Encord unveils EBind method with world’s largest open multimodal dataset
Encord released a massive open multimodal dataset and introduced EBind, a training methodology that enables multimodal AI models to be trained on a single GPU within hours instead of days or weeks. The dataset spans text, images, audio, video, and 3D point clouds. Encord claims its method achieved performance rivaling models 17× larger by focusing on data quality and parameter efficiency. The release targets democratizing multimodal AI development, especially for smaller teams and startups.
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2 days ago
Encord unveils EBind method with world’s largest open multimodal dataset
Encord released a massive open multimodal dataset and introduced EBind, a training methodology that enables multimodal AI models to be trained on a single GPU within hours instead of days or weeks. The dataset spans text, images, audio, video, and 3D point clouds. Encord claims its method achieved performance rivaling models 17× larger by focusing on data quality and parameter efficiency. The release targets democratizing multimodal AI development, especially for smaller teams and startups.
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Encord unveils EBind method with world’s largest open multimodal dataset
2 days ago
1 min read
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Encord releases open dataset and EBind to train multimodal AI on one GPU in hours.
Encord released a massive open multimodal dataset and introduced EBind, a training methodology that enables multimodal AI models to be trained on a single GPU within hours instead of days or weeks. The dataset spans text, images, audio, video, and 3D point clouds. Encord claims its method achieved performance rivaling models 17× larger by focusing on data quality and parameter efficiency. The release targets democratizing multimodal AI development, especially for smaller teams and startups.
Encord released a massive open multimodal dataset and introduced EBind, a training methodology that enables multimodal AI models to be trained on a single GPU within hours instead of days or weeks. The dataset spans text, images, audio, video, and 3D point clouds. Encord claims its method achieved performance rivaling models 17× larger by focusing on data quality and parameter efficiency. The release targets democratizing multimodal AI development, especially for smaller teams and startups.