Search and view compatibility
A summary of search and view capabilities available in Catalog by data type.
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.
Supported search filters (basic)
To learn more about the supported filters, see Filters.
Asset type | Annotation | Dataset | Metadata | Project | Media attribute | Batch | Data row |
---|---|---|---|---|---|---|---|
Image | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ |
Video | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ |
Text | ✔ | ✔ | ✔ | ✔ | Only mime type | ✔ | ✔ |
HTML | ✔ | ✔ | ✔ | ✔ | Only mime type | ✔ | ✔ |
Document | ✔ | ✔ | ✔ | ✔ | Only mime type | ✔ | ✔ |
Tiled imagery | ✔ | ✔ | ✔ | ✔ | Only mime type | ✔ | ✔ |
Audio | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ |
Conversational | ✔ | ✔ | ✔ | ✔ | Only mime type | ✔ | ✔ |
Enrich your data with custom metadata
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.
Supported search filters (advanced)
Asset type | Find text | Natural language |
---|---|---|
Image | - | ✔* |
Video | - | ✔ (beta, add-on feature) |
Text | ✔ | ✔** |
HTML | ✔ | ✔** |
Document | ✔ | ✔*** |
Tiled imagery | - | ✔* |
Audio | - | - |
Conversational | ✔ | ✔** |
* 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).
Similarity (embeddings)
Asset type | Off-the-shelf embeddings | Custom embeddings |
---|---|---|
Image | CLIP-ViT-B-32 (512 dimensions) | 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. |
Text | all-mpnet-base-v2 (768 dimensions) | Up to 2048 dimensions per embedding; up to 100 custom embeddings per workspace. |
HTML | all-mpnet-base-v2 (768 dimensions) | Up to 2048 dimensions per embedding; up to 100 custom embeddings per workspace. |
Document | CLIP-ViT-B-32 (512 dimensions) and all-mpnet-base-v2 (768 dimensions) | Up to 2048 dimensions per embedding; up to 100 custom embeddings per workspace. |
Tiled imagery | CLIP-ViT-B-32 (512 dimensions) | Up to 2048 dimensions per embedding; up to 100 custom embeddings per workspace. |
Audio | Audio is transcribed to text. all-mpnet-base-v2 (768 dimensions) | Up to 2048 dimensions per embedding; up to 100 custom embeddings per workspace. |
Conversational | all-mpnet-base-v2 (768 dimensions) | Up to 2048 dimensions per embedding; up to 100 custom embeddings per workspace. |
Enhance similarity search with custom embeddings
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.
Options for viewing your data
Asset type | Thumbnail view | Detail view | Annotations overlay (thumbnail) | Annotations overlay (detail) |
---|---|---|---|---|
Image | ✔ | ✔ | ✔ | ✔ |
Video | ✔ | ✔ | - | Classifications only |
Text | ✔ | ✔ | ✔ | ✔ |
HTML | ✔ | ✔ | - | ✔ |
Document | ✔ | ✔ | Classifications only | Classifications only |
Tiled imagery | ✔ | ✔ | ✔ | ✔ |
Audio | - | - | - | ✔ |
Conversational | ✔ | ✔ | ✔ | ✔ |
Updated about 1 month ago