OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Models

Visionreasoner

VisionReasoner is an open-source Python research project for a vision-language model that combines visual reasoning with image perception tasks. Its framework uses a task router to map requests to detection, segmentation, counting or visual question answering, alongside a reasoning module and an optional segmentation module.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Visionreasoner — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want to examine or run the repository's documented local inference examples for image counting, visual question answering, detection or segmentation.

Who it is for

  • Researchers studying unified visual perception and reasoning.
  • Developers exploring local image question answering, object counting, detection or segmentation with a vision-language model.

Documented uses

  • Count objects in an image.
  • Ask a question about image contents and receive a visual answer.
  • Request object detection or segmentation for a specified image.
  • Explore a shared model framework spanning multiple visual task types.
Before you choose

Requirements & limits

The documented inference workflow requires installing the Python environment and downloading model files from Hugging Face. The repository identifies its license as Apache-2.0.

  • Inference is documented through a Python script rather than a hosted service or API.
  • The workflow requires both the VisionReasoner-7B and TaskRouter-1.5B model repositories.
  • The framework groups supported requests into detection, segmentation, counting and visual question answering.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up and run a documented image inference example

Install the repository, obtain its two model repositories and run the default counting inference or a documented image query.

Before you start

  • Python 3.12 and Conda
  • PyTorch, torchvision and packages in the repository's requirements file
  • Git LFS and access to the VisionReasoner-7B and TaskRouter-1.5B repositories on Hugging Face

Steps

  1. Clone the VisionReasoner repository and create and activate a Conda environment with Python 3.12.

  2. Install PyTorch, torchvision and the packages listed in requirements.txt.

  3. Create a pretrained_models directory, initialize Git LFS, and clone the VisionReasoner-7B and TaskRouter-1.5B model repositories there.

  4. Run python vision_reasoner/inference.py for the default counting task, or provide --image_path and --query as shown in the README for an image-specific request.

Expected outcome

The README describes a command-line answer for the default counting task and a result_visualization.png output for its detection or segmentation examples.

Guide limitations

  • The default task is counting; image-specific examples require an image path and query.
  • The documented workflow depends on downloading both model repositories from Hugging Face.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
JIA-Lab-research/VisionReasoner · Official homepage title · retained in its source language
How we review sourcesSuggest a correction