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Swin Tiny Patch4 Window7 224 Cifar10
A tiny model based on Swin Transformer architecture, specifically fine-tuned for CIFAR-10 image classification tasks
Downloads 94
Release Time : 6/11/2023
Model Overview
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the CIFAR-10 dataset for image classification tasks, achieving 97.18% accuracy on the test set.
Model Features
High accuracy
Achieves 97.18% classification accuracy on the CIFAR-10 test set
Based on Swin Transformer
Utilizes advanced Swin Transformer architecture with local window attention mechanism
Lightweight model
Designed as a tiny variant, suitable for deployment in resource-constrained environments
Model Capabilities
Image classification
Multi-category recognition
Use Cases
Computer vision
CIFAR-10 image classification
Accurately classifies 10 categories of objects in the CIFAR-10 dataset
97.18% test accuracy
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