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.
MegaDetector is one project in the microsoft/Biodiversity ecosystem and is invoked through the PyTorch-Wildlife framework. It is free, open-source, and available under permissive licenses.
pip install PytorchWildlifefrom PytorchWildlife.models import detection as pw_detection
# Load MegaDetector V6 (weights download automatically)
model = pw_detection.MegaDetectorV6()
# Run on a single image
results = model.single_image_detection("path/to/camera_trap_image.jpg")
# Run on a folder of images
results = model.batch_image_detection("path/to/image_folder/")That's it. Three lines to detect animals in your camera-trap images.
Need the local megadetector CLI or fine-tuning? See Install from source and Fine-Tuning below.
Try it without installing anything:
- Hugging Face demo — upload images in your browser
- Google Colab notebook — free cloud GPU
Camera traps generate millions of images, and the vast majority are empty frames triggered by wind or vegetation. Manually reviewing them is one of the biggest bottlenecks in wildlife research.
MegaDetector solves this. It scans your images and draws bounding boxes around any animal — mammals, birds, reptiles, insects, and more. Each detection has a confidence score between 0 and 1. You set a threshold (typically 0.15–0.3), and anything above it is flagged. The output lets you sort images, filter blanks, or feed detected animals into a species classifier.
MegaDetector is intentionally a detector, not a classifier. "Animal vs. background" generalizes across ecosystems far better than species identification. For species classification, pair MegaDetector with a downstream classifier — see Species Classification below.
The latest release focuses on efficiency and modern architectures — SMALLER, FASTER, BETTER.
- 50x smaller: The compact YOLOv10 variant has 2.3M parameters — 2% of MegaDetector V5's 139.9M — with comparable accuracy
- Multiple architectures: YOLOv9, YOLOv10, RT-DETR — pick the one that fits your hardware
- Ongoing fine-tuning: V6 models are continuously fine-tuned on newly collected public and private data to further improve generalization
| Model | Params | Animal Recall | mAP50 | License |
|---|---|---|---|---|
| MDV6-yolov10-e | 29.5M | 82.8% | 92.8% | AGPL-3.0 |
| MDV6-yolov9-e | 58.1M | 82.1% | 88.6% | AGPL-3.0 |
| MDV6-rtdetr-c | 31.9M | 81.6% | 89.9% | AGPL-3.0 |
| MDV6-yolov9-c | 25.5M | 78.4% | 87.9% | AGPL-3.0 |
| MDV6-yolov10-c | 2.3M | 76.8% | 87.2% | AGPL-3.0 |
Model names are standardized into MDV6-Compact and MDV6-Extra for the two model sizes within each architecture, reducing confusion across variants.
Which should I use?
- Best accuracy: MDV6-yolov10-e (82.8% recall, AGPL-3.0)
- Best for laptops/edge: MDV6-yolov10-c (2.3M params, runs on CPU)
- Best balance: MDV6-yolov10-e (29.5M params, 82.8% recall)
# Load a specific variant
from PytorchWildlife.models import detection as pw_detection
model = pw_detection.MegaDetectorV6(version="MDV6-yolov10-e")pip install PytorchWildlifeRequirements:
- Python 3.8+ (3.10+ recommended)
- Optional: NVIDIA GPU with CUDA for 10–50x speedup
Conda users:
conda create -n megadetector python=3.10 -y
conda activate megadetector
pip install PytorchWildlifeGPU setup (if PyTorch didn't install with CUDA):
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121For the local megadetector command-line tool and to fine-tune V6 weights on
your own dataset, install this repository in editable mode:
git clone https://github.com/microsoft/MegaDetector
cd MegaDetector
pip install -e .This installs the megadetector_core Python package and exposes the
megadetector shell command (megadetector detect|train|validate|inference).
The pyproject.toml covers the full dependency set — no separate
requirements.txt is needed.
