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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-VL-7B-Instruct |
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pipeline_tag: image-text-to-text |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- trl |
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- spatial-reasoning |
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- vision-understanding |
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--- |
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%3C!-- HTML_TAG_END --> |
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# **Spatial-VU** |
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> The **Spatial-VU** model is a fine-tuned version of **Qwen2.5-VL-7B-Instruct**, tailored for **Spatial Reasoning and Vision Understanding**. This variant is designed to generate highly detailed and descriptive captions across a broad range of visual categories, including images with complex, sensitive, or nuanced content—across varying aspect ratios and resolutions. |
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# Key Highlights |
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* **Spatial Reasoning & Vision Understanding**: Fine-tuned to provide accurate and descriptive visual interpretations, enabling deeper understanding of spatial relationships, structures, and context. |
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* **High-Fidelity Descriptions**: Generates comprehensive captions for general, artistic, technical, abstract, and low-context images. |
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* **Robust Across Aspect Ratios**: Capable of accurately captioning images with wide, tall, square, and irregular dimensions. |
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* **Variational Detail Control**: Produces outputs with both high-level summaries and fine-grained descriptions as needed. |
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* **Foundation on Qwen2.5-VL Architecture**: Leverages the strengths of the Qwen2.5-VL-7B multimodal model for visual reasoning, comprehension, and instruction-following. |
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* **Multilingual Output Capability**: Can support multilingual descriptions (English as default), adaptable via prompt engineering. |
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# Quick Start with Transformers |
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```python |
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained( |
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"prithivMLmods/Spatial-VU", torch_dtype="auto", device_map="auto" |
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) |
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processor = AutoProcessor.from_pretrained("prithivMLmods/Spatial-VU") |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "image", |
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", |
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}, |
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{"type": "text", "text": "Describe this image in detail."}, |
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], |
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} |
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] |
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text = processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True |
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) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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inputs = inputs.to("cuda") |
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generated_ids = model.generate(**inputs, max_new_tokens=128) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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output_text = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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) |
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print(output_text) |
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``` |
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# Intended Use |
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* Generating detailed and unfiltered image captions for general-purpose or artistic datasets. |
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* Spatial reasoning and vision understanding research. |
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* Content moderation research, red-teaming, and generative safety evaluations. |
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* Enabling descriptive captioning for visual datasets typically excluded from mainstream models. |
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* Use in creative applications (e.g., storytelling, art generation) that benefit from rich descriptive captions. |
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* Captioning for non-standard aspect ratios and stylized visual content. |
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# Limitations |
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* May produce explicit, sensitive, or offensive descriptions depending on image content and prompts. |
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* Not suitable for deployment in production systems requiring content filtering or moderation. |
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* Can exhibit variability in caption tone or style depending on input prompt phrasing. |
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* Accuracy for unfamiliar or synthetic visual styles may vary. |
