Sa2VA-8B
| Property | Value |
|---|---|
| Author | ByteDance |
| Base Model | InternVL2.5-8B |
| Language Component | internlm2_5-7b-chat |
| Model Hub | Hugging Face |
What is Sa2VA-8B?
Sa2VA-8B is an advanced multimodal language model that combines the capabilities of SAM2 and LLaVA to deliver comprehensive image and video understanding. Built on InternVL2.5-8B architecture, it achieves state-of-the-art performance in both visual understanding and dense object segmentation tasks. The model demonstrates exceptional capabilities in MMBench (84.4%) and various reference datasets like RefCOCO (82.6%) and DAVIS (75.9%).
Implementation Details
The model integrates seamlessly with the transformers library and supports both image and video processing tasks. It operates with bfloat16 precision and includes optimizations like flash attention for improved performance. The architecture combines visual understanding with dense segmentation capabilities, enabling both textual responses and mask-based outputs.
- Built on InternVL2.5-8B architecture with internlm2_5-7b-chat language component
- Supports both image and video processing with segmentation capabilities
- Implements efficient processing with flash attention and bfloat16 precision
Core Capabilities
- Advanced question answering on visual content
- Dense object segmentation for both images and videos
- Visual prompt understanding with mask-based inputs
- Real-time video frame analysis and segmentation
- Multi-turn dialogue support with visual context
Frequently Asked Questions
Q: What makes this model unique?
Sa2VA-8B stands out for its ability to combine high-level visual understanding with precise object segmentation, something that competitors like Qwen2-VL and InternVL2.5 don't offer. It achieves this while maintaining competitive performance on standard benchmarks.
Q: What are the recommended use cases?
The model excels in applications requiring detailed visual analysis, such as image/video description, object segmentation, and interactive visual conversations. It's particularly useful for tasks requiring both natural language understanding and precise object localization.
