Toolbox

The Toolbox section aggregates resources related to 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.

Kornia

Model exploration

Computer vision

Image processing

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

Gradient boosting

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

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. High…