  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.
