Swin-Tiny-Patch4-Window7-224-Finetuned-Eurosat Open-Source Image Classification Model

Swin Tiny Patch4 Window7 224 Finetuned Eurosat

Developed by marvind434

A fine-tuned model based on Swin Transformer Tiny architecture for image classification tasks, achieving 100% accuracy on the evaluation set

Image Classification

Transformers

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

Downloads 17

Release Time : 8/24/2022

Model Overview

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

Model Features

High Accuracy

Achieves 100% accuracy on the evaluation set

Swin Transformer Architecture

Utilizes the advanced Swin Transformer architecture with efficient local attention mechanisms

Fine-Tuning Optimization

Fine-tuned from a pre-trained model to adapt to specific image classification tasks

Model Capabilities

Image Classification

Visual Feature Extraction

Use Cases

Remote Sensing Image Analysis

EuroSAT Dataset Classification

Classifies images from the EuroSAT remote sensing dataset

Evaluation accuracy reaches 100%

General Image Classification

Custom Image Classification

Can be used for other image classification tasks

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

This model is a fine - tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset, achieving high accuracy in image classification.

🚀 Quick Start

This model is a fine - tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.3026
Accuracy: 1.0

📚 Documentation

Model Information

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

Model Index

Name: swin - tiny - patch4 - window7 - 224 - finetuned - eurosat
- Results:
  - Task:
    - Name: Image Classification
    - Type: image - classification
  - Dataset:
    - Name: imagefolder
    - Type: imagefolder
    - Config: default
    - Split: train
    - Args: default
  - Metrics:
    - Name: Accuracy
    - Type: accuracy
    - Value: 1.0

Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e - 05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training Results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	1	1.0940	0.25
No log	2.0	2	0.9836	0.25
No log	3.0	3	0.7624	0.25
No log	4.0	4	0.6527	0.5
No log	5.0	5	0.5697	0.75
No log	6.0	6	0.5167	1.0
No log	7.0	7	0.4898	0.75
No log	8.0	8	0.4572	0.75
No log	9.0	9	0.4286	0.75
0.299	10.0	10	0.3976	0.75
0.299	11.0	11	0.3678	1.0
0.299	12.0	12	0.3531	1.0
0.299	13.0	13	0.3384	1.0
0.299	14.0	14	0.3264	1.0
0.299	15.0	15	0.3188	1.0
0.299	16.0	16	0.3114	1.0
0.299	17.0	17	0.3083	1.0
0.299	18.0	18	0.3071	1.0
0.299	19.0	19	0.3041	1.0
0.2051	20.0	20	0.3026	1.0

Framework Versions

Transformers 4.21.3
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1

📄 License

This model is licensed under the Apache - 2.0 license.

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご