BGE-VL-MLLM-S1
| Property | Value |
|---|---|
| Author | BAAI |
| License | MIT License |
| Paper | MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval |
| Model URL | https://huggingface.co/BAAI/BGE-VL-MLLM-S1 |
What is BGE-VL-MLLM-S1?
BGE-VL-MLLM-S1 is a state-of-the-art multimodal retrieval model trained exclusively on the MegaPairs dataset, which contains over 26 million heterogeneous KNN triplets. The model demonstrates exceptional performance in composed image retrieval tasks, achieving an 8.1% improvement on the CIRCO benchmark (mAP@5) compared to previous SOTA models.
Implementation Details
The model utilizes a sophisticated architecture optimized for multimodal retrieval tasks. It can process both images and text inputs, making it particularly effective for composed image retrieval scenarios. The implementation supports both query and candidate processing modes, with built-in normalization and embedding generation capabilities.
- Built-in processor for handling both image and text inputs
- CUDA-compatible for enhanced performance
- Supports batch processing of multiple images
- Generates normalized embeddings for accurate similarity matching
Core Capabilities
- Zero-shot composed image retrieval with SOTA performance
- Efficient processing of multimodal inputs
- Robust generalization across various retrieval tasks
- Advanced similarity scoring between queries and candidates
Frequently Asked Questions
Q: What makes this model unique?
BGE-VL-MLLM-S1's uniqueness lies in its training on the MegaPairs dataset and its ability to achieve state-of-the-art performance in composed image retrieval without requiring additional fine-tuning. It demonstrates exceptional zero-shot capabilities while maintaining a relatively efficient architecture.
Q: What are the recommended use cases?
The model excels in multimodal retrieval tasks, particularly in scenarios requiring composed image retrieval where both text and image inputs need to be processed. It's ideal for applications like visual search engines, content recommendation systems, and image-text matching tasks.
