PyTorch-Wildlife and MegaDetector

PyTorch-Wildlife is the Microsoft AI for Good Lab's framework for building, sharing and running conservation models; the lab's Biodiversity hub gathers it with MegaDetector, the detector most people know it for, plus MegaDetector-Acoustic for sound and MegaDetector-Overhead for drone and aerial imagery, and a model zoo of species classifiers. MegaDetector is an animal detector, not a species classifier: it draws boxes around animals, people and vehicles with a confidence score, and researchers use it mainly to discard empty camera-trap frames before review. Versions 1 to 5 were developed by Dan Morris at Microsoft; version 6 uses YOLOv9, YOLOv10 and RT-DETR architectures.

MegaDetector-Overhead extends the same approach to survey imagery from drones, where targets are small and altitude and perspective vary; it ships the OWL model family (formerly Overhead Wildlife Locator), including a caribou-specific checkpoint. The models can also be loaded through no-code front ends such as AddaxAI, SPARROW Studio and a Hugging Face demo.

Everything is installed locally: pip install PytorchWildlife gives a Python API and a command line that write JSON detections per image. Code is MIT-licensed; weights carry their own licences per variant, and the overhead model and data release is CC BY-NC-SA 4.0. Status: operational and maintained.