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Frequently Asked Questions

What is MegaDetector?

MegaDetector is the Microsoft AI for Good Lab's open-source model for finding animals, people, and vehicles in camera-trap images. It draws a bounding box around each detected object and gives it a confidence score between 0 and 1.

It is a detector, not a classifier. It tells you something is there, not which species. That is a deliberate choice: one detector generalizes across ecosystems far better than a species classifier, which is usually region-specific. For species identification, pair MegaDetector with a downstream classifier.

What does MegaDetector detect?

MegaDetector detects three categories:

Category Examples
Animal Mammals, birds, reptiles, insects, fish, any wildlife
Person Researchers, poachers, tourists
Vehicle Cars, trucks, ATVs

It does not identify species. An image containing a lion and a zebra returns two "animal" detections, not "lion" and "zebra."

How do I filter blank camera-trap images?

Run MegaDetector across your image folder and keep only the frames that have an animal, person, or vehicle detection above your confidence threshold. Everything below the threshold is a blank you can set aside. Since most camera-trap datasets are mostly empty, this one pass clears the bulk of the review queue before any human looks at an image. The Camera-Trap AI page walks through the blank-filtering workflow step by step.

Do I need a GPU?

No, MegaDetector runs on CPU. A GPU with CUDA support gives a 10–50x speedup and is strongly recommended for large datasets (tens of thousands of images or more), but is not required.

The compact MegaDetectorV6 variants (YOLOv10-Compact, YOLOv9-Compact) are specifically designed for low-budget devices and edge hardware like the SPARROW field unit.

MegaDetector V6 vs V5: what's the difference?

MegaDetectorV5 MegaDetectorV6
Architecture YOLOv5 YOLOv9, YOLOv10, RT-DETR (multiple variants)
Parameters (compact) 139.9M 2.3M (YOLOv10-Compact, 2% of V5)
License MIT MIT
Status Legacy, still usable for existing workflows Current release, recommended for new projects
Weights Available on archive branch Download automatically via PyTorch-Wildlife

Recommendation: Use MegaDetectorV6 for new projects. MegaDetectorV5 weights remain available on the archive branch for projects already built around them.

[!TIP] For detailed model variants and benchmark results, see the Model Zoo.

How accurate is MegaDetector?

Accuracy depends on the dataset and confidence threshold. MegaDetectorV6 compact variants achieve comparable accuracy to V5 at 2% of the parameter count on the AI for Good Lab's validation datasets.

In practice, most deployments use a confidence threshold of 0.15–0.3 for the "animal" category, which provides high recall (few missed animals) at the cost of some false positives on vegetation and lighting artifacts. Setting the threshold higher reduces false positives but risks missing low-confidence true detections.

MegaDetector generalizes well across ecosystems because it was trained on a large and geographically diverse dataset. Performance is typically strongest on large mammals and degrades on very small or camouflaged animals.

What confidence threshold should I use?

A starting value of 0.15–0.3 on the animal category works for most datasets. That range favors recall, so you keep most true animals while accepting some false positives on moving vegetation and lighting artifacts. Raise it when a clean set matters more than catching every animal; lower it when a missed animal costs more than a few extra frames to review. When you move to a new deployment, check recall against a small labeled set first.

What species does MegaDetector support?

All of them, with caveats. MegaDetector detects the presence of an animal, not the species. It has been deployed on projects involving African savanna megafauna, North American forest species, European ungulates, tropical insects, and marine wildlife.

Performance varies. Large mammals in open habitat are reliably detected. Very small animals, heavily camouflaged species, or unusual angles may have lower confidence scores. If you're working with an atypical dataset, we recommend testing on a labeled sample before full deployment.

How do I run MegaDetector?

The quickest path is three lines of Python:

pip install PytorchWildlife

from PytorchWildlife.models import detection as pw_detection
model = pw_detection.MegaDetectorV6()
results = model.batch_image_detection("path/to/image_folder/")

For a graphical interface, use SPARROW Studio or the Hugging Face demo.

See Installation for full setup instructions.

What is the megadetector CLI?

megadetector is a command-line tool registered when you install the repository from source (pip install -e .). It runs the same V6 models without writing any Python:

megadetector detect --input ./images/ --output results.json --model MDV6-yolov10-e

It also exposes train, validate, and inference for fine-tuning. The full flag list and the supported --model values are in the CLI reference.

Can I fine-tune MegaDetector on my own data?

Yes. MegaDetectorV6 ships a training pipeline for fine-tuning the YOLOv9, YOLOv10, and RT-DETR variants on your own labeled camera-trap images, which helps when your species or habitats are thin in the base model. The fine-tuning guide covers the data layout, the configuration file, and the megadetector train command from start to finish.

What does MegaDetector output look like?

MegaDetector returns one record per image, a file path plus a list of detections, each carrying a category (animal, person, or vehicle), a confidence score from 0 to 1, and a bbox as [x1, y1, x2, y2] pixel coordinates. Detections below your threshold are dropped, and the JSON feeds straight into review tools or a species classifier. See the Output Format reference for the full schema.

What is the license?

MegaDetector is released under the MIT License. You can use it for any purpose, including commercial use, subject to the license terms.

The PyTorch-Wildlife framework that distributes MegaDetector is also MIT-licensed. Individual model weights may carry separate licenses, see the Model Zoo for per-model licensing information.

Where are the V5 weights?

MegaDetectorV5 weights are available on the archive branch of the microsoft/Biodiversity repository (formerly microsoft/CameraTraps). They load directly through PyTorch-Wildlife, so existing V5 workflows keep running without changes.

How do I cite MegaDetector?

A CITATION.cff is maintained in this repository for automated citation tools (Zenodo, GitHub's "Cite this repository" button).

For MegaDetector specifically, cite:

Beery, Morris, Yang (2019). Efficient Pipeline for Camera Trap Image Review. arXiv:1907.06772.

For the PyTorch-Wildlife framework:

Hernandez et al. (2024). Pytorch-Wildlife: A Collaborative Deep Learning Framework for Conservation. arXiv:2405.12930.

See Cite Us for full citation details and BibTeX.

Where do I get help?