Omni Recognition

Recognize animals, plants, people, scenic spots, cars, merchandise, and various objects

Qwen3-VL excels at recognizing a wide variety of objects beyond basic image classification. The model can identify animals, plants, people, celebrities, scenic spots, cars, merchandise, and many other object types with high accuracy.

Capability Overview

The omni recognition capability enables you to:

• Identify animals, plants, and natural objects
• Recognize people, celebrities, and anime characters
• Detect products, merchandise, and commercial items
• Identify landmarks, scenic spots, and locations
• Recognize vehicles, cars, and transportation
• Classify flora, fauna, and various objects

Example Usage

from transformers import AutoModelForImageTextToText, AutoProcessor

model = AutoModelForImageTextToText.from_pretrained(
    "Qwen/Qwen3-VL-235B-A22B-Instruct", dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-235B-A22B-Instruct")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "path/to/your/image.jpg",
            },
            {"type": "text", "text": "What objects do you see in this image?"},
        ],
    }
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
)
inputs = inputs.to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=128)
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)

Try it Yourself

Explore the full omni recognition cookbook with interactive examples:

View on GitHub