Full installation guide: microsoft.github.io/MegaDetector/installation/
MegaDetector finds animals — it doesn't identify species. For species ID, run a two-stage pipeline:
- MegaDetector detects and localizes animals
- A species classifier identifies the species in each crop
import supervision as sv
from PytorchWildlife.models import detection as pw_detection
from PytorchWildlife.models import classification as pw_classification
detector = pw_detection.MegaDetectorV6()
classifier = pw_classification.AI4GAmazonRainforest()
det_results = detector.single_image_detection("image.jpg")
for xyxy in det_results["detections"].xyxy:
cropped = sv.crop_image(image=det_results["img"], xyxy=xyxy)
cls_result = classifier.single_image_classification(cropped)
print(f"Species: {cls_result['prediction']}")Available classifiers in PyTorch-Wildlife:
| Classifier | Region | Classes | License |
|---|---|---|---|
| AI4G Amazon Rainforest | Amazon basin | 36 | MIT |
| AI4G Snapshot Serengeti | East Africa | 10 | MIT |
| AI4G Opossum | Galapagos | 2 | MIT |
| DeepFaune Classifier | Europe | 34 | CC BY-SA 4.0 |
| DFNE | Northeastern U.S. | 23 | CC0 1.0 |
Google's SpeciesNet is also designed to work with MegaDetector output and covers ~2,000 species globally.
SPARROW Studio is a unified desktop application by the AI for Good Lab built on PyTorch-Wildlife:
- Run MegaDetector and species classifiers through a graphical interface
- Manage camera-trap data locally or in the cloud
- Annotate, analyze, and visualize detection results
- Supports bioacoustics and overhead wildlife imagery
Windows installer: Download from Zenodo (signed). Mac and Linux builds in progress.
AddaxAI is a third-party desktop tool for running MegaDetector with batch processing, annotation, and results visualization. Windows, macOS, and Linux.
Upload images and run MegaDetector in your browser — no installation required.
MegaDetector is one model in a larger open-source ecosystem from the AI for Good Lab. Each project lives in its own repository, with the microsoft/Biodiversity umbrella tying them together.
| Repo | Purpose |
|---|---|
| microsoft/Biodiversity | The umbrella repository — documentation hub for the AI for Good Lab's biodiversity work |
| microsoft/PytorchWildlife | The collaborative deep learning framework that hosts MegaDetector, species classifiers (AI4G Amazon Rainforest, AI4G Snapshot Serengeti, DeepFaune), HerdNet, PW-Engine (a Rust-based inference core), and demo notebooks |
| microsoft/SPARROW | Solar-Powered Acoustic and Remote Recording Observation Watch — the AI-enabled edge device that runs MegaDetector in remote field locations |
| 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 |
MegaDetector is the entry point for most users. SPARROW Studio is the full platform. SPARROW is the field-hardened edge device.
MegaDetector is used by 80+ organizations across government agencies, universities, NGOs, and technology companies worldwide. A selection:
Government: Arizona DEQ, Idaho Fish & Game, Oregon DFW, Michigan DNR, Parks Canada (Banff), U.S. Fish & Wildlife Service (multiple refuges), National Park Service, Canadian Wildlife Service
Conservation NGOs: The Nature Conservancy, Island Conservation, Wildlife Protection Solutions, Australian Wildlife Conservancy, RSPB, CPAWS, SPEA, Felidae Conservation Fund
Universities: UCLA, University of Washington, UBC, University of Florida, University of Idaho, UNSW Sydney, Macquarie University, University of Saskatchewan, Washington State University, Bangor University, Tel Aviv University, TU Berlin, Wildlife Institute of India
Museums & Zoos: American Museum of Natural History, Smithsonian, Woodland Park Zoo, San Diego Zoo Wildlife Alliance, Taronga Conservation Society
Platforms: TrapTagger, WildTrax, Camelot, Animl, Wildlife Observer Network, OCAPI, WildePod
See the full list in the PyTorch-Wildlife repository.
| Hardware | Model | Approximate Speed |
|---|---|---|
| NVIDIA RTX 3090 | MDV6-yolov10-c (2.3M) | ~100–200 images/sec |
| NVIDIA RTX 3090 | MDV6-yolov10-e (29.5M) | ~30–60 images/sec |
| Modern CPU (no GPU) | MDV6-yolov10-c (2.3M) | ~2–5 images/sec |
| Google Colab (free GPU) | Any V6 variant | ~10–50 images/sec |
At 50 images/sec on a GPU, one million images takes about 5.5 hours. On CPU with the compact model, about 3.9 days.
