Table 1.

Resources for machine and deep learning-based wildlife conservation.

Name	Description	URL
AIDE150	Tasks: Annotation; detection; classification; segmentation Free, open source, web-based, collaborative labeling platform specifically designed for large-scale ecological image analyses. Users can concurrently annotate up to billions of images with labels, points, bounding boxes, or pixel-wise segmentation masks. AIDE tightly integrates ML models through Active Learning151, where annotators are asked to provide inputs where the model is the least confident. AIDE further offers functionality to share and exchange trained ML models with other users of the system for collaborative annotation efforts in image campaigns across the globe.	GitHub
MegaDetector36	Tasks: Detection Free and open source detector based on deep learning hosted by Microsoft AI4Earth. The current model is trained with the TensorFlow Object Detection API using several hundred thousand camera trap images labeled with bounding boxes from a variety of ecosystems. The model identifies animals (not species-specific), humans, and vehicles, and is robust to novel sensor deployment locations and taxa not seen during training. Updates of the model, trained with additional data, are periodically released. Microsoft AI4Earth provides support to assist ecologists in using the model, including a public API for batch inference, and integration with commonly-used camera trap data management platforms such as TimeLapse and Camelot.	GitHub
Wildbook99	Tasks: Individual re-identification Wildbook blends structured wildlife research with artificial intelligence, community science, and computer vision to speed population analysis and develop new insights to help fight extinction. They host community-run individual re-identification systems and global data repositories for a broad and expanding set of species, including Grevy’s zebra, whale sharks, manta rays, and many more.	URL
Wildlife Insights41	Tasks: Filtering Large-scale platform for camera trap data management with computer vision in the backend. Currently open for whitelisted users, extensible via a waitlist. Wildlife Insights filters blank images and provides species identification for images that the computer vision model scores highly, allowing expert ecologists to focus on labeling only challenging images.	URL
DeepLabCut74	Tasks: Pose estimation and behavioral analysis Free and open-source pose estimation toolbox based on deep learning. Pre-trained models (for instance for primate faces and bodies, as well as quadruped) as well as a light-weight, real-time version are available.	GitHub
DeepPoseKit75	Tasks: Pose estimation and behavioral analysis Free and open-source pose estimation toolbox based on deep learning.	GitHub