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Swin Tiny Patch4 Window7 224 Finetuned Eurosat
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Swin Tiny Patch4 Window7 224 Finetuned Eurosat

Developed by HekmatTaherinejad
Fine-tuned image classification model based on Swin Transformer architecture, achieving 98% accuracy on the EuroSAT dataset
Image Classification
Transformers
Open Source License:Apache-2.0#High-precision image classification#Remote sensing image recognition#Swin Transformer architecture
Downloads 16
Release Time : 7/5/2022

Model Overview

This model is a fine-tuned image classification model based on microsoft/swin-tiny-patch4-window7-224, specifically optimized for remote sensing image classification tasks

Model Features

High-precision classification
Achieves 98% accuracy on the EuroSAT dataset with excellent performance
Based on Swin Transformer
Utilizes advanced Swin Transformer architecture with powerful feature extraction capabilities
Lightweight design
Tiny version design reduces computational resource requirements while maintaining performance

Model Capabilities

Remote sensing image classification
Multi-category image recognition
High-resolution image processing

Use Cases

Remote sensing analysis
Land use classification
Classify and identify different land types in satellite images
98% accuracy
Agricultural monitoring
Identify crop types and growth conditions in farmland
Environmental monitoring
Forest coverage analysis
Monitor forest coverage changes and type distribution
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