The DeepFaune initiative: a collaborative effort towards the automatic identification of French fauna in camera-trap images

Camera traps have revolutionized how ecologists monitor wildlife, but their full potential is realized only when the hundreds of thousands of collected images can be readily classified with minimal human intervention. Deep-learning classification models have allowed extraordinary progress towards this end, but trained models remain rare and are only now emerging for European fauna. We report on the first milestone of the DeepFaune initiative (https://www.deepfaune.cnrs.fr), a large-scale collaboration between more than 40 partners involved in wildlife research, conservation and management in France. We developed a two-step pipeline built upon the MegaDetectorV5 algorithm for the detection of animals, humans or vehicles, and on a classification model trained to recognize 22 species or higher-level taxa. The classification model achieved 0.97 validation accuracy and often >0.92 precision and recall for all classes, and these performances were overall higher than 0.90 when tested on an independent out-of-sample dataset. We implemented this pipeline in a software to classify images stored locally on a personal computer, so as to provide a free, user-friendly and high-performance tool for wildlife practitioners to automatically classify camera-trap images.