V

Vit B 16 SigLIP I18n 256

Developed by timm
A SigLIP (Sigmoid Loss for Language-Image Pre-training) model trained on the WebLI dataset, suitable for zero-shot image classification tasks.
Downloads 87.92k
Release Time : 10/17/2023

Model Overview

This model is a vision-language model trained based on SigLIP (Sigmoid Loss for Language-Image Pre-training), primarily used for zero-shot image classification tasks. It can map images and text into the same embedding space, enabling contrastive learning between images and text.

Model Features

Sigmoid loss function
Uses the Sigmoid loss function for language-image pre-training, which handles multi-label classification tasks better compared to traditional Softmax loss functions.
Zero-shot classification
Supports zero-shot image classification, allowing direct application to new categories without task-specific fine-tuning.
Multilingual support
The 'i18n' in the model name indicates internationalization support, enabling processing of text inputs in multiple languages.

Model Capabilities

Zero-shot image classification
Image-text contrastive learning
Multilingual text processing

Use Cases

Image classification
Zero-shot image classification
Classifies images without training, requiring only the category label text.
Accurately identifies image content and matches it to the most relevant text label.
Cross-modal retrieval
Image-text matching
Computes the similarity between images and text for retrieving relevant content.
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