SigLIP Base Patch16-256
| Property | Value |
|---|---|
| Parameter Count | 203M |
| License | Apache 2.0 |
| Training Data | WebLI Dataset |
| Resolution | 256x256 |
| Paper | Sigmoid Loss for Language Image Pre-Training |
What is siglip-base-patch16-256?
SigLIP (Sigmoid Loss for Language Image Pre-Training) is an advanced vision-language model that improves upon the CLIP architecture by implementing a more efficient sigmoid loss function. This base variant, with 203M parameters, processes images in 16x16 patches at 256x256 resolution and is specifically designed for zero-shot image classification and image-text retrieval tasks.
Implementation Details
The model was trained on WebLI dataset using 16 TPU-v4 chips over three days. It processes images by normalizing them across RGB channels (mean: 0.5, std: 0.5) and handles text through tokenization with a 64-token maximum length.
- Implements patch-based image processing (16x16 patches)
- Uses sigmoid loss for better scaling capabilities
- Supports batch processing without requiring global similarity normalization
- Maintains F32 tensor precision
Core Capabilities
- Zero-shot image classification
- Image-text similarity scoring
- Efficient batch processing
- Real-time inference capabilities
Frequently Asked Questions
Q: What makes this model unique?
SigLIP's key innovation is its sigmoid loss function, which allows for better scaling and performance compared to traditional CLIP models, even with smaller batch sizes. It eliminates the need for global normalization in computing image-text similarities.
Q: What are the recommended use cases?
The model excels in zero-shot image classification and image-text retrieval tasks. It's particularly suitable for applications requiring efficient processing of image-text pairs without extensive fine-tuning.
