Owlv 2 Base Patch 16 Ensemble
Owlv 2 Base Patch 16 Ensemble is a zero-shot text-conditioned object detection model developed by Google, utilizing a CLIP backbone with a ViT-B/16 Transformer architecture as its image encoder and a masked self-attention Transformer as its text encoder. It is genuinely best at enabling researchers to explore zero-shot, text-conditioned object detection, and a notable technical trait is its use of a bipartite matching loss to fine-tune the model end-to-end on standard detection datasets.
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Parameters
155M
Technical Specifications
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Estimated GPU requirements for Owlv 2 Base Patch 16 Ensemble
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about Owlv 2 Base Patch 16 Ensemble
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Google has not published a context window for Owlv 2 Base Patch 16 Ensemble, so the catalog shows it as not available rather than estimating one.
Yes. Owlv 2 Base Patch 16 Ensemble is an open-weight model released under the Apache 2.0 license, so the weights can be downloaded and self-hosted within that license's terms.
Owlv 2 Base Patch 16 Ensemble accepts Image and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for Owlv 2 Base Patch 16 Ensemble is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; Owlv 2 Base Patch 16 Ensemble is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 0.5 GB at INT8 for the weights and a default context, from the catalog's 155M parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Aug 28, 2026
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