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Resnet50 Clip Gap.openai
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Resnet50 Clip Gap.openai

Developed by timm
A ResNet50 variant based on the visual encoder part of the CLIP model, extracting image features through Global Average Pooling (GAP)
Image Classification
Transformers
Open Source License:Apache-2.0#CLIP feature extraction#Zero-shot classification#Image semantic understanding
Downloads 250
Release Time : 12/26/2024

Model Overview

This model is an implementation of the ResNet50 architecture for CLIP's visual encoder, specifically designed for image feature extraction and can serve as a foundational feature extractor for computer vision tasks

Model Features

CLIP Visual Encoder
Based on the visual encoder part of the CLIP model, with powerful cross-modal representation capabilities
Global Average Pooling
Uses Global Average Pooling (GAP) instead of fully connected layers, making it more suitable for feature extraction tasks
Pre-trained Weights
Utilizes OpenAI CLIP's pre-trained weights, providing excellent image representation capabilities

Model Capabilities

Image feature extraction
Visual representation learning

Use Cases

Computer Vision
Image Classification
Serves as a foundational feature extractor for image classification tasks
Image Retrieval
Extracts image features for similarity search and retrieval
Multimodal Learning
Combined with text models for cross-modal learning tasks
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