Instead of writing queries with precise syntax, you can just type what you’re looking for, such as “a photo of an emergency vehicle.” If a 2D image is matched with a corresponding point cloud, you can explore that point cloud and its tags to see if it suits the “edge case” you’re investigating to train your model to handle it. Join us for an in-depth tech talk where we show you how to leverage a novel approach to data discovery using Nucleus.
Key takeaways from this talk:
- Don’t bother to learn syntax: filter your dataset with plain old words
- Slice and dice your 3D data with only a text search
- Speed up your dataset creation pipeline, even with challenging data types like 3D
- See how you can unearth even unlabeled data with natural language
From 3D to Natural Language Query in Nucleus with CLIP
Nucleus is already an incredibly accessible way to curate your training data, and today, instead of writing queries with precise syntax, you can just type what you’re looking for, such as “a photo of an emergency vehicle.” If a 2D image is matched with a corresponding point cloud, you can explore that point cloud and its tags to see if it suits the “edge case” you’re investigating, in order to train your model to handle it.
