• Author(s): Andres Hernandez, Zhongqi Miao, Luisa Vargas, Rahul Dodhia, Juan Lavista

The paper titled “Pytorch-Wildlife: An Open-Source Deep Learning Platform for Wildlife Monitoring” addresses the urgent need for large-scale wildlife monitoring in response to the alarming decline in global biodiversity. This decline is driven by various factors, necessitating the development of automated deep learning methods for data processing in wildlife monitoring. However, the application of 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, the paper introduces Pytorch-Wildlife, an open-source deep learning platform built on PyTorch. Pytorch-Wildlife is designed to facilitate the creation, modification, and sharing of powerful AI models. The platform emphasizes usability and accessibility, making it accessible to individuals with limited or no technical background. It offers a modular codebase to simplify feature expansion and further development. Additionally, Pytorch-Wildlife provides an intuitive, user-friendly interface, accessible through local installation or Hugging Face, for animal detection and classification in images and videos.

The paper highlights two real-world applications of Pytorch-Wildlife. The platform 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 an impressive 98% accuracy, while the Amazon model has a 92% recognition accuracy for 36 animals in 90% of the data. These results demonstrate the platform’s effectiveness in real-world wildlife monitoring scenarios.

As Pytorch-Wildlife continues to evolve, the aim is to integrate more conservation tasks, addressing various environmental challenges. The platform’s emphasis on usability and accessibility, combined with its powerful capabilities, positions it as a valuable tool for wildlife monitoring and conservation efforts. This paper provides a comprehensive overview of Pytorch-Wildlife’s features, applications, and potential for future development in the field of wildlife monitoring.