Swin-Tiny Image Classification Model - Open source and free, with an image classification accuracy of up to 95.17%!

Swin Tiny Patch4 Window7 224 Finetuned Eurosat

Developed by ezzouhri

A fine-tuned image classification model based on Swin Transformer architecture, achieving 95.17% accuracy on image classification tasks

Image Classification

Transformers

Open Source License:Apache-2.0 #High-precision Image Classification #Remote Sensing Image Analysis #Swin-Transformer Architecture

Downloads 13

Release Time : 9/9/2022

Model Overview

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an image folder dataset, primarily used for image classification tasks.

Model Features

High Accuracy

Achieves 95.17% classification accuracy on the evaluation set

Based on Swin Transformer

Utilizes the advanced Swin Transformer architecture with excellent visual feature extraction capabilities

Fine-tuned Model

Optimized for specific tasks based on pre-trained models

Model Capabilities

Image Classification

Visual Feature Extraction

Use Cases

Remote Sensing Image Analysis

Satellite Image Classification

Classify and identify surface images captured by satellites

95.17% accuracy

General Image Recognition

Object Classification

Identify and classify common objects

Property	Details
Model Type	Fine - tuned version of microsoft/swin-tiny-patch4-window7-224
Training Data	imagefolder
Metrics	accuracy

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3432	1.0	266	0.1674	0.9517

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Swin Tiny Patch4 Window7 224 Finetuned Eurosat

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 swin-tiny-patch4-window7-224-finetuned-eurosat

🚀 Quick Start

📚 Documentation

Model Information

Training Procedure

Training hyperparameters

Training results

Framework versions

📄 License