49 lines
2.2 KiB
Markdown
49 lines
2.2 KiB
Markdown
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# Qwen2.5-VL-3B-Instruct-Traffic
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**Qwen2.5-VL-3B-Instruct-Traffic** is a multimodal model fine-tuned on the **MITS (Multimodal Intelligent Traffic Surveillance)** dataset for intelligent traffic surveillance scenarios.
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- **Tasks:** recognition, counting, localization, background awareness, reasoning
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- **Data:** 170,400 images + ~5M instruction-following VQA pairs from MITS
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- **Modality:** Image + Text → Text
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- **Domain:** traffic scenes (congestion, accidents, construction, smoke/fireworks, unusual weather, spills, etc.)
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## Quick Links
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- 📚 Dataset: [`zhaokaikai/Multimodal_Intelligent_Traffic_Surveillance`](https://www.modelscope.cn/datasets/zhaokaikai/Multimodal_Intelligent_Traffic_Surveillance)
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- 💻 Usage & examples: please refer to the GitHub repo
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**https://github.com/LifeIsSoSolong/Multimodal-Intelligent-Traffic-Surveillance-Dataset-Models**
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## Intended Use
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- Urban traffic monitoring, incident analysis, visual question answering for transportation management
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- Research on ITS-specific multimodal reasoning and instruction following
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## Model Inputs/Outputs
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- **Input:** an image (traffic scene) + a natural language instruction/question
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- **Output:** a natural language response (e.g., description, count, event reasoning)
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## Training Summary
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- Objective: instruction tuning on MITS traffic QA
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- Backbone family: Qwen2.5-VL 3B Instruct
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- Notes: align vision-language features to traffic-centric concepts and events
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## Limitations & Notes
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- The model may make mistakes on rare objects or extreme weather/night scenes not well represented in training.
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- Not a safety-critical system; human verification is required for real-world decisions.
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## License
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- Follow the licenses of this model and the MITS dataset as stated on their ModelScope pages.
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## Citation
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If you use this model or dataset, please cite:
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```bibtex
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@article{zhao2025mits,
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title = {MITS: A large-scale multimodal benchmark dataset for Intelligent Traffic Surveillance},
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author = {Zhao, Kaikai and Liu, Zhaoxiang and Wang, Peng and Wang, Xin and Ma, Zhicheng and Xu, Yajun and Zhang, Wenjing and Nan, Yibing and Wang, Kai and Lian, Shiguo},
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journal = {Image and Vision Computing},
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pages = {105736},
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year = {2025},
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publisher = {Elsevier}
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}
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```
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## Contact
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Unicom AI
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