Global AI chat room · 17 online now Join now
L
MODEL Listed

LocateAnything-3B

LocateAnything-3B is a specialized vision-language model from NVIDIA designed specifically for high-precision spatial grounding. Unlike general-purpose multimodal models that provide broad descriptions, this 3B-parameter architecture focuses on the 'where' as much as the 'what.' It excels at mapping natural language queries to specific bounding boxes within an image, making it a critical tool for developers building object detection pipelines, visual search engines, or automated robotic vision systems. For engineers looking to integrate grounding capabilities without the massive computational overhead of larger models, its 3B footprint offers a highly efficient middle ground between lightweight detectors and heavy LLMs. It is particularly useful for zero-shot object localization tasks where pre-defined labels are insufficient and you need to detect arbitrary objects via text prompts. Integration is straightforward via Hugging Face, making it a plug-and-play option for RAG-based vision workflows or complex scene understanding applications.

nvidiaimage text to text
01 / MODEL CARD

Model card

LocateAnything-3B is a specialized vision-language model from NVIDIA designed specifically for high-precision spatial grounding. Unlike general-purpose multimodal models that provide broad descriptions, this 3B-parameter architecture focuses on the 'where' as much as the 'what.' It excels at mapping natural language queries to specific bounding boxes within an image, making it a critical tool for developers building object detection pipelines, visual search engines, or automated robotic vision systems. For engineers looking to integrate grounding capabilities without the massive computational overhead of larger models, its 3B footprint offers a highly efficient middle ground between lightweight detectors and heavy LLMs. It is particularly useful for zero-shot object localization tasks where pre-defined labels are insufficient and you need to detect arbitrary objects via text prompts. Integration is straightforward via Hugging Face, making it a plug-and-play option for RAG-based vision workflows or complex scene understanding applications.

Model typeimage text to text
Providernvidia
Licenseother
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/nvidia/LocateAnything-3B
View model source
Version informationUse the source repository for the latest version
—
03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: nvidia/LocateAnything-3B
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model nvidia/LocateAnything-3B
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model nvidia/LocateAnything-3B README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('nvidia/LocateAnything-3B')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/nvidia/LocateAnything-3B.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/nvidia/LocateAnything-3B.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

Open source page
Email