O

OPENCLIP SigLIP Tiny 14 Distill SigLIP 400m Cc9m

Developed by PumeTu
A lightweight vision-language model based on the SigLIP architecture, extracting knowledge from the larger SigLIP-400m model through distillation techniques, suitable for zero-shot image classification tasks.
Downloads 30
Release Time : 4/22/2025

Model Overview

This model combines the OpenCLIP framework with the SigLIP architecture, focusing on efficient zero-shot image classification. Through distillation techniques, it maintains a smaller scale while inheriting the performance of larger models.

Model Features

Lightweight design
The Tiny-14 version is optimized for deployment in resource-constrained environments
Knowledge distillation
Extracts knowledge from the larger SigLIP-400m model, balancing performance and efficiency
Zero-shot capability
Performs image classification tasks without task-specific training

Model Capabilities

Zero-shot image classification
Multimodal understanding
Vision-language alignment

Use Cases

Content management
Automatic image tagging
Automatically generates descriptive tags for unlabeled images
Improves content management efficiency and reduces manual labeling costs
E-commerce
Product categorization
Automatically classifies products based on uploaded images
Simplifies product listing processes and improves classification accuracy
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
Š 2025AIbase