Qwen2.5-VL Vision Model: Features, Applications, and More

The Qwen family of vision-language models continues to evolve, with the release of Qwen2.5-VL marking a significant leap forward. Building on the success of Qwen2-VL, which was launched five months ago, Qwen2.5-VL benefits from valuable feedback and contributions from the developer community. This feedback has played a key role in refining the model, adding new features, and optimizing its abilities. In this article, we will be exploring the architecture of Qwen2.5-VL, along with its features and capabilities.

What is Qwen2.5-VL?

Alibaba Cloud’s Qwen model has gotten a vision upgrade with the new Qwen2.5-VL. It is designed to offer cutting-edge vision features for complex real-life tasks. Here’s what the advanced features of this new model can do:

• Omnidocument Parsing: Expands text recognition to handle multilingual documents, including handwritten notes, tables, charts, chemical formulas, and music sheets.
• Precision Object Grounding: Detects and localizes objects with improved accuracy, supporting absolute coordinates and JSON formats for advanced spatial analysis.
• Ultra-Long Video Comprehension: Processes multi-hour videos through dynamic frame-rate sampling and temporal resolution alignment, enabling precise event segmentation.
• Enhanced Agent Capabilities: Empowers devices like smartphones and computers with superior decision-making, grounding, and reasoning for interactive tasks.
• Long-Form Video Comprehension: Processes hour-long videos using dynamic frame-rate sampling and temporal encoding, enabling precise event localization, summary creation, and targeted information extraction.
• Integration with Workflows: Automates document processing, object tracking, and video indexing with structured JSON outputs and QwenVL HTML, seamlessly connecting AI capabilities to enterprise workflows.

Also Read: Chinese Giants Faceoff: DeepSeek-V3 vs Qwen2.5

Qwen2.5-VL: Model Architecture

The model’s architecture introduces two key innovations:

1. Dynamic Resolution and Frame Rate Training: It adjusts the frame rate (FPS) of videos to suit different temporal conditions. It uses mRoPE (multidimensional Rotary Position Embedding) to align time data and accurately track moments in videos.

2. Streamlined Vision Encoder: It enhances the Vision Transformer (ViT) by improving attention mechanisms and activation functions. This facilitates faster and more efficient training and inference, making it work seamlessly with Qwen2.5’s language model.

Qwen2.5-VL: Model Capabilities

Now let’s try out some prompts and test the capabilities of Qwen2.5-VL.

1. Global Image Recognition

Qwen2.5-VL can identify an expanded range of categories, including flora, fauna, global landmarks, film/TV IPs, and commercial products.

Let’s test it with an example.

Prompt: “What are these attractions? Please give their names in Hindi and English.”

Response by Qwen2.5-VL-72B-Instruct:

The attractions in the images are:

1. Red Fort (लाल क़िला) – The image shows a part of the Red Fort, a historic fort in Delhi, India.
2. Humayun’s Tomb (हुमायूँ का मकबरा) – This is a Mughal architecture tomb located in Delhi.
3. India Gate (इंडिया गेट) – A war memorial located astride the Rajpath, on the eastern edge of the ‘ceremonial axis’ of New Delhi.
4. Qutub Minar (क़ुतुब मीनार) – A minaret and “victory tower” that forms part of the Qutb complex, a UNESCO World Heritage Site in the Mehrauli area of New Delhi.

2. Precise Object Grounding

The model employs bounding boxes and point coordinates for hierarchical object localization, outputting standardized JSON for spatial reasoning.

Prompt: “Locate every cake and describe their features, output the box coordinates in JSON format.”

Response by Qwen2.5-VL:

3. Advanced Text Recognition

Enhanced OCR capabilities support multilingual, multi-orientation text extraction, critical for financial audits and compliance workflows.

Prompt: “Spotting all the text in the image with line-level, and output in JSON format.”

Response by Qwen2.5-VL:

4. Document Parsing with QwenVL HTML

A proprietary format extracts layout data (headings, paragraphs, images) from magazines, research papers, and mobile screenshots.

Prompt: “Structure this technical report into HTML with bounding boxes for titles, abstracts, and figures.”

Response by Qwen2.5-VL:

Qwen2.5-VL: Performance Comparison

Qwen2.5-VL demonstrates state-of-the-art results across diverse benchmarks, solidifying its position as a leader in vision-language tasks. The flagship Qwen2.5-VL-72B-Instruct excels in college-level problem-solving, mathematical reasoning, document understanding, video analysis, and agent-based applications. Notably, it outperforms competitors in document/diagram comprehension and operates as a visual agent without task-specific fine-tuning.

The model outperforms competitors like Gemini-2 Flash, GPT-4o, and Claude3.5 Sonnet across benchmarks such as MMMU (70.2), DocVQA (96.4), and VideoMME (73.3/79.1).

