What is Qwen2.5-VL
Qwen2.5-VL is the flagship vision-language model open-sourced by the Alibaba Tongyi Qianwen team, available in three different scales: 3B, 7B, and 72B. The model performs excellently in visual understanding, able to recognize common objects and analyze elements such as text and charts in images. Qwen2.5-VL has the capability to act as a visual Agent, can reason and dynamically use tools, and can preliminarily operate computers and mobile phones. In video processing, it can understand long videos exceeding 1 hour and precisely locate relevant segments to capture events. The model supports structured output of data such as invoices and forms. In performance tests, Qwen2.5-VL-72B-Instruct performs excellently across multiple domains and tasks, with obvious advantages in document and chart understanding. The 7B model surpasses GPT-4o-mini in multiple tasks.
Main Features of Qwen2.5-VL
- Visual Understanding: Able to recognize common objects such as flowers, birds, fish, and insects, and analyze text, charts, icons, graphics, and layouts in images.
- Visual Agent Capability: Can directly act as a visual Agent, reasoning and dynamically using tools, and preliminarily possesses the ability to use computers and mobile phones.
- Understanding Long Videos and Capturing Events: Can understand videos exceeding 1 hour and precisely locate relevant video segments to capture events.
- Visual Grounding: Can accurately locate objects in images by generating bounding boxes or points, and provide stable JSON output for coordinates and attributes.
- Structured Output: Supports structured output of content for data such as invoices, forms, and tables.
Technical Principles of Qwen2.5-VL
- Model Architecture: Qwen2.5-VL continues the serial structure of ViT plus Qwen2 from the previous generation Qwen-VL. All three models of different scales use a 600M-scale ViT and support unified input of images and videos. This enables the model to better integrate visual and language information and improve its understanding of multimodal data.
- Multimodal Rotary Position Embedding (M-ROPE): The M-ROPE adopted by Qwen2.5-VL decomposes rotary position embedding into three parts: time and space (height and width), enabling the large language model to simultaneously capture and integrate position information from one-dimensional text, two-dimensional vision, and three-dimensional video, endowing the model with powerful multimodal processing and reasoning capabilities.
- Arbitrary Resolution Image Recognition: Qwen2.5-VL can understand images of different resolutions and aspect ratios, and can easily recognize image clarity or size. Based on naive dynamic resolution support, it can map images of arbitrary resolution into a dynamic number of visual tokens, ensuring consistency between model input and image information.
- Simplified Network Architecture: Compared with Qwen2-VL, Qwen2.5-VL enhances the model's perception of temporal and spatial scales and further simplifies the network architecture to improve model efficiency.
- Improved Model Reasoning Capability: Qwen2.5-VL has achieved the best results among open-source models of the same scale in multiple authoritative evaluations, with particularly obvious advantages in document understanding. Compared with closed-source models such as GPT-4O and Claude3.5-Sonnet, Qwen2.5-VL achieves optimal performance on most metrics.
Qwen2.5-VL Project Address
- Project Official Website：https://qwenlm.github.io/blog/qwen2.5-vl
- GitHub Repository：https://github.com/QwenLM/Qwen2.5-VL
- HuggingFace Model Library：https://huggingface.co/collections/Qwen/qwen25-vl
Application Scenarios of Qwen2.5-VL
- Document Understanding: Qwen2.5-VL has advantages in understanding documents and charts, and can operate as a visual Agent without task-specific fine-tuning.
- Intelligent Assistant: The model can serve as an intelligent assistant to help users complete various tasks, such as booking flights, checking the weather, and more.
- Data Processing: Qwen2.5-VL supports structured output for data such as invoices, forms, and tables.
- Device Operation: The model can operate mobile phones, online platforms, and computers, providing a valuable reference point for creating a true visual agent.
- Object Localization: Qwen2.5-VL can accurately locate objects in images by generating bounding boxes or points, and can provide stable JSON output for coordinates and attributes.