kanshan/R1-Onevision
1
0
Fork 0
mirror of https://github.com/Fancy-MLLM/R1-Onevision.git synced 2026-01-13 23:07:18 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Update README.md

Browse Source
This commit is contained in:
YangYi-hub 2025-02-23 01:49:15 +08:00 committed by GitHub
parent 2885c2f88c
commit 0375041481
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 16 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

17
README.md
View File

@ -33,13 +33,28 @@
### Datasets
The **R1-Onevision** dataset is a meticulously crafted resource designed to empower models with advanced multimodal reasoning capabilities. Aimed at bridging the gap between visual and textual understanding, this dataset provides rich, context-aware reasoning tasks across diverse domains, including natural scenes, science, mathematical problems, OCR-based content, and complex charts.
It combines high-quality data from LLaVA-OneVision with domain-specific datasets, each carefully selected and filtered to provide a solid foundation for complex visual reasoning tasks. With a focus on enabling deep reasoning and accurate model predictions, **R1-Onevision** equips models to handle a variety of visual and textual inputs, tackling intricate reasoning challenges with precision.
As shown in the chart, the R1-Onevision dataset is a carefully crafted tool designed to push the boundaries of multimodal reasoning. By combining advanced captioning techniques, innovative reasoning methodologies, and rigorous quality control, we’ve created a dataset that not only supports reasoning tasks but also enhances the ability of models to think deeply and critically.
![R1-Onevision-Dataset](https://github.com/user-attachments/assets/8b0173e8-de06-4b39-b0ba-85f2f52f8c8e)
### Models
This is a multimodal large language model fine-tuned from Qwen2.5-VL on the **R1-Onevision** dataset. The model enhances vision-language understanding and reasoning capabilities, making it suitable for various tasks such as visual reasoning, image understanding. With its robust ability to perform multimodal reasoning, R1-Onevision emerges as a powerful AI assistant capable of addressing a wide range of problem-solving challenges across different domains.
### Performance
We evaluated R1-Onevision on Mathvision, Mathverse and R1-Onevision-Bench, and our model exhibits stronger reasoning performance than Qwen2.5-VL-72B and GPT-4V. The evaluation results are as follows:
| | Mathvision | Mathverse | R1-Onevision-Bench |
| --- | --- | --- | --- |
| Qwen2.5-VL-72B | 23.20% | 37.35% | |
| R1-Onevision | 26.16% | 44.06% | |
| GPT-4V | 22.76% | 39.4% | |
| GPT-4o | | | |
## 🏗️ Start
## 🧑‍💻 Authors