by 57
- 2019A deep active learning system for species identification and counting in camera trap imagescited by 44
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