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

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chat_template.json Normal file
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"content": "<|file_sep|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
}
},
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": {},
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"processor_class": "Qwen2_5_VLProcessor",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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