%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)
CLIP ViT B 16 DataComp.L S1b B8k
A zero-shot image classification model based on the CLIP architecture, trained using the DataComp dataset, supporting efficient image-text matching tasks.
Downloads 1,166
Release Time : 4/26/2023
Model Overview
This model is a vision-language model based on the CLIP architecture, capable of mapping images and text into the same embedding space, enabling zero-shot image classification and cross-modal retrieval.
Model Features
Zero-shot Learning Capability
Can perform image classification for new categories without task-specific fine-tuning.
Cross-modal Understanding
Capable of processing both image and text inputs, understanding the semantic relationships between them.
Efficient Inference
Optimized based on the ViT architecture, achieving high inference speed while maintaining performance.
Large-scale Pretraining
Pretrained using the DataComp.L dataset and s1B-b8K training configuration.
Model Capabilities
Image Classification
Image-Text Matching
Cross-modal Retrieval
Zero-shot Learning
Multimodal Embedding
Use Cases
Content Retrieval
Text-based Image Search
Retrieve relevant images using natural language descriptions.
Enables semantic search without predefined labels.
E-commerce
Product Categorization
Automatically categorize product images based on user descriptions.
Reduces manual labeling costs and improves classification efficiency.
Content Moderation
Inappropriate Content Detection
Automatically identify inappropriate images based on text rules.
Adapts to new types of violations without requiring retraining.
Transformers
SafetensorsFeatured Recommended AI Models
Š 2025AIbase