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Swin Tiny Patch4 Window7 224 Finetuned Eurosat
This is a tiny version of the image classification model based on the Swin Transformer architecture, fine-tuned on the EuroSAT dataset, suitable for remote sensing image classification tasks.
Downloads 11
Release Time : 12/7/2022
Model Overview
This model is a tiny version based on the Swin Transformer architecture, specifically optimized for remote sensing image classification tasks. After fine-tuning on the EuroSAT dataset, it can efficiently and accurately identify different types of satellite images.
Model Features
High accuracy
Achieved an accuracy of 98.52% on the evaluation set, showing excellent performance.
Efficient architecture
Adopts the tiny version of Swin Transformer, reducing the computational resource requirements while maintaining performance.
Remote sensing image optimization
Specifically optimized for satellite and remote sensing image classification tasks.
Model Capabilities
Satellite image classification
Remote sensing image recognition
Multi-class image classification
Use Cases
Remote sensing analysis
Land cover classification
Identify different land cover types in satellite images, such as forests, farmlands, cities, etc.
The accuracy can reach 98.52%
Environmental monitoring
Monitor surface changes, such as deforestation, urban expansion, etc.
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