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Openclip Resnet50 CC12M
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Openclip Resnet50 CC12M
Developed by thaottn
OpenCLIP model based on ResNet50 architecture and trained on the CC12M dataset, supporting zero-shot image classification tasks.
Downloads 13.67k
Release Time : 1/4/2024
Model Overview
This model combines the ResNet50 visual encoder with CLIP's contrastive learning framework, enabling image classification without fine-tuning.
Model Features
Zero-shot Learning Capability
Performs image classification tasks without task-specific fine-tuning.
Multimodal Understanding
Capable of processing both visual and textual information to establish cross-modal associations.
Open Source License
Released under the MIT license, allowing free use for both commercial and research purposes.
Model Capabilities
Zero-shot Image Classification
Cross-modal Retrieval
Image-Text Matching
Use Cases
Content Management
Automatic Image Tagging
Automatically generates descriptive labels for unannotated images.
Improves content management efficiency and reduces manual labeling costs.
E-commerce
Visual Search
Finds relevant product images using natural language descriptions.
Enhances user experience and conversion rates.
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