The Caption3o-XL-2B-Qwen2VL model is a fine-tuned version of Qwen2-VL-2B-Instruct, tailored for Image Captioning and Vision Language Attribution. This variant is designed to generate precise, highly descriptive captions with a focus on defining visual properties, object attributes, and scene details across a wide spectrum of images and aspect ratios.

Key Highlights

Vision Language Attribution (VLA): Specially fine-tuned to attribute and define visual properties of objects, scenes, and environments.
Detailed Object Definitions: Generates captions with rich attribute descriptions, making outputs more precise than generic captioners.
High-Fidelity Descriptions: Handles general, artistic, technical, abstract, and low-context images with descriptive depth.
Robust Across Aspect Ratios: Accurately captions images regardless of format—wide, tall, square, or irregular.
Variational Detail Control: Supports both concise summaries and fine-grained attributions depending on prompt structure.
Foundation on Qwen2-VL Architecture: Leverages Qwen2-VL-2B-Instruct’s multimodal reasoning for visual comprehension and instruction-following.
Multilingual Capability: Default in English, but adaptable for multilingual captioning through prompt engineering.

model type: experimental

[!note] General Query: Caption the image precisely.

Demo
Open In Colab

Demo Inference

Image A	Image B
Image A	Image B

Quick Start with Transformers

python

from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen2VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/Caption3o-XL-2B-Qwen2VL", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Caption3o-XL-2B-Qwen2VL")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Describe this image with detailed attributes and properties."},
        ],
    }
]

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")

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)

Intended Use

Generating attribute-rich image captions for research, dataset creation, and AI training.
Vision-language attribution for object detection, scene understanding, and dataset annotation.
Supporting creative, artistic, and technical applications requiring detailed descriptions.
Captioning across varied aspect ratios, unusual visual styles, and non-standard datasets.

Limitations

May over-attribute or infer properties not explicitly visible in ambiguous images.
Outputs can vary in tone depending on prompt phrasing.
Accuracy may degrade on synthetic or highly abstract visual domains.