OPENCLIP SigLIP Tiny 14 Distill SigLIP 400m Cc9m
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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
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