V

Vit B 16 SigLIP 512

Developed by timm
SigLIP (Sigmoid Loss Language-Image Pretraining) model trained on the WebLI dataset for zero-shot image classification tasks
Downloads 3,787
Release Time : 10/16/2023

Model Overview

This is a contrastive image-text model that uses Sigmoid loss for language-image pretraining, particularly suitable for zero-shot image classification tasks. The model was converted from a JAX checkpoint to PyTorch format and can be used in OpenCLIP and timm.

Model Features

Sigmoid Loss Function
Uses Sigmoid loss instead of traditional Softmax loss for language-image pretraining, improving model performance
Zero-shot Classification Capability
Can be directly applied to new image classification tasks without task-specific fine-tuning
Multi-framework Support
Supports both OpenCLIP (image + text) and timm (image only) frameworks

Model Capabilities

Zero-shot Image Classification
Image Feature Extraction
Text Feature Extraction
Image-Text Matching

Use Cases

Image Recognition
Food Recognition
Identify food categories in images, such as donuts, beignets, etc.
Can output probability distributions for each category
Content Moderation
Inappropriate Content Detection
Detect whether an image contains specific categories of inappropriate content
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