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Resnet101 Clip Gap.openai
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Resnet101 Clip Gap.openai

Developed by timm
ResNet101 image encoder based on CLIP framework, extracting image features through Global Average Pooling (GAP)
Image Classification
Transformers
Open Source License:Apache-2.0#CLIP visual encoding#Image feature extraction#Zero-shot learning
Downloads 104
Release Time : 12/26/2024

Model Overview

This model serves as the image encoder component in the CLIP framework, utilizing ResNet101 architecture with Global Average Pooling (GAP) for image feature extraction, suitable for visual representation learning tasks

Model Features

CLIP framework compatibility
As the image encoder component of the CLIP framework, it can be used in conjunction with text encoders
Global Average Pooling
Uses GAP layer to extract global image features, suitable for downstream vision tasks
ResNet101 backbone
Based on deep residual network architecture with powerful feature extraction capabilities

Model Capabilities

Image feature extraction
Visual representation learning
Image classification

Use Cases

Computer vision
Image retrieval
Performing similar image retrieval using extracted image features
Visual representation learning
Used as a pre-trained model for downstream vision tasks
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