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Resnet50x4 Clip Gap.openai
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Resnet50x4 Clip Gap.openai

Developed by timm
ResNet50x4 variant model based on the CLIP framework, designed for image feature extraction
Image Classification
Transformers
Open Source License:Apache-2.0#CLIP visual encoding#Multimodal pre-training#Zero-shot classification
Downloads 170
Release Time : 12/26/2024

Model Overview

This model serves as the image encoder component in the CLIP framework, utilizing the ResNet50x4 architecture with Global Average Pooling (GAP) to output feature vectors, suitable for image representation learning tasks

Model Features

CLIP framework compatibility
As a visual encoder component of the CLIP model, it can be used in conjunction with text encoders
Deep residual architecture
Based on the ResNet50x4 architecture, providing enhanced feature extraction capabilities
Global pooling output
Utilizes Global Average Pooling (GAP) to generate fixed-length image feature vectors

Model Capabilities

Image feature extraction
Visual representation learning
Image embedding generation

Use Cases

Computer vision
Image retrieval
Enables similar image search through extracted image feature vectors
Multimodal learning
Serves as a visual encoder combined with text models to build cross-modal systems
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