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Swin Tiny Patch4 Window7 224 Finetuned Woody LeftGR Clean 130epochs
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Swin Tiny Patch4 Window7 224 Finetuned Woody LeftGR Clean 130epochs

Developed by Alex-VisTas
An image classification model based on the Swin Transformer Tiny architecture, fine-tuned on a custom image dataset for 130 epochs, with an accuracy of 90.23%.
Image Classification
Transformers
Open Source License:Apache-2.0#High-precision image classification#Swin Transformer fine-tuning#Small target recognition
Downloads 11
Release Time : 11/21/2022

Model Overview

This model is a fine-tuned version based on Microsoft's Swin Transformer Tiny architecture, specifically designed for image classification tasks. It performs excellently on the evaluation set, achieving an accuracy of 90.23%.

Model Features

High accuracy
Achieves a classification accuracy of 90.23% on the evaluation set.
Swin Transformer architecture
Based on the advanced Swin Transformer architecture, with powerful feature extraction capabilities.
Long-term training
After 130 epochs of sufficient training to ensure the model fully converges.

Model Capabilities

Image classification
Visual feature extraction

Use Cases

Industrial inspection
Wood classification
Inferred from the model name that it may be used for wood quality inspection or classification.
Accuracy 90.23%
General image recognition
Object classification
Can be used for general object classification tasks.
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