For smaller models, Qwen2.5-VL-7B-Instruct surpasses GPT-4o-mini in multiple tasks, while the compact Qwen2.5-VL-3B—designed for edge AI—outperforms its predecessor, Qwen2-VL-7B, showcasing efficiency without compromising capability.

How to Access Qwen2.5-VL

You can access Qwen2.5VL in 2 ways – by using Huggin Face Transformers or with the API. Let’s understand both these ways.

Via Hugging Face Transformers

To access the Qwen2.5-VL model using Hugging Face, follow these steps:

1. Install Dependencies

First, make sure you have the latest version of Hugging Face Transformers and accelerate by installing them from the source:

pip install git+https://github.com/huggingface/transformers accelerate

Also, install qwen-vl-utils for handling various types of visual input:

pip install qwen-vl-utils[decord]==0.0.8

If you’re not on Linux, you can install without the [decord] feature. But if you need it, try installing from the source.

2. Load the Model and Tokenizer

Use the following code to load the Qwen2.5-VL model and tokenizer from Hugging Face:

from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor

# Load the model
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2.5-VL-3B-Instruct", torch_dtype="auto", device_map="auto"
)

# Load the processor for handling inputs (images, text, etc.)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct")

3. Prepare the Input (Image + Text)

You can provide images and text in different formats (URLs, base64, or local paths). Here’s an example using an image URL:

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "https://path.to/your/image.jpg"},
            {"type": "text", "text": "Describe this image."}
        ]
    }
]

4. Process the Inputs

Prepare the input for the model, including images and text, and tokenize the text:

# Process the messages (images + text)
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")  # Move the input to GPU if available

5. Generate the Output

Generate the model’s output based on the inputs:

# Generate the output from the model
generated_ids = model.generate(**inputs, max_new_tokens=128)

# Decode the output
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)

print(output_text)

API Access

Here’s how you can access the API for exploring the Qween 2.5 VL 72B model via Dashscope:

import dashscope

# Set your Dashscope API key
dashscope.api_key = "your_api_key"

# Make the API call with the desired model and messages
response = dashscope.MultiModalConversation.call(
    model='qwen2.5-vl-72b-instruct',
    messages=[{"role": "user", "content": [{"image": "image_url"}, {"text": "Query"}]}]
)

# You can access the response here
print(response)

Make sure to replace “your_api_key” with your actual API key and “image_url” with the URL of the image you want to use, along with the query text.

Real Life Use Cases

Qwen2.5-VL’s upgrades unlock diverse applications across industries, transforming how professionals interact with visual and textual data. Here are some of its real life use cases:

1. Document Analysis

The model revolutionizes workflows by effortlessly parsing complex materials like multilingual research papers, handwritten notes, financial invoices, and technical diagrams.

• In education, it helps students and researchers extract formulas or data from scanned textbooks.
• Banks can use it to automate compliance checks by reading tables in contracts.
• Law firms can quickly analyze multilingual legal documents with this model.

2. Industrial Automation

With pinpoint object detection and JSON-formatted coordinates, Qwen2.5-VL boosts precision in factories and warehouses.

• Robots can use its spatial reasoning to identify and sort items on conveyor belts.
• Quality control systems can spot defects in products like circuit boards or machinery parts using it.
• Logistics teams can track shipments in real time by analyzing warehouse camera feeds.

3. Media Production

The model’s video analysis skills save hours for content creators. It can scan a 2-hour documentary to tag key scenes, generate chapter summaries, or extract clips of specific events (e.g., “all shots of the Eiffel Tower”).

• News agencies can use it to index archived footage.
• Social media teams can auto-generate captions for video posts in multiple languages.

4. Smart Device Integration

Qwen2.5-VL powers “AI assistants” that understand screen content and automate tasks.

• On smartphones, it can read app interfaces to book flights or fill forms without manual input.
• In smart homes, it can guide robots to locate misplaced items by analyzing camera feeds.
• Office workers can use it to automate repetitive desktop tasks, like organizing files based on document content.

Conclusion

Qwen2.5-VL is a major step forward in AI technology that combines text, images, and video understanding. Building on its earlier versions, this model introduces smarter features like reading complex documents, including handwritten notes and charts. It also pinpoints objects in images with precise coordinates and analyzes hours-long videos to identify key moments.

Easy to access through platforms like Hugging Face or APIs, Qwen2.5-VL makes powerful AI tools available to everyone. By tackling real-world challenges from reducing manual data entry to speeding up content creation Qwen2.5-VL proves that advanced AI isn’t just for labs. It’s a practical tool reshaping everyday workflows across the globe.

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Janvi Kumari

Hi, I am Janvi, a passionate data science enthusiast currently working at Analytics Vidhya. My journey into the world of data began with a deep curiosity about how we can extract meaningful insights from complex datasets.