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Supervised Contrastive Learning Cifar10
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Supervised Contrastive Learning Cifar10

Developed by keras-io
This is a CIFAR-10 image classification model trained using supervised contrastive learning techniques, achieving a test accuracy of 81.06%
Image ClassificationOpen Source License:Apache-2.0#Supervised Contrastive Learning#Image Feature Enhancement#Few-shot Optimization
Downloads 29
Release Time : 3/2/2022

Model Overview

The model is trained on the CIFAR-10 dataset using supervised contrastive learning and can classify images into 10 common categories (e.g., airplanes, cars, birds, etc.). Compared to traditional methods, contrastive learning improves model performance.

Model Features

Supervised Contrastive Learning
Adopts the supervised contrastive learning method proposed by Prannay Khosla et al., which learns more discriminative feature representations compared to traditional classification methods
Performance Improvement
The contrastive learning method enables the model to achieve a test accuracy of 81.06% on CIFAR-10, an improvement over traditional methods (79.88%)
Lightweight Implementation
Implemented with tf-keras for easy deployment and integration

Model Capabilities

Image Classification
Feature Extraction

Use Cases

Computer Vision
Object Recognition
Identify common object categories in images
Achieves 81.06% accuracy on the CIFAR-10 dataset
Educational Demonstration
Used for teaching the application of contrastive learning in computer vision
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