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This page is the syndication target for the Microsoft Biodiversity umbrella aggregator. The canonical project documentation lives in README.md.
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MegaDetector now has its own home at microsoft/MegaDetector. The full model zoo and PyTorch-Wildlife framework live at microsoft/Pytorch-Wildlife, with everything tied together under the microsoft/Biodiversity umbrella.
MegaDetector is an open-source AI model from the Microsoft AI for Good Lab that detects animals in camera-trap imagery. Used by more than 80 conservation organizations worldwide, MegaDetector automates the review of camera-trap images so researchers can skip empty frames and focus on science. It does not identify species — it locates animals so they can be passed to a downstream classifier.
Our mission is to create a global community where conservation scientists can collaborate — sharing datasets and deep learning architectures for wildlife conservation. We're committed to supporting, maintaining, and advancing MegaDetector to ensure its continued relevance, performance, and impact for biodiversity research worldwide.
We have officially released our 6th version of MegaDetector, MegaDetectorV6. In the next generation of MegaDetector, we focused on computational efficiency, performance, modernizing of model architectures, and licensing. We have trained multiple new models using different model architectures that are optimized for performance and low-budget devices, including YOLOv9, YOLOv10, and RT-DETR for maximum user flexibility.
For example, the MegaDetectorV6-Ultralytics-YoloV10-Compact (MDV6-yolov10-c) model has only 2% of the parameters of the previous MegaDetectorV5 (2.3M vs. 139.9M) and still exhibits comparable performance on our validation datasets.
To test the newest version of MegaDetector with all the existing functionalities, you can use our Hugging Face interface or simply load the model with PyTorch-Wildlife. The weights will be automatically downloaded:
from PytorchWildlife.models import detection as pw_detection
detection_model = pw_detection.MegaDetectorV6()We will continuously fine-tune our V6 models on newly collected public and private data to further improve generalization performance.
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All versions of MegaDetector and corresponding performance can be found in the model zoo.
From now on, we encourage our users to use MegaDetectorV6 as their default animal detection model and choose whichever model fits the project needs. To reduce potential confusion, we have also standardized the model names into MDV6-Compact and MDV6-Extra for two model sizes using the same architecture. Learn how to use MegaDetectorV6 in our image demo and our demo data installation guideline.
For those interested in accessing the previous MegaDetector repository, which utilizes the same MegaDetectorV5 model weights and was primarily developed by Dan Morris during his time at Microsoft, please visit the archive branch of the Biodiversity repository (formerly microsoft/CameraTraps), or you can visit this forked repository that Dan Morris is currently actively maintaining.
MegaDetector is one project in a larger open-source ecosystem from the AI for Good Lab:
| Repo | Purpose |
|---|---|
| microsoft/Biodiversity | The umbrella repository — documentation hub for the AI for Good Lab's biodiversity work |
| microsoft/MegaDetector | This project — animal detection in camera-trap imagery |
| microsoft/Pytorch-Wildlife | The collaborative deep learning framework hosting MegaDetector, species classifiers, and demo notebooks |
| microsoft/SPARROW | Solar-Powered Acoustic and Remote Recording Observation Watch — the AI-enabled edge device that runs MegaDetector in the field |
| microsoft/MegaDetector-Acoustic | Bioacoustic models for audio-based wildlife monitoring |
| microsoft/MegaDetector-Overhead | Point-based detection models for overhead and aerial imagery |
| SPARROW Studio | The desktop application that wraps it all in a graphical interface |
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If you have any questions regarding MegaDetector and PyTorch-Wildlife, please email us or join us in our discord channel:
