%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
OPENCLIP SigLIP Tiny 14 Distill SigLIP 400m Cc9m
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
TransformersFeatured Recommended AI Models
Š 2025AIbase