---
license: cc-by-nc-4.0
task_categories:
  - image-segmentation
language:
  - en
tags:
  - reasoning
  - zero-shot
  - reinforcement-learning
  - multi-modal
  - VLM
size_categories:
  - n<1K
dataset_info:
  features:
  - name: image
    dtype: image
  - name: text
    dtype: string
  - name: mask
    sequence:
      sequence: bool
  - name: image_id
    dtype: string
  - name: ann_id
    dtype: string
  - name: img_height
    dtype: int64
  - name: img_width
    dtype: int64
  splits:
  - name: test
    num_bytes: 1666685613.0
    num_examples: 779
  download_size: 1235514015
  dataset_size: 1666685613.0
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---

# ReasonSeg Test Dataset

This repository contains the **ReasonSeg Test Dataset**, which serves as an evaluation benchmark for the paper [Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement](https://arxiv.org/abs/2503.06520).

**Code:** [https://github.com/dvlab-research/Seg-Zero](https://github.com/dvlab-research/Seg-Zero)

## Paper Abstract

Traditional methods for reasoning segmentation rely on supervised fine-tuning with categorical labels and simple descriptions, limiting its out-of-domain generalization and lacking explicit reasoning processes. To address these limitations, we propose Seg-Zero, a novel framework that demonstrates remarkable generalizability and derives explicit chain-of-thought reasoning through cognitive reinforcement. Seg-Zero introduces a decoupled architecture consisting of a reasoning model and a segmentation model. The reasoning model interprets user intentions, generates explicit reasoning chains, and produces positional prompts, which are subsequently used by the segmentation model to generate precious pixel-level masks. We design a sophisticated reward mechanism that integrates both format and accuracy rewards to effectively guide optimization directions. Trained exclusively via reinforcement learning with GRPO and without explicit reasoning data, Seg-Zero achieves robust zero-shot generalization and exhibits emergent test-time reasoning capabilities. Experiments show that Seg-Zero-7B achieves a zero-shot performance of 57.5 on the ReasonSeg benchmark, surpassing the prior LISA-7B by 18%. This significant improvement highlights Seg-Zero's ability to generalize across domains while presenting an explicit reasoning process.

## About Seg-Zero

Seg-Zero is a novel framework for reasoning segmentation that utilizes cognitive reinforcement to achieve remarkable generalizability and explicit chain-of-thought reasoning.

<div align=center>
<img width="98%" src="https://github.com/dvlab-research/Seg-Zero/raw/main/assets/overview.png"/>
</div>

Seg-Zero demonstrates the following features:
1.  Seg-Zero exhibits emergent test-time reasoning ability. It generates a reasoning chain before producing the final segmentation mask.
2.  Seg-Zero is trained exclusively using reinforcement learning, without any explicit supervised reasoning data.
3.  Compared to supervised fine-tuning, our Seg-Zero achieves superior performance on both in-domain and out-of-domain data.

### Model Pipeline

Seg-Zero employs a decoupled architecture, including a reasoning model and segmentation model. A sophisticated reward mechanism integrates both format and accuracy rewards.

<div align=center>
<img width="98%" src="https://github.com/dvlab-research/Seg-Zero/raw/main/assets/pipeline.png"/>
</div>

### Examples

<div align=center>
<img width="98%" src="https://github.com/dvlab-research/Seg-Zero/raw/main/assets/examples.png"/>
</div>

## Sample Usage: Evaluation

This dataset (`ReasonSeg-Test`) is designed for evaluating the zero-shot performance of models like Seg-Zero on reasoning-based image segmentation tasks.

First, install the necessary dependencies for the Seg-Zero project:

```bash
git clone https://github.com/dvlab-research/Seg-Zero.git
cd Seg-Zero
conda create -n visionreasoner python=3.12
conda activate visionreasoner
pip install torch==2.6.0 torchvision==0.21.0
pip install -e .
```

Then, you can run evaluation using the provided scripts. Make sure to download pretrained models first:

```bash
mkdir pretrained_models
cd pretrained_models
git lfs install
git clone https://huggingface.co/Ricky06662/VisionReasoner-7B
```

With the pretrained models downloaded, you can run the evaluation script for ReasonSeg:

```bash
bash evaluation_scripts/eval_reasonseg_visionreasoner.sh
```

Adjust `'--batch_size'` in the bash scripts based on your GPU. You will see the gIoU in your command line.

<div align=center>
<img width="98%" src="https://github.com/dvlab-research/Seg-Zero/raw/main/assets/val_results.png"/>
</div>

## The GRPO Algorithm

Seg-Zero generates several samples, calculates the rewards and then optimizes towards samples that achieve higher rewards.

<div align=center>
<img width="48%" src="https://github.com/dvlab-research/Seg-Zero/raw/main/assets/rl_sample.png"/>
</div>

## Citation

If you use this dataset or the Seg-Zero framework, please cite the associated papers:

```bibtex
@article{liu2025segzero,
  title        = {Seg-Zero: Reasoning-Chain Guided  Segmentation via Cognitive Reinforcement},
  author       = {Liu, Yuqi and Peng, Bohao and Zhong, Zhisheng and Yue, Zihao and Lu, Fanbin and Yu, Bei and Jia, Jiaya},
  journal      = {arXiv preprint arXiv:2503.06520},
  year         = {2025}
}

@article{liu2025visionreasoner,
  title        = {VisionReasoner: Unified Visual Perception and Reasoning via Reinforcement Learning},
  author       = {Liu, Yuqi and Qu, Tianyuan and Zhong, Zhisheng and Peng, Bohao and Liu, Shu and Yu, Bei and Jia, Jiaya},
  journal = {arXiv preprint arXiv:2505.12081},
  year         = {2025}
}
```