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Iterative human and automated identification of wildlife images

A preprint version of the article is available at arXiv.


Camera trapping is increasingly being used to monitor wildlife, but this technology typically requires extensive data annotation. Recently, deep learning has substantially advanced automatic wildlife recognition. However, current methods are hampered by a dependence on large static datasets, whereas wildlife data are intrinsically dynamic and involve long-tailed distributions. These drawbacks can be overcome through a hybrid combination of machine learning and humans in the loop. Our proposed iterative human and automated identification approach is capable of learning from wildlife imagery data with a long-tailed distribution. Additionally, it includes self-updating learning, which facilitates capturing the community dynamics of rapidly changing natural systems. Extensive experiments show that our approach can achieve an ~90% accuracy employing only ~20% of the human annotations of existing approaches. Our synergistic collaboration of humans and machines transforms deep learning from a relatively inefficient post-annotation tool to a collaborative ongoing annotation tool that vastly reduces the burden of human annotation and enables efficient and constant model updates.

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Fig. 1: Overview of a realistic animal classification system.
Fig. 2: Label efficiency comparison with transfer learning on the group 2 validation set (ordered with respect to training sample size).
Fig. 3: Failure cases.


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We thank T. Gu, A. Ke, H. Rosen, A. Wu, C. Jurgensen, E. Lai, M. Levy and E. Silverberg for annotating the images used in this study, as well as everyone else involved in this project. Data collection was supported by J. Brashares and through grants to K.M.G. from HHMI BioInteractive, the Rufford Foundation, Idea Wild, the Explorers Club and the UC Berkeley Center for African Studies. We are grateful for the support of Gorongosa National Park, especially M. Stalmans, in permitting and facilitating this research. Z.L. is supported by NTU NAP. K.M.G. is supported by Schmidt Science Fellows in partnership with the Rhodes Trust, and the National Center for Ecological Analysis and Synthesis Director’s Postdoctoral Fellowship. M.S.P. is funded by National Science Foundation grant no. PRFB #1810586.

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Authors and Affiliations



This study was conceived by Z.M., Z.L., K.M.G. and M.S.P. The methods were designed by Z.M. and Z.L. Code was written by Z.M., and the computations were undertaken by Z.M. with help from Z.L. The main text was drafted by Z.M. and Z.L., with contributions, editing and comments from all authors. K.M.G. and M.S.P. collected all data and oversaw annotation. Z.M. created all figures and tables in consultation with W.M.G., Z.L. and S.X.Y.

Corresponding authors

Correspondence to Zhongqi Miao or Wayne M. Getz.

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The authors declare no competing interests.

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Peer review information Nature Machine Intelligence thanks Dan Morris and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 The distribution of images across species in the entire camera trap data set.

There are 55 categories in total. 14 categories were tagged as "unknown” (colored in orange) and used to improve and validate our model’s sensitivity to novel and difficult samples.

Extended Data Fig. 2 The distribution of species across the two groups of data.

We split the data set into two groups to mimic two sequential data collection seasons. In the first group, there are 26 categories (colored in blue). The second group has 41 categories. Group 1 is used in the first period experiment to train a baseline model, and Group 2 is used in the second period experiment to test and update the model.

Extended Data Fig. 3 The overall experimental workflow of our framework.

In the first time step, a baseline model is trained using group 1 training data with only 26 categories. Next, the classifier is fine-tuned using the 14 unknown categories and energy-based loss to increase the sensitivity to out-of-distribution categories. After the classifier is fine-tuned, the classifier is then used to predict classifications for group 2 training data. Here, high-confidence predictions are trusted while low-confidence predictions are flagged for human annotation. In the final step, both machine- and human-annotations are used to update the previous model with OLTR and semi-supervised techniques. Once the model is updated, the classifier is fine-tuned using energy-based loss again for out-of-distribution sensitivity.

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Miao, Z., Liu, Z., Gaynor, K.M. et al. Iterative human and automated identification of wildlife images. Nat Mach Intell 3, 885–895 (2021).

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