Brook, Barry W.Buettel, Jessie C.Lunteren, Peter vanRajmohan, Prakash P.Aandahl, R. Zach2026-06-272026-06-27ORCID:/0000-0001-6737-7468/work/218726680https://hdl.handle.net/1885/733812121Monitoring wildlife is crucial for making informed conservation and land-management decisions. Remotely triggered cameras are widely used for this purpose, but the resulting ’big data’ are laborious to process. Although artificial intelligence (AI) offers a powerful solution to this bottleneck, it has been challenging for ecologists and practitioners without substantial technical expertise to tailor current approaches to their specific use cases. Generic, online offerings also have issues of ongoing costs and data privacy. Here we present an open-source, scalable, modular, cross-platform workflow, deployed using Docker containers, which leverages deep learning for wildlife image classification. It can be run using simple command-line prompts or via a user-friendly graphical user interface (AddaxAI). It enables end-users to easily execute a full range of tasks—from animal detection and counting to species identification—on local or cloud GPU-accelerated machines. It also integrates with the widely used open-source camera-trapping software ‘Camelot’, writing AI-classification data directly to image metadata and to CSV files, ready for either expert verification or direct data analysis. The result is a user-friendly but powerful multi-platform application for wildlife-image classification and research pipelines. An example case study with Tasmanian wildlife demonstrates the utility of our classifier training and inference workflow.This work was funded by the Australian Research Council through projects FT160100101 and CE170100015 to BWB. Hardware and virtual machine Infrastructure support was provided through Australian Research Data Commons Nectar Research Cloud, funded by the Australian government and the University of Tasmania’s High Performance Computing facility. This work was funded by the Australian Research Council through projects FT160100101 and CE170100015 to BWB. Hardware and virtual machine Infrastructure support was provided through Australian Research Data Commons Nectar Research Cloud, funded by the Australian21enPublisher Copyright: © 2025, Centre Mersenne. All rights reserved.AddaxAIArtificial IntelligenceCamelotCamera TrappingDeep LearningDockerEcological Data ProcessingImage ClassificationMegaDetectorOpen-Source SoftwareWildlife Monitoring202510.24072/pcjournal.565105007066004