Update README.md

README.md

@@ -15,18 +15,23 @@ tags:
 
 # **Spatial-VU**
 
-> The **Spatial-VU** model is a fine-tuned variant of **Qwen2.5-VL-7B-Instruct**, developed for **Spatial Reasoning** and **Vision Understanding**. It is designed to deliver detailed, context-aware visual descriptions and reasoning outputs across a wide range of image types, resolutions, and aspect ratios.
+> 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.
 
-## Key Highlights
+# Key Highlights
 
-* **Spatial Reasoning and Visual Comprehension**: Optimized for interpreting spatial layouts, object relationships, and scene understanding within images.
-* **High-Precision Descriptions**: Generates detailed, context-rich captions for general, technical, and abstract imagery.
-* **Adaptive Across Aspect Ratios**: Performs effectively with images of varying formats—wide, tall, square, and irregular.
-* **Multi-Level Detail Control**: Supports both concise summaries and fine-grained analytical outputs, depending on the prompt.
-* **Built on Qwen2.5-VL Architecture**: Utilizes the visual-linguistic reasoning power of Qwen2.5-VL-7B for structured and accurate comprehension tasks.
-* **Multilingual Support**: Outputs in English by default, with the ability to generate multilingual responses through prompt conditioning.
+* **Spatial Reasoning & Vision Understanding**: Fine-tuned to provide accurate and descriptive visual interpretations, enabling deeper understanding of spatial relationships, structures, and context.
 
-## Quick Start with Transformers
+* **High-Fidelity Descriptions**: Generates comprehensive captions for general, artistic, technical, abstract, and low-context images.
+
+* **Robust Across Aspect Ratios**: Capable of accurately captioning images with wide, tall, square, and irregular dimensions.
+
+* **Variational Detail Control**: Produces outputs with both high-level summaries and fine-grained descriptions as needed.
+
+* **Foundation on Qwen2.5-VL Architecture**: Leverages the strengths of the Qwen2.5-VL-7B multimodal model for visual reasoning, comprehension, and instruction-following.
+
+* **Multilingual Output Capability**: Can support multilingual descriptions (English as default), adaptable via prompt engineering.
+
+# Quick Start with Transformers
 
 ```python
 from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
@@ -46,7 +51,7 @@ messages = [
                 "type": "image",
                 "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
             },
-            {"type": "text", "text": "Analyze the spatial layout and describe the scene."},
+            {"type": "text", "text": "Describe this image in detail."},
         ],
     }
 ]
@@ -74,17 +79,18 @@ output_text = processor.batch_decode(
 print(output_text)
 ```
 
-## Intended Use
+# Intended Use
 
-* Spatial reasoning, visual understanding, and scene analysis tasks.
-* Descriptive and interpretive caption generation for research or vision-language evaluation.
-* Structured visual comprehension in creative and analytical applications.
-* Data annotation and reasoning augmentation in multimodal AI pipelines.
-* Spatial layout and context interpretation across diverse visual domains.
+* Generating detailed and unfiltered image captions for general-purpose or artistic datasets.
+* Spatial reasoning and vision understanding research.
+* Content moderation research, red-teaming, and generative safety evaluations.
+* Enabling descriptive captioning for visual datasets typically excluded from mainstream models.
+* Use in creative applications (e.g., storytelling, art generation) that benefit from rich descriptive captions.
+* Captioning for non-standard aspect ratios and stylized visual content.
 
-## Limitations
+# Limitations
 
-* May generate variable outputs depending on the phrasing of prompts.
-* Not recommended for production use cases requiring strong moderation or controlled tone.
-* Performance may vary on abstract, heavily stylized, or low-context visual inputs.
-* Lacks fine-tuned control over subjective or ambiguous visual content interpretations.
+* May produce explicit, sensitive, or offensive descriptions depending on image content and prompts.
+* Not suitable for deployment in production systems requiring content filtering or moderation.
+* Can exhibit variability in caption tone or style depending on input prompt phrasing.
+* Accuracy for unfamiliar or synthetic visual styles may vary.