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title: README
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hyper³labs (pronounced Hypercube Labs) builds embedding models and retrieval infrastructure across text and vision. We also build open-source tools for evaluating and understanding embedding systems.
We are especially interested in how hierarchy and non-Euclidean geometry affect retrieval, and in the failures that aggregate benchmark scores hide.
Models
- hyper3-clip-v1 is an open-weight vision-language embedding model for hierarchy-sensitive retrieval. It produces 512-dimensional embeddings and works with the Sentence Transformers interface.
Tools and demos
- HyperView is our open-source workbench for exploring embedding spaces and tracing retrieval failures back to real samples.
- ABO Catalog Explorer tests fine-grained retrieval on product catalogs.
- DeepFashion Search explores text-to-image search for garments and product attributes.
- Precision Region Search demonstrates referring-expression retrieval on RefCOCOg.
- Jaguar Re-ID examines whether re-identification models recognize an animal or its background.
- VisA Industrial Search explores anomaly and part retrieval in industrial imagery.
Datasets
- jaguar-re-id: individual jaguar identification from spot patterns.
- amazon-berkeley-objects: a mirror used for hierarchical product retrieval evaluation.
- jaguar-hyperview-demo: a smaller subset prepared for interactive analysis.
Research
Are We Recognizing the Jaguar or Its Background? A Diagnostic Framework for Jaguar Re-Identification
Antonio Rueda-Toicen, Robert Vava, Matin Mahmood. April 2026.