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Resnet50x64 Clip.openai
CLIP model based on the ResNet50x64 architecture from the OpenCLIP library, supporting zero-shot image classification tasks.
Downloads 622
Release Time : 6/9/2024
Model Overview
This model combines the ResNet50x64 deep residual network with the CLIP (Contrastive Language-Image Pretraining) framework, enabling cross-modal understanding between 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 image and text information, establishing semantic connections between them
Large-scale Pretraining
Pretrained on large-scale image-text pairs, offering broad knowledge coverage
Model Capabilities
Zero-shot Image Classification
Image-Text Matching
Cross-modal Retrieval
Use Cases
Content Moderation
Inappropriate Content Detection
Automatically identify potentially inappropriate content in images
Quickly filter potential inappropriate images, reducing manual review workload
E-commerce
Automatic Product Categorization
Automatically classify new products based on descriptions and images
No need to retrain the model for each new product category
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