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Toolbox

Explore popular pretrained & foundation models, useful scripts/libraries, and datasets that you can leverage for your next ML project. Learn about their features, how to use them effectively, and see examples of them in action.

INQUIRE

Computer vision

Zero-shot learning

Multimodal learning

INQUIRE is a benchmark for evaluating text-to-image retrieval models on expert-level ecological queries. Built on a 5-million-image subset of iNaturalist 2024 (iNat24), it…

CIFAR Dataset

Computer vision

Image classification

CIFAR-10 (Canadian Institute for Advanced Research) is a widely used dataset consisting of 60,000 color images at a resolution of 32×32 pixels, spanning 10 distinct classes…

Aidan O’Brien

UW Generative AI Services & Policies

Microsoft Copilot

The University of Wisconsin–‍Madison is committed to responsibly harnessing the power of generative artificial intelligence to enhance teaching, learning, research and…

NotebookLM: A GenAI Summarization Tool

NotebookLM is a generative AI tool designed to assist with understanding dense, complex texts, including research papers in machine learning and AI. Developed by Google Labs…

AI Fairness 360 (AIF360)

Bias mitigation

Computer vision

AI Fairness 360 (AIF360) is a scikit-learn-compatible open-source Python library designed to detect and mitigate bias in machine learning models. Its compatibility with…

PyTorch-OOD

Anomaly detection

Novelty detection

Open-set recognition

Computer vision

PyTorch-OOD is an open-source Python library designed for out-of-distribution (OOD) detection and related tasks like anomaly detection, novelty detection, and open-set…

Intro to AWS SageMaker for Predictive ML/AI

This introductory AWS SageMaker workshop teaches core workflows for running predictive ML/AI models in AWS SageMaker, an AWS-managed machine learning environment.…

Kornia

Model exploration

Computer vision

Image preprocessing

Image segmentation

Kornia is a differentiable library that allows classical computer vision to be integrated into deep learning models. Developed by E. Riba, D. Mishkin, D. Ponsa, E. Rublee…

Leaf Vein Dataset (LVD2021)

Plant phenotyping

Image segmentation

Computer vision

The Leaf Vein Dataset 2021 (LVD2021), introduced in the paper Leaf vein segmentation with self-supervision, contains high-resolution images of leaves with pixel-wise…

XGBoost: Tree-Based Gradient Boosting for Tabular Data

XGBoost, or eXtreme Gradient Boosting, is a machine learning algorithm built upon the foundation of decision trees, extending their power through boosting. Originally…

Project Gutenberg: Text & Audio Books

Multimodal data

The Project Gutenberg dataset contains text from thousands of books, spanning a variety of genres and styles, and in some cases, corresponding audiobooks. Researchers and…

U-Net: Convolutional Networks for Biomedical Image Segmentation

Medical imaging

Image segmentation

U-Net is a convolutional neural network architecture designed for biomedical image segmentation. Introduced in 2015 by Ronneberger and colleagues in the paper, “U-Net…

MONAI: Medical Open Network for AI

Medical imaging

Model exploration

MONAI (Medical Open Network for AI) is an open-source, community-supported framework for deep learning in healthcare imaging. Built on top of PyTorch, it provides…

Center for High Throughput Computing (CHTC)

Established in 2006, the Center for High Throughput Computing (CHTC) is committed to democratizing access to powerful computing resources across all research domains. From…