Use this file to discover all available pages before exploring further.
Below are the supported search filters that Catalog supports out of the box. You can extend the usability of these core offerings by uploading your own custom metadata and embeddings, then combining them with the supported filters below to accomplish your search and curation objectives.
As noted above, Labelbox supports metadata on any data type. To learn how to use custom metadata and these Catalog filters to enrich your data, read this blog post on how to make your data queryable using foundation models.
* Uses the off-the-shelf CLIP-ViT-B-32 vision model (512 dimensions).** Uses the off-the-shelf all-mpnet-base-v2 text model (768 dimensions), based on the first 64k characters.*** Users can pick between off-the-shelf CLIP-ViT-B-32 vision model (512 dimensions) and all-mpnet-base-v2 text model (768 dimensions, based on the first 64k characters).
Up to 2048 dimensions per embedding; up to 100 custom embeddings per workspace.
Video
Google Gemini Pro Vision . First two (2) minutes of content is embedded. Audio signal is not used currently. This is a paid add-on feature available upon request.
Up to 2048 dimensions per embedding; up to 100 custom embeddings per workspace.
As noted above, Labelbox supports custom embeddings on any data type. Powerful embeddings can be generated using foundational models and easily uploaded to Labelbox. You can then use these embeddings in combination with any of the above filters to accomplish your data search goals.For an example of how to get started, check out this guide on how to generate custom embeddings using foundational models and upload them to Labelbox.
