Qwen2.5-VL - A visual language model open-sourced by Alibaba's Tongyi Qianwen platform.
Qwen2.5-VL is the flagship visual language model open-sourced by Alibaba's Tongyi Qianwen team, available in three different scales: 3B, 7B, and 72B. The model excels in visual understanding, capable of recognizing common objects and analyzing text and charts in images...
What is Qwen2.5-VL?
Qwen2.5-VL is the flagship visual language model open-sourced by the Alibaba Tongyi Qianwen team, available in three different scales: 3B, 7B, and 72B. The model excels in visual understanding, capable of recognizing common objects and analyzing text, charts, and other elements in images. Qwen2.5-VL possesses the capabilities of a visual agent, able to reason and dynamically use tools, and can perform basic computer and mobile phone operations. In video processing, it can understand videos exceeding one hour in length, accurately locating relevant segments and capturing events. The model supports structured output of data such as invoices and forms. In performance tests, Qwen2.5-VL-72B-Instruct demonstrates superior performance across multiple domains and tasks, showing a clear advantage in document and chart understanding. The 7B model outperforms GPT-4o-mini in several tasks.
Main functions of Qwen2.5-VL
- Visual understandingIt can recognize common objects such as flowers, birds, fish and insects, and analyze text, charts, icons, graphics and layouts in images.
- Visual Agent CapabilitiesIt can directly function as a visual agent, reasoning and dynamically using tools, and has a basic ability to use computers and mobile phones.
- Understanding Long Videos and Capturing EventsIt can understand videos longer than one hour and accurately locate relevant video clips to capture events.
- Visual positioningIt can accurately locate objects in an image by generating bounding boxes or points, and can provide stable JSON output for coordinates and attributes.
- Structured outputFor data such as invoices, forms, and tables, it supports structured output of their content.
Technical Principles of Qwen2.5-VL
- Model StructureQwen2.5-VL continues the cascaded structure of ViT plus Qwen2 from its predecessor, Qwen-VL. All three models of different sizes utilize a 600M ViT and support unified input of images and videos. This allows the model to better integrate visual and linguistic information, improving its ability to understand multimodal data.
- Multimodal Rotational Position Encoding (M-ROPE)Qwen2.5-VL uses M-ROPE to decompose rotational position encoding into three parts: time, space (height and width), enabling large-scale language models to simultaneously capture and integrate positional information from one-dimensional text, two-dimensional vision, and three-dimensional video, thus giving the model powerful multimodal processing and reasoning capabilities.
- Arbitrary resolution image recognitionQwen2.5-VL can understand images of different resolutions and aspect ratios, easily recognizing image sharpness and size. Based on support for naive dynamic resolution, it can map images of any resolution into a dynamically increasing number of visual tokens, ensuring consistency between model input and image information.
- Network structure simplificationCompared to Qwen2-VL, Qwen2.5-VL enhances the model's ability to perceive temporal and spatial scales and further simplifies the network structure to improve model efficiency.
- Improved model reasoning abilityQwen2.5-VL has achieved best results in multiple authoritative benchmarks for open-source models of similar size, with a particularly significant advantage in document understanding. Compared to 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 UnderstandingQwen2.5-VL excels at understanding documents and charts, and can operate as a visual agent without requiring task-specific fine-tuning.
- Smart AssistantThe model can act as an intelligent assistant to help users complete various tasks, such as booking flights and checking the weather.
- Data processingQwen2.5-VL supports structured output of data such as invoices, forms, and tables.
- Equipment operationThe model can operate on mobile phones, network platforms, and computers, providing a valuable reference point for creating real visual agents.
- Object positioningQwen2.5-VL can accurately locate objects in an image by generating bounding boxes or points, and can provide stable JSON output for coordinates and attributes.