SigLIP Base Multilingual Vision Model
| Property | Value |
|---|---|
| Parameter Count | 371M |
| License | Apache 2.0 |
| Resolution | 256x256 |
| Paper | Sigmoid Loss for Language Image Pre-Training |
| Training Data | WebLI Dataset |
What is siglip-base-patch16-256-multilingual?
SigLIP is an advanced multimodal model that builds upon CLIP's architecture while introducing an innovative sigmoid loss function for improved image-text processing. This multilingual version supports cross-language image classification and retrieval tasks, trained on the comprehensive WebLI dataset without language filtering.
Implementation Details
The model processes images at 256x256 resolution with RGB normalization (mean: 0.5, std: 0.5). Text inputs are tokenized to 64 tokens, making it efficient for real-world applications. Training was conducted on 16 TPU-v4 chips over three days, resulting in a model that excels at zero-shot classification tasks.
- Optimized sigmoid loss function for better scaling
- Multilingual support for diverse applications
- Efficient 256x256 resolution processing
- 64-token text processing capacity
Core Capabilities
- Zero-shot image classification across languages
- Image-text retrieval and matching
- Batch processing optimization
- Multilingual understanding and classification
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its sigmoid loss function that operates directly on image-text pairs without requiring global similarity normalization, enabling better scaling and improved performance even with smaller batch sizes.
Q: What are the recommended use cases?
This model excels in zero-shot image classification, multilingual image-text retrieval, and general visual understanding tasks. It's particularly useful for applications requiring cross-lingual image classification without additional training.
