Featured Dataset
SA-V Dataset
SA-V is a dataset designed for training general-purpose object segmentation models from open world videos. The dataset was introduced in our paper “Segment Anything 2”.
Datasets
FACET Dataset
FACET is a comprehensive benchmark dataset designed for measuring or evaluating the robustness and algorithmic fairness of AI and machine-learning vision models for protected groups.
EgoTV Dataset
A benchmark and dataset for systematic investigation of vision-language models on compositional, causal (e.g., effect of actions), and temporal (e.g., action ordering) reasoning in egocentric settings.
MMCSG Dataset
The MMCSG (Multi-Modal Conversations in Smart Glasses) dataset comprises two-sided conversations recorded using Aria glasses, featuring multi-modal data such as multi-channel audio, video, accelerometer, and gyroscope measurements.
Speech Fairness Dataset
Designed for training general-purpose object segmentation models from open world images.
Casual Conversations V2
For evaluating computer vision, audio and speech models for accuracy across a diverse set of ages, genders, language/dialects, geographies, disabilities, and more.
Casual Conversations
For evaluating computer vision and audio models for accuracy across a diverse set of age, genders, apparent skin tones and ambient lighting conditions.
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Our open-sourced libraries and models for those taking our AI learnings further through software and app development
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Our demos for anyone wanting to experience our latest research breakthroughs first hand
System cards
Multiple machine learning (ML) models that help people understand the intention, impact and limitations of our AI systems
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Our library of published papers to learn about our latest AI breakthroughs and innovations
Foundational models
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Foundational models