AAI_2025_Capstone_Chronicles_Combined

Le Coz, A., Herbin, S., & Adjed, F. (2024). Confidence calibration of classifiers with many classes. arXiv . https://arxiv.org/abs/2411.02988

Leorna, S., & Brinkman, T. (2022). Human vs. machine: Detecting wildlife in camera trap images. Ecological Informatics, 71 , 101876. https://doi.org/10.1016/j.ecoinf.2022.101876

Liu, L., Mou, C., & Xu, F. (2024). Improved wildlife recognition through fusing camera trap images and temporal metadata. Diversity, 16 (3), 139. https://doi.org/10.3390/d16030139

Li, C., Hu, X., & Chen, C. (2023). Confidence estimation using unlabeled data. arXiv . https://arxiv.org/abs/2307.10440

Morris, D. (2023). Camera trap ML survey [GitHub repository]. https://github.com/agentmorris/camera-trap-ml-survey

Tabak, M. A., Norouzzadeh, M. S., & Tabak, M. A. et al. (2019). Data from: Machine learning to classify animal species in camera trap images: Applications in ecology [Dataset]. Dryad. https://doi.org/10.5061/dryad.st8f5n7 Yang, D.-Q., Tan, K., Huang, Z.-P., Li, X.-W., Chen, B.-H., Ren, G.-P., & Xiao, W. (2021). An automatic method for removing empty camera trap images using ensemble learning. Ecology and Evolution, 11 (12), 7591–7601. https://doi.org/10.1002/ece3.7591

Zhong, Y., Li, X., Xie, J., & Zhang, J. (2023). A lightweight automatic wildlife recognition model design method mitigating shortcut learning. Animals, 13 (5), 838. https://doi.org/10.3390/ani1305083

Optuna. (n.d.). Optuna: A hyperparameter optimization framework. https://optuna.org/

DigitalOcean. (2023, August 7). Writing ResNet from scratch in PyTorch. DigitalOcean. https://www.digitalocean.com/community/tutorials/writing-resnet-from-scratch-in-pytorch

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