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LocateAnything-3B

LocateAnything-3B is a 3-billion-parameter vision-language model from NVIDIA that performs open-vocabulary object grounding and localization via natural language queries. It is fine-tuned from Qwen2.5-3B-Instruct using NVIDIA's Eagle visual encoder framework and targets conversational grounding workflows. The model supports referring expression comprehension and visual question answering with spatial outputs.

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From the model card

Fields below are copied from the tags and counters on the HuggingFace repository nvidia/LocateAnything-3B at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.

Publisher (HF namespace)
nvidia
Pipeline tag
image-text-to-text
Library
Transformers
Weight formats
safetensors
License tag
other — read the license file in the repo before relying on it
Lineage
Language tags
English (en)
Papers cited
arXiv:2605.27365, arXiv:2504.07491, arXiv:2109.10852, arXiv:2510.12798, arXiv:2303.05499, arXiv:1405.0312, arXiv:1908.03195, arXiv:2504.07981
Downloads (HF counter at last fetch)
384,700
Likes (HF counter at last fetch)
2,904
Model card
https://huggingface.co/nvidia/LocateAnything-3B

Use cases

  • Referring expression comprehension in complex scene images
  • Natural language-driven bounding box prediction for downstream pipelines
  • Grounding-augmented visual question answering tasks
  • Object detection research using open-vocabulary language specifications
  • Agentic visual reasoning requiring spatial object localization

Pros

  • Open-vocabulary grounding removes the need for fixed category lists at inference time
  • Built on Qwen2.5-3B-Instruct, providing a well-characterized instruction-following backbone
  • Multiple ArXiv papers referenced, giving traceable methodology for academic reproducibility
  • High community interest (2387 likes) indicates broad developer validation
  • Conversational interface allows iterative grounding refinement through dialogue

Cons

  • License is listed as 'other' — requires manual review before commercial use
  • Requires custom_code, meaning standard pipeline loading may fail without the model repo's code
  • 3B parameter scale may underperform larger grounding models on dense or small-object scenes
  • No standardized benchmark scores published in the HuggingFace model card
  • Eagle visual encoder is NVIDIA-specific; limited third-party tooling and documentation outside NVIDIA ecosystem

Tags

transformerssafetensorslocateanythingfeature-extractionnvidiaeaglevisionobject-detectiongroundingarxiv:2605.27365image-text-to-textconversationalcustom_codeenarxiv:2504.07491arxiv:2109.10852arxiv:2510.12798arxiv:2303.05499arxiv:1405.0312arxiv:1908.03195