A Collaborative Deep Learning Framework for Conservation

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📣 Announcements

• We have fully recreated our documentation page with MKDocs. Please take a look and let us know what you think! (Special thanks to @ss26 for creating the foundation of this documentation page!)

• We will also be releasing new MegaDetectorV6 model weights this coming week and add new performance numbers in our model zoo. We did make a mistake when evaluating the V5 model because of input resolution mismatch as the V5 model was trained using 1280 size inputs and V6 models were trained using 640 models. Now we have retrained 1280 V6 models to keep everything consistent. We also evaluated our models using pycocotool this time instead of Ultralytics evaluation functions to make the evaluations on the MIT and Apache models more easier.

Previous versions:

• What's New

👋 Welcome to Pytorch-Wildlife

PyTorch-Wildlife is an AI platform designed for the AI for Conservation community to create, modify, and share powerful AI conservation models. It allows users to directly load a variety of models including MegaDetector, DeepFaune, and HerdNet from our ever expanding model zoo for both animal detection and classification. In the future, we will also include models that can be used for applications, including underwater images and bioacoustics. We want to provide a unified and straightforward experience for both practicioners and developers in the AI for conservation field. Your engagement with our work is greatly appreciated, and we eagerly await any feedback you may have.

Explore the codebase, functionalities and user interfaces of Pytorch-Wildlife through our documentation, interactive HuggingFace web app or local demos and notebooks.

🚀 Quick Start

👇 Here is a quick example on how to perform detection and classification on a single image using PyTorch-wildlife

import numpy as np
from PytorchWildlife.models import detection as pw_detection
from PytorchWildlife.models import classification as pw_classification

img = np.random.randn(3, 1280, 1280)

# Detection
detection_model = pw_detection.MegaDetectorV6() # Model weights are automatically downloaded.
detection_result = detection_model.single_image_detection(img)

#Classification
classification_model = pw_classification.AI4GAmazonRainforest() # Model weights are automatically downloaded.
classification_results = classification_model.single_image_classification(img)

More models can be found in our model zoo

⚙️ Install Pytorch-Wildlife

pip install PytorchWildlife

Please refer to our installation guide for more installation information.

📃 Documentation

Please also go to our newly made dofumentation page for more information:

🖼️ Examples

Image detection using MegaDetector

Credits to Universidad de los Andes, Colombia.

Image classification with MegaDetector and AI4GAmazonRainforest

Credits to Universidad de los Andes, Colombia.

Opossum ID with MegaDetector and AI4GOpossum

Credits to the Agency for Regulation and Control of Biosecurity and Quarantine for Galápagos (ABG), Ecuador.

🖋️ Cite us!

We have recently published a summary paper on Pytorch-Wildlife. The paper has been accepted as an oral presentation at the CV4Animals workshop at this CVPR 2024. Please feel free to cite us!

@misc{hernandez2024pytorchwildlife,
      title={Pytorch-Wildlife: A Collaborative Deep Learning Framework for Conservation}, 
      author={Andres Hernandez and Zhongqi Miao and Luisa Vargas and Sara Beery and Rahul Dodhia and Juan Lavista},
      year={2024},
      eprint={2405.12930},
      archivePrefix={arXiv},
}

Also, don't forget to cite our original paper for MegaDetector:

@misc{beery2019efficient,
      title={Efficient Pipeline for Camera Trap Image Review},
      author={Sara Beery and Dan Morris and Siyu Yang},
      year={2019}
      eprint={1907.06772},
      archivePrefix={arXiv},
}

🤝 Existing Collaborators and Contributors

The extensive collaborative efforts of Megadetector have genuinely inspired us, and we deeply value its significant contributions to the community. As we continue to advance with Pytorch-Wildlife, our commitment to delivering technical support to our existing partners on MegaDetector remains the same.

Here we list a few of the organizations that have used MegaDetector. We're only listing organizations who have given us permission to refer to them here or have posted publicly about their use of MegaDetector.

We are also building a list of contributors and will release in future updates! Thank you for your efforts!

👉 Full list of organizations

• (Newly Added) TerrOïko (OCAPI platform)
• Arizona Department of Environmental Quality
• Blackbird Environmental
• Camelot
• Canadian Parks and Wilderness Society (CPAWS) Northern Alberta Chapter
• Conservation X Labs
• Czech University of Life Sciences Prague
• EcoLogic Consultants Ltd.
• Estación Biológica de Doñana
• Idaho Department of Fish and Game
• Island Conservation
• Myall Lakes Dingo Project
• Point No Point Treaty Council
• Ramat Hanadiv Nature Park
• SPEA (Portuguese Society for the Study of Birds)
• Synthetaic
• Taronga Conservation Society
• The Nature Conservancy in Wyoming
• TrapTagger
• Upper Yellowstone Watershed Group
• Applied Conservation Macro Ecology Lab, University of Victoria
• Banff National Park Resource Conservation, Parks Canada
• Blumstein Lab, UCLA
• Borderlands Research Institute, Sul Ross State University
• Capitol Reef National Park / Utah Valley University
• Center for Biodiversity and Conservation, American Museum of Natural History
• Centre for Ecosystem Science, UNSW Sydney
• Cross-Cultural Ecology Lab, Macquarie University
• DC Cat Count, led by the Humane Rescue Alliance
• Department of Fish and Wildlife Sciences, University of Idaho
• Department of Wildlife Ecology and Conservation, University of Florida
• Ecology and Conservation of Amazonian Vertebrates Research Group, Federal University of Amapá
• Gola Forest Programme, Royal Society for the Protection of Birds (RSPB)
• Graeme Shannon's Research Group, Bangor University
• Hamaarag, The Steinhardt Museum of Natural History, Tel Aviv University
• Institut des Science de la Forêt Tempérée (ISFORT), Université du Québec en Outaouais
• Lab of Dr. Bilal Habib, the Wildlife Institute of India
• Mammal Spatial Ecology and Conservation Lab, Washington State University
• McLoughlin Lab in Population Ecology, University of Saskatchewan
• National Wildlife Refuge System, Southwest Region, U.S. Fish & Wildlife Service
• Northern Great Plains Program, Smithsonian
• Quantitative Ecology Lab, University of Washington
• Santa Monica Mountains Recreation Area, National Park Service
• Seattle Urban Carnivore Project, Woodland Park Zoo
• Serra dos Órgãos National Park, ICMBio
• Snapshot USA, Smithsonian
• Wildlife Coexistence Lab, University of British Columbia
• Wildlife Research, Oregon Department of Fish and Wildlife
• Wildlife Division, Michigan Department of Natural Resources
• Department of Ecology, TU Berlin
• Ghost Cat Analytics
• Protected Areas Unit, Canadian Wildlife Service
• School of Natural Sciences, University of Tasmania (story)
• Kenai National Wildlife Refuge, U.S. Fish & Wildlife Service (story)
• Australian Wildlife Conservancy (blog, blog)
• Felidae Conservation Fund (WildePod platform) (blog post)
• Alberta Biodiversity Monitoring Institute (ABMI) (WildTrax platform) (blog post)
• Shan Shui Conservation Center (blog post) (translated blog post)
• Irvine Ranch Conservancy (story)
• Wildlife Protection Solutions (story, story)
• Road Ecology Center, University of California, Davis (Wildlife Observer Network platform)
• The Nature Conservancy in California (Animl platform)
• San Diego Zoo Wildlife Alliance (Animl R package)

Important

If you would like to be added to this list or have any questions regarding MegaDetector and Pytorch-Wildlife, please email us or join us in our Discord channel: