AIbase
EN
Home
Resnet50x4 Clip.openai
R

Resnet50x4 Clip.openai

Developed by timm
ResNet50x4 vision-language model based on CLIP architecture, supporting zero-shot image classification tasks
Image-to-Text
Safetensors
Open Source License:MIT#Zero-shot Image Classification#Multimodal Understanding#High-precision Visual Model
Downloads 2,303
Release Time : 6/9/2024

Model Overview

This model combines the visual encoder of ResNet50x4 with CLIP's contrastive learning framework, enabling cross-modal understanding of images and text, particularly suitable for zero-shot image classification scenarios.

Model Features

Zero-shot Learning Capability
Classify new categories without requiring specific training data
Cross-modal Understanding
Capable of processing both visual and textual information, establishing semantic connections between them
Large-scale Pretraining
Pretrained on large-scale image-text pairs, offering strong generalization capabilities

Model Capabilities

Zero-shot Image Classification
Image-Text Matching
Cross-modal Retrieval

Use Cases

Content Moderation
Prohibited Content Identification
Identify newly emerging prohibited content types without pre-collecting samples
E-commerce
Automatic Product Categorization
Automatically categorize new product images based on their descriptions
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
English简体中文繁體中文にほんご
© 2025AIbase