Discover an index of datasets, SDKs, APIs and open-source tools developed by Microsoft researchers and shared with the global academic community below. These experimental technologies—available through Azure AI Foundry Labs (opens in new tab)—offer a glimpse into the future of AI innovation.
At the core of our mission is the desire to create a harmonious space where conservation scientists from all over the globe can unite. Where they’re able to share, grow, use datasets and deep learning…
This project contains the training code for the Microsoft AI for Earth Species Classification API, along with the code for our API demo page (opens in new tab). This API classifies handheld photos of around 5000…
After developing an algorithm or machine learning model, researchers face the problem of deploying their model for others to consume, integrating it with data sources, securing its access, and keeping it current. Due to these…
These images and examples are meant to illustrate how to build containers for use in the AI for Earth API system.
Multi-species bioacoustic classification using deep learning algorithms. With audio recordings collected from rainforests in Puerto Rico, we build a deep learning model that combines transfer learning and pseudo-labeling as a data augmentation technique to: 1)…
This is a deep learning toolbox to train models on medical images (or more generally, 3D images). It integrates seamlessly with cloud computing in Azure.
This repository contains the code and models necessary to replicate the results of our recent paper: Denoised Smoothing: A Provable Defense for Pretrained Classifiers Hadi Salman, Mingjie Sun, Greg Yang, Ashish Kapoor, J. Zico Kolter…
This AI for Earth project, in collaboration with the Wildlife Conservation Society Colombia (WCS Colombia (opens in new tab)) was developed to create up-to-date land cover maps of the Orinoquía (opens in new tab) region…
