DeepAttriCap-VLA-3B

The DeepAttriCap-VLA-3B model is a fine-tuned version of Qwen2.5-VL-3B-Instruct, tailored for Vision-Language Attribution and Image Captioning. This variant is designed to generate precise, attribute-rich descriptions that define the visual properties of objects and scenes in detail, ensuring both object-level identification and contextual captioning.

Key Highlights

1. Vision-Language Attribution: Produces structured captions with explicit object attributes, properties, and contextual details.
2. High-Precision Descriptions: Captures fine-grained visual properties (shape, color, texture, material, relations).
3. Balanced Object-Centric and Scene-Level Captions: Generates both holistic captions and per-object attributions.
4. Adaptable Across Image Types: Works well on natural, artistic, abstract, and technical imagery.
5. Built on Qwen2.5-VL Architecture: Leverages the strengths of the 3B multimodal instruction-tuned variant for fine-grained reasoning.
6. Multilingual Capability: English is default, with multilingual captioning enabled through prompt engineering.

model type: experimental

Training Details

This model was fine-tuned on a mixture of curated image–caption datasets with emphasis on attribute-based captioning and precise object-property definition:

• prithivMLmods/blip3o-caption-mini-arrow
• prithivMLmods/Caption3o-Opt-v3
• prithivMLmods/Caption3o-Opt-v2
• Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_2.7b_Attributes_Caption_ns_5647

The training objective emphasized attribution-style captioning—capturing precise object details, relationships, and scene-level semantics.

---

SYSTEM_PROMPT

CAPTION_SYSTEM_PROMPT = """
You are an AI assistant that rigorously follows this response protocol:

1. For every input image, your primary task is to write a **precise caption**. The caption must capture the **essence of the image** in clear, concise, and contextually accurate language.

2. Along with the caption, provide a structured set of **attributes** that describe the visual elements. Attributes should include details such as objects, people, actions, colors, environment, mood, and other notable characteristics.

3. Always include a **class_name** field. This must represent the **core theme or main subject** of the image in a compact format.  
   - Use the syntax: `{class_name==write_the_core_theme}`  
   - Example: `{class_name==dog_playing}` or `{class_name==city_sunset}`  

4. Maintain the following strict format in your output:
   - **Caption:** <one-sentence description>  
   - **Attributes:** <comma-separated list of visual attributes>  
   - **{class_name==core_theme}**

5. Ensure captions are **precise, neutral, and descriptive**, avoiding unnecessary elaboration or subjective interpretation unless explicitly required.

6. Do not reference the rules or instructions in the output. Only return the formatted caption, attributes, and class_name.

""".strip()

---

Quick Start with Transformers

from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/DeepAttriCap-VLA-3B", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/DeepAttriCap-VLA-3B")

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"},
            {"type": "text", "text": "Provide an attribute-rich caption for this image."},
        ],
    }
]

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"
).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

• Attribute-rich object recognition and captioning.
• Vision-language research in attribution and property extraction.
• Dataset creation for fine-grained visual description tasks.
• Enabling descriptive captions for images with complex object relationships.
• Supporting creative, technical, and educational use cases requiring precise captions.

Limitations

• May produce variable levels of granularity depending on the image complexity.
• Not optimized for highly censored or safety-critical deployments.
• Might over-attribute or hallucinate properties in ambiguous or abstract visuals