Pytorch-Wildlife：面向保护的协作式深度学习框架
Pytorch-Wildlife: A Collaborative Deep Learning Framework for Conservation
AI总结：
针对全球生物多样性下降下野生动物监测的技术复杂性与跨学科障碍，本文提出开源深度学习平台Pytorch-Wildlife，通过简化模型创建与共享，助力非技术人员应用AI，已在亚马逊雨林和加拉帕戈斯群岛实现高精度物种识别，推动保护工作。
摘要：
由多种因素驱动的全球生物多样性急剧下降，凸显了大规模野生动物监测的迫切需求。对此，科学家们已转向自动化深度学习方法来处理野生动物监测中的数据。然而，由于这些先进方法的复杂性和对专业知识的需求，主要是技术挑战和跨学科障碍，在现实场景中应用这些方法具有挑战性。为应对这些挑战，我们引入Pytorch-Wildlife，这是一个基于PyTorch构建的开源深度学习平台，旨在创建、修改和共享强大的AI模型。该平台强调可用性和可访问性，使技术背景有限或无技术背景的个人也能使用。它提供直观的用户友好界面，可通过本地安装或Hugging Face访问，用于图像和视频中的动物检测与分类。作为两个实际应用案例，Pytorch-Wildlife已被用于训练亚马逊雨林物种识别的动物分类模型和加拉帕戈斯群岛入侵负鼠识别模型，其中负鼠模型准确率达98%，亚马逊模型对90%数据中的36种动物识别准确率为92%。随着Pytorch-Wildlife的发展，我们旨在整合更多保护任务，以应对各种环境挑战。
英文摘要：
The alarming decline in global biodiversity, driven by various factors, underscores the urgent need for large-scale wildlife monitoring. In response, scientists have turned to automated deep learning methods for data processing in wildlife monitoring. However, applying these advanced methods in real-world scenarios is challenging due to their complexity and the need for specialized knowledge, primarily because of technical challenges and interdisciplinary barriers. To address these challenges, we introduce Pytorch-Wildlife, an open-source deep learning platform built on PyTorch. It is designed for creating, modifying, and sharing powerful AI models. This platform emphasizes usability and accessibility, making it accessible to individuals with limited or no technical background. It also offers a modular codebase to simplify feature expansion and further development. Pytorch-Wildlife offers an intuitive, user-friendly interface, accessible through local installation or Hugging Face, for animal detection and classification in images and videos. As two real-world applications, Pytorch-Wildlife has been utilized to train animal classification models for species recognition in the Amazon Rainforest and for invasive opossum recognition in the Galapagos Islands. The Opossum model achieves 98% accuracy, and the Amazon model has 92% recognition accuracy for 36 animals in 90% of the data. As Pytorch-Wildlife evolves, we aim to integrate more conservation tasks, addressing various environmental challenges. Pytorch-Wildlife is available at https://github.com/microsoft/CameraTraps.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
