Machine learning

Image classification - fast.ai datasets

Some of the most important datasets for image classification research, including CIFAR 10 and 100, Caltech 101, MNIST, Food-101, Oxford-102-Flowers, Oxford-IIIT-Pets, and Stanford-Cars. This is part of the fast.ai datasets collection hosted by AWS for convenience of fast.ai students. See documentation link for citation and license details for each dataset.

Listed for discovery. Access is provided by the original publisher under its terms.

Data coverage

Access & permitted use

Review the publisher’s documentation for availability, eligibility and permitted uses, including commercial analysis, model training and redistribution.

Varies by dataset - see documentation link

Need help sourcing data?

Tell us your required fields, population, geography and time period. We can review suitable sources and licensing options.

Request sourcing help

Related data sources

Machine learningOpen corpus

Common Crawl Web Corpus

Publicly accessible historical web crawl archives containing WARC and related data, with periodic releases that support web-scale text analysis.

Common Crawl Foundation

Machine learningExternal source; review access terms

3DCoMPaT: Composition of Materials on Parts of 3D Things

3D CoMPaT is a richly annotated large-scale dataset of rendered compositions of Materials on Parts of thousands of unique 3D Models. This dataset primarily focuses on stylizing 3D shapes at part-level with compatible materials. Each object with the applied part-material compositions is rendered from four equally spaced views as well as four randomized views. We introduce a new task, called Grounded CoMPaT Recognition (GCR), to collectively recognize and ground compositions of materials on parts of 3D objects. We present two variations of this task and adapt state-of-art 2D/3D deep learning methods to solve the problem as...

Vision-CAIR, CEMSE, KAUST

Machine learningExternal source; review access terms

A2D2: Audi Autonomous Driving Dataset

An open multi-sensor dataset for autonomous driving research. This dataset comprises semantically segmented images, semantic point clouds, and 3D bounding boxes. In addition, it contains unlabelled 360 degree camera images, lidar, and bus data for three sequences. The dataset supports learning tasks such as semantic segmentation, 3D object detection, SLAM, and depth estimation. We hope this dataset will further facilitate active research and development in AI, computer vision, and robotics for autonomous driving.

Mentar Mahmudi, Jakob Geyer, Lorenz Hauswald