---
task_categories:
- image-segmentation
license: cc-by-nc-4.0
tags:
- reasoning
- reinforcement-learning
- zero-shot
dataset_info:
  features:
  - name: id
    dtype: string
  - name: problem
    dtype: string
  - name: solution
    dtype: string
  - name: image
    dtype: image
  - name: img_height
    dtype: int64
  - name: img_width
    dtype: int64
  splits:
  - name: train
    num_bytes: 1872591792.0
    num_examples: 2000
  download_size: 1090458784
  dataset_size: 1872591792.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Seg-Zero Dataset: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement

This repository hosts the training dataset introduced in the paper [Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement](https://huggingface.co/papers/2503.06520).

## 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.

## Code and Project Links
*   **Paper Link:** [https://huggingface.co/papers/2503.06520](https://huggingface.co/papers/2503.06520)
*   **Code Repository:** [https://github.com/dvlab-research/Seg-Zero](https://github.com/dvlab-research/Seg-Zero)

## Dataset Description
This dataset is designed for training and evaluating models on reasoning-chain guided image segmentation tasks. It contains `2000` examples in the `train` split, with each entry comprising:
- `id`: Unique identifier for the sample.
- `problem`: The reasoning problem or question.
- `solution`: The explicit reasoning chain or solution.
- `image`: The input image.
- `img_height`: Height of the image.
- `img_width`: Width of the image.

## Key Features of Seg-Zero (Associated Framework)
This dataset supports the Seg-Zero framework, which demonstrates the following key features:
1.  **Emergent Test-Time Reasoning**: Seg-Zero exhibits emergent test-time reasoning ability. It generates a reasoning chain before producing the final segmentation mask.
2.  **Reinforcement Learning Only**: Seg-Zero is trained exclusively using reinforcement learning, without any explicit supervised reasoning data.
3.  **Superior Generalization**: Compared to supervised fine-tuning, Seg-Zero achieves superior performance on both in-domain and out-of-domain data.

## Sample Usage
You can load the dataset using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

# Load the training split of the Seg-Zero dataset
dataset = load_dataset("Ricky06662/Seg-Zero", split="train")

# Access the first example
print(dataset[0])

# Example of accessing image and problem statement
print(f"Problem: {dataset[0]['problem']}")
dataset[0]['image'].save("first_image.png")
print("First image saved as first_image.png")
```

## Citation
If you find this dataset or the associated work useful, please cite the paper:

```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}
}
```