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 50 partners involved in wildlife research, conservation and management in France. We developed a classification model trained to recognize 26 species or higher-level taxa that are common in Europe, with an emphasis on mammals. The classification model achieved 0.97 validation accuracy and often > 0.95 precision and recall for many classes. These performances were generally higher than 0.90 when tested on independent out-of-sample datasets for which we used image redundancy contained in sequences of images. We implemented our model 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. The DeepFaune initiative is an ongoing project, with new partners joining regularly, which allows us to continuously add new species to the classification model.

相机陷阱彻底改变了生态学家监测野生动物的方式，但只有当数十万张收集到的图像能够在最少的人为干预下轻松分类时，它们的全部潜力才能发挥出来。深度学习分类模型在这方面取得了非凡的进展，但训练有素的模型仍然很少见，而且现在才在欧洲动物群中出现。我们报道 DeepFaune 计划 (https://www.deepfaune.cnrs.fr) 的第一个里程碑，这是法国 50 多个参与野生动物研究、保护和管理的合作伙伴之间的大规模合作。我们开发了一个分类模型，经过训练可以识别欧洲常见的 26 个物种或更高级别的分类单元，重点是哺乳动物。分类模型实现了 0.97 的验证准确度，并且对于许多类别而言，精度和召回率通常 > 0.95。当我们使用图像序列中包含的图像冗余在独立的样本外数据集上进行测试时，这些性能通常高于 0.90。我们在软件中实现了我们的模型，对个人计算机上本地存储的图像进行分类，从而为野生动物从业者提供免费、用户友好且高性能的工具来自动对相机陷阱图像进行分类。DeepFaune 计划是一个正在进行的项目，定期有新的合作伙伴加入，这使我们能够不断向分类模型添加新物种。