A Collaborative Deep Learning Framework for Conservation

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Hey everyone! It’s been a while since our last update — we hope you haven’t forgotten about us! 😊

Over the past couple of months we’ve been thinking hard about the future of PyTorchWildlife and cooking up some exciting new features just for you.

After two years of community use, one thing has become super clear: most people prefer a nice graphical interface over writing code. People have been asking for a more seamless, unified experience that covers data management, processing, AI inference, analysis, and annotation all in one place.

So we built Sparrow Studio — a clean, unified UI built on top of PyTorchWildlife that brings exactly those tools together:

• Local and cloud-based data storage & management
• AI inference using the PyTorchWildlife model zoo
• Post-inference statistics and analysis
• Pre- and post-inference data annotation (easy bounding-box and category editing)
• Embedding visualization and feature retrieval tools

We’re kicking things off with a beta test before the official release. The Windows MSI installer is available directly on Zenodo: SPARROW Studio Installer (signed). Mac and Linux builds are in progress — reach out if you’d like to be on that list.

We’ve also expanded PyTorchWildlife itself into bioacoustics and overhead animal localization — both are out in this release:

• A dedicated bioacoustics module with several newly trained bioacoustics models
• OWL (Overhead Wildlife Locator) — our new generalized, point-based detection model for overhead imagery. (publication on the way.)

Sparrow Studio already has dedicated support for both, so beta testers can run inference and annotate bioacoustics recordings or overhead images directly in the UI.

With Sparrow Studio stepping into the picture, PyTorchWildlife itself will gradually evolve into a clean, stable API + high‑quality model zoo layered on top of a general model inference engine — called PW-Engine, while Sparrow Studio becomes the intuitive, everything‑in‑one-place frontend.

PW-Engine (PyTorch-Wildlife Engine) is an inference core written in Rust. It is model-agnostic and targets the full PyTorch-Wildlife model zoo and future third party models (e.g. BioClip and Perch) through four consumption surfaces: an HTTP REST API, a single-binary CLI, Python bindings, and a native C library for desktop integration. All four surfaces are feature-complete today; a data-management layer and MLOps functionality are the next milestones. PW-Engine also powers Sparrow Studio under the hood, and the same surfaces are open to anyone building their own frontend. A short overview — what it is, how it fits alongside the current Python API and Sparrow Studio, and how to pilot it — is here: PW-Engine Overview.

If you’re interested in API or backend work, or you run an inference-heavy pipeline and want to pilot PW-Engine early, we’d love your help shaping the next chapter of PyTorchWildlife. We’ll update our public task board later.

And one dream we’ve had for a long time: letting non‑coders fine‑tune their own models on their own data. Thanks to recent advances, we’re finally close — and this will be a major focus for both PyTorchWildlife and Sparrow Studio next.

Some of the UI features we needed for PyTorchWildlife also fit naturally as a frontend for Project Sparrow, another effort in our group focused on remote data-collection hardwares and edge computing. Since the name “Sparrow” already carried a warm, lively spirit — and the overlap between the projects made things simpler — we decided to call the UI Sparrow Studio. The name just reflects some shared roots and a bit of personality we liked.

Stay tuned! These updates are dropping very soon, and we’d genuinely love to have you in the Sparrow Studio beta. Drop us a message anytime — the more feedback the better! 🐦

• What's New

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.

👇 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

pip install PytorchWildlife

Please refer to our installation guide for more installation information.

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

Credits to Universidad de los Andes, Colombia.

Credits to Universidad de los Andes, Colombia.

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

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},
}

We’re excited to announce that Pytorch-Wildlife is now open to community contributions!
If you’d like to get involved and help improve the project, we’d love to have you on board.

👉 Check out our Contribution Guidelines:

📚 How to Participate

You’ll find everything you need there — from how to pick an issue, to submitting your first pull request.
Let’s build this together! 🐾🌱

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!