Machine learning

Gretel Synthetic Safety Alignment Dataset

A comprehensive dataset designed for aligning language models with safety and ethical guidelines. Contains 8,361 curated triplets of prompts, responses, and safe responses across various risk categories. Each entry includes safety scores, judge reasoning, and harm probability assessments, making it valuable for model alignment, testing, and benchmarking.

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

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Apache License 2.0 (https://www.apache.org/licenses/LICENSE-2.0)

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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

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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