V

Vit B 16 SigLIP

Developed by timm
SigLIP (Sigmoid Loss for Language Image Pre-training) model trained on the WebLI dataset for zero-shot image classification tasks.
Downloads 27.77k
Release Time : 10/16/2023

Model Overview

This model is a contrastive image-text model that uses the Sigmoid loss function for language-image pre-training and supports zero-shot image classification tasks.

Model Features

Sigmoid loss function
Uses the Sigmoid loss function for language-image pre-training, which performs better than traditional Softmax loss functions in certain tasks.
Zero-shot classification capability
Can perform image classification tasks without task-specific fine-tuning.
WebLI dataset training
Trained on the large-scale WebLI dataset, enabling broad visual concept understanding.

Model Capabilities

Image-text contrastive learning
Zero-shot image classification
Image feature extraction

Use Cases

Image classification
Food recognition
Identify types of food in images, such as donuts, beignets, etc.
Can accurately identify various food types
Animal recognition
Identify types of animals in images, such as cats, dogs, etc.
Can accurately identify common animals
Content understanding
Image content description
Understand image content and match relevant text descriptions.
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