Sa2VA-4B
| Property | Value |
|---|---|
| Model Size | 4B parameters |
| Base MLLM | InternVL2.5-4B |
| Language Model | Qwen2.5-3B-Instruct |
| MMBench Score | 81.8 |
| Hugging Face | ByteDance/Sa2VA-4B |
What is Sa2VA-4B?
Sa2VA-4B is an advanced multimodal large language model that combines the capabilities of SAM2 with LLaVA to provide dense grounded understanding of both images and videos. Built by ByteDance, it represents a significant advancement in visual-language AI models, achieving comparable performance to state-of-the-art MLLMs while adding unique capabilities in visual prompt understanding and dense object segmentation.
Implementation Details
The model is built on InternVL2.5-4B architecture and incorporates Qwen2.5-3B-Instruct for language processing. It can be easily implemented using the Transformers library, supporting both image and video analysis with built-in segmentation capabilities.
- Supports bfloat16 precision for efficient processing
- Implements flash attention for improved performance
- Provides comprehensive API for both image and video analysis
- Outputs both textual descriptions and segmentation masks
Core Capabilities
- Dense object segmentation for both images and videos
- Visual prompt understanding with mask-based inputs
- High-performance question answering (81.8 on MMBench)
- Video analysis with frame sampling and processing
- Interleaved segmentation mask generation
Frequently Asked Questions
Q: What makes this model unique?
Sa2VA-4B stands out for its ability to combine high-level language understanding with precise object segmentation capabilities, something that many other MLLMs like Qwen2-VL and InternVL2.5 lack. It achieves state-of-the-art performance on both image and video grounding and segmentation benchmarks.
Q: What are the recommended use cases?
The model is ideal for applications requiring detailed visual analysis with segmentation, including image and video description, object localization, and interactive visual questioning. It's particularly useful for tasks requiring both natural language understanding and precise object identification.
