SigLIP SO400M Vision Model
| Property | Value |
|---|---|
| Parameter Count | 1.13B |
| License | Apache 2.0 |
| Training Data | WebLI Dataset |
| Architecture | SoViT-400m with patch16-256 |
| Paper | Sigmoid Loss for Language Image Pre-Training |
What is siglip-so400m-patch16-256-i18n?
The siglip-so400m-patch16-256-i18n is a shape-optimized vision transformer model developed by Google that implements the SigLIP architecture with multilingual capabilities. It represents an evolution of the CLIP model, utilizing a superior sigmoid loss function for enhanced image-text processing at varying batch sizes.
Implementation Details
This model was trained on 16 TPU-v4 chips over three days using the WebLI dataset. It processes images at 384x384 resolution with RGB normalization (mean and std of 0.5) and handles text with 64-token sequences.
- Shape-optimized SoViT backbone architecture
- Multilingual support for broader language coverage
- Advanced sigmoid loss function for improved performance
- 1.13 billion parameters for robust feature extraction
Core Capabilities
- Zero-shot image classification
- Image-text retrieval tasks
- Multilingual processing
- Efficient batch processing with optimized loss function
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its shape-optimized architecture and sigmoid loss function, which eliminates the need for global similarity normalization, enabling better scaling and performance at various batch sizes.
Q: What are the recommended use cases?
The model excels in zero-shot image classification and image-text retrieval tasks, particularly in multilingual contexts. It's ideal for applications requiring robust visual understanding without extensive task-specific training.
