SpaceLLaVA-lite
| Property | Value |
|---|---|
| License | Apache-2.0 |
| Framework | PyTorch, Transformers |
| Base Model | MobileVLM |
| Paper | SpatialVLM Paper |
What is SpaceLLaVA-lite?
SpaceLLaVA-lite is an innovative vision-language model that enhances MobileVLM's capabilities with advanced spatial reasoning abilities. Built using VQASynth data synthesis techniques, it specializes in understanding and describing spatial relationships between objects in visual scenes. The model represents a significant step forward in making spatial reasoning more accessible and efficient in multimodal AI systems.
Implementation Details
The model is implemented using PyTorch and the Transformers framework, building upon the MobileVLM architecture. It incorporates specialized training data generated through VQASynth to improve spatial reasoning capabilities. The implementation focuses on efficiency while maintaining robust performance in spatial understanding tasks.
- Fine-tuned from MobileVLM base model
- Utilizes VQASynth for dataset generation
- Implements SpatialVLM methodology for enhanced spatial reasoning
- Supports inference with customizable parameters
Core Capabilities
- Accurate spatial relationship analysis between objects
- Distance estimation between scene elements
- Complex scene understanding and description
- Efficient inference on mobile devices
Frequently Asked Questions
Q: What makes this model unique?
SpaceLLaVA-lite uniquely combines MobileVLM's efficiency with enhanced spatial reasoning capabilities, making it particularly effective for applications requiring detailed understanding of object relationships in visual scenes.
Q: What are the recommended use cases?
The model is ideal for applications requiring spatial relationship analysis, such as robotics, scene understanding, and automated visual description systems. It's particularly suitable for mobile and edge devices due to its lite architecture.