Every V6 variant is faster than V5 (139.9M params). The compact V6 is 2% the size.
MegaDetector V6 ships with a fine-tuning pipeline (built on the ultralytics framework) so you can adapt a V6 model to your own camera-trap dataset. Fine-tuning is useful when the off-the-shelf V6 models miss animals that look different from the training distribution — for example, an under-represented species, a specific environment (forest canopy, snow, night-vision IR), or a new sensor.
Quick path:
# 1. Clone and install in editable mode (from a fresh venv or conda env).
git clone https://github.com/microsoft/MegaDetector
cd MegaDetector
pip install -e .
# 2. Copy the reference config and edit `data:` to point at your dataset YAML.
cp examples/config_training.yaml ./config.yaml
# 3. Train, validate, and run inference with the same CLI.
megadetector train --config ./config.yaml
megadetector validate --config ./config.yaml
megadetector inference --config ./config.yamlSupported fine-tuning model variants:
MDV6-yolov9-c— compact YOLOv9MDV6-yolov9-e— extra-large YOLOv9MDV6-yolov10-c— compact YOLOv10 (2.3M params)MDV6-yolov10-e— extra-large YOLOv10MDV6-rtdetr-c— compact RT-DETR
Training outputs (weights, plots, metrics) land under ./runs/ keyed by the
exp_name field in your config. Fine-tuned .pt weights can be loaded back
into MegaDetectorV6(weights="path/to/best.pt") for inference.
Full reference: docs/training_guide.md covers data layout, the full config schema, conda environment setup, and the Python API equivalents of each CLI subcommand.
| Version | Year | Architecture | Params | Notes |
|---|---|---|---|---|
| V6.0 (current) | 2024 | YOLOv9/v10, RT-DETR | 2.3M–58.1M | YOLOv9/v10 + RT-DETR variants (AGPL-3.0) |
| V5.0 | 2022 | YOLOv5 | 139.9M | Two sub-versions (5a, 5b) |
| V4.1 | 2020 | Faster R-CNN | — | Added vehicle class |
| V3 | 2019 | Faster R-CNN | — | Added human class |
| V2 | 2018 | Faster R-CNN | — | First public release |
For MegaDetectorV5 model weights and earlier versions, see the archive branch of the Biodiversity repository (formerly microsoft/CameraTraps).
The original MegaDetector repository was primarily developed by Dan Morris during his time at Microsoft. Dan continues to actively maintain a forked version at agentmorris/MegaDetector, which remains a valuable resource for the community.
At the core of our mission is the desire to create a harmonious space where conservation scientists from all over the globe can unite — to share, grow, and use datasets and deep learning architectures for wildlife conservation. We've been inspired by the potential and capabilities of MegaDetector, and we deeply value its contributions to the community. We remain committed to supporting, maintaining, and developing MegaDetector — ensuring its continued relevance, expansion, and utility.
PyTorch-Wildlife (the framework):
@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},
}MegaDetector (the original model):
@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},
}You can also use GitHub's "Cite this repository" button in the sidebar.
MegaDetector's source code lives in this repository. To contribute code, file issues, or submit pull requests, head to microsoft/MegaDetector/issues.
For framework-level changes (PyTorch-Wildlife API, classifiers, demo notebooks), see microsoft/PytorchWildlife. For ecosystem-wide questions, see the microsoft/Biodiversity umbrella.
For questions, feature requests, or to report how MegaDetector worked on your data:
- Email: zhongqimiao@microsoft.com
- Discord:
- GitHub Discussions: microsoft/Biodiversity/discussions
The MegaDetector code is released under the MIT License. Individual model weights documented in this repository are released under AGPL-3.0 — see the Model Variants table for details.