Improve model card for VisionReasoner-7B (Seg-Zero framework) (#2)

- Improve model card for VisionReasoner-7B (Seg-Zero framework) (99e8a6b6cd819c0727f49ba0edb9d79dd70d5c24)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,33 +1,50 @@
 ---
-license: apache-2.0
+base_model:
+  - Qwen2.5-VL
 datasets:
-- COCO
-- ReasonSeg
-- CountBench
+  - COCO
+  - ReasonSeg
+  - CountBench
+  - Ricky06662/refCOCOg_9k_840
+  - Ricky06662/VisionReasoner_multi_object_7k_840
 language:
-- en
-metrics:
-- accuracy
-base_model:
-- Qwen2.5-VL
-pipeline_tag: image-text-to-text
+  - en
 library_name: transformers
+license: apache-2.0
+metrics:
+  - accuracy
+pipeline_tag: image-segmentation
 ---
 
-# VisionReasoner-7B
+# VisionReasoner-7B from the Seg-Zero Framework
 
-[Paper](https://huggingface.co/papers/2505.12081)
+This repository contains the **VisionReasoner-7B** model, developed as part of the novel **Seg-Zero** framework, presented in the paper [Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement](https://huggingface.co/papers/2503.06520). This model is also associated with the paper [VisionReasoner: Unified Visual Perception and Reasoning via Reinforcement Learning](https://huggingface.co/papers/2505.12081).
 
-Code: [https://github.com/dvlab-research/VisionReasoner](https://github.com/dvlab-research/VisionReasoner)
+Code: [https://github.com/dvlab-research/Seg-Zero](https://github.com/dvlab-research/Seg-Zero)
+Project page: [https://github.com/dvlab-research/Seg-Zero](https://github.com/dvlab-research/Seg-Zero)
 
-Project page: [https://github.com/dvlab-research/VisionReasoner](https://github.com/dvlab-research/VisionReasoner)
+<div align="center">
+<img width="98%" src="https://raw.githubusercontent.com/dvlab-research/Seg-Zero/main/assets/overview.png"/>
+</div>
 
 ## Description
 
-This is a VisionReasoner-7B model. It 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 pixel-level masks.
+**Seg-Zero** is a novel framework that demonstrates remarkable generalizability and derives explicit chain-of-thought reasoning through cognitive reinforcement for reasoning segmentation. This **VisionReasoner-7B** model employs 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 precise pixel-level masks.
+
+<div align="center">
+<img width="98%" src="https://raw.githubusercontent.com/dvlab-research/Seg-Zero/main/assets/pipeline.png"/>
+</div>
+
+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.
+
+<div align="center">
+<img width="98%" src="https://raw.githubusercontent.com/dvlab-research/Seg-Zero/main/assets/examples.png"/>
+</div>
 
 ## Usage
 
+You can load and use this model with the `transformers` library:
+
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
@@ -35,4 +52,26 @@ import torch
 # load model
 model = AutoModelForCausalLM.from_pretrained("Ricky06662/VisionReasoner-7B")
 tokenizer = AutoTokenizer.from_pretrained("Ricky06662/VisionReasoner-7B")
+```
+
+For full inference examples, including image processing and input formatting, please refer to the project's GitHub repository.
+
+## Citation
+
+If you find our work helpful or inspiring, please feel free to cite our 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}
+}
 ```