Data from: Machine learning to classify animal species in camera trap images: applications in ecology

Micheal A. Tabak, Mohammad Sadegh Norouzzadeh, Michael A. Tabak, David W. Wolfson, Steven J. Sweeney, Paul A. Di Salvo, Ryan S. Miller, Jesse S. Lewis, Jeff Clune, Ryan K. Brook, Elizabeth G. Mandeville, Paul M. Lukacs, Anna K. Moeller, Raoul K. Boughton, Bethany Wight, James C. Beasley & Peter E. Schlichting
Motion‐activated cameras (“camera traps”) are increasingly used in ecological and management studies for remotely observing wildlife and are amongst the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analysed, typically by visually observing each image, in order to extract data that can be used in ecological analyses. We trained machine learning models using convolutional neural networks with the ResNet‐18 architecture and 3,367,383 images...
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