histo_train_swin Open-source Image Classification Model - Fine-tuned Based on Swin, with 90% Accuracy on the Evaluation Set

Histo Train Swin

Developed by tcvrishank

This model is a fine-tuned image classification model based on the Swin Transformer architecture, achieving 90% accuracy on the evaluation set.

Image Classification

Transformers

Open Source License:Apache-2.0 #Medical Image Classification #High-Precision Swin Architecture #Histopathological Analysis

Downloads 16

Release Time : 3/25/2023

Model Overview

This is an image classification model fine-tuned from microsoft/swinv2-tiny-patch4-window8-256, primarily used for histopathological image analysis tasks.

Model Features

High Accuracy

Achieves 90% classification accuracy on the evaluation set.

Efficient Architecture

Based on a lightweight variant of Swin Transformer, balancing performance and efficiency.

Transfer Learning

Fine-tuned from a pre-trained model, suitable for small-scale datasets.

Model Capabilities

Histopathological Image Classification

Medical Image Analysis

Feature Extraction

Use Cases

Medical Imaging

Pathology Slide Classification

Classify histopathological slide images.

90% accuracy

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0305	16.67	100	0.2654	0.9

Property	Details
Model Type	Fine - tuned version of microsoft/swinv2 - tiny - patch4 - window8 - 256 on imagefolder dataset
Training Data	imagefolder
Metrics	accuracy

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Histo Train Swin

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🚀 histo_train_swin

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