Histo_train_vit Open-Source Image Classification Model - Free to Use, with 82.5% Accuracy on Evaluation Set

Histo Train Vit

Developed by tcvrishank

An image classification model fine-tuned based on google/vit-base-patch16-224, achieving an accuracy of 82.5% on the evaluation set

Image Classification

Transformers

Open Source License:Apache-2.0 #Medical Image Classification #High-Accuracy ViT #Histopathological Analysis

Downloads 18

Release Time : 3/25/2023

Model Overview

This model is an image classification model based on the Vision Transformer (ViT) architecture, specifically fine-tuned for a particular image dataset.

Model Features

High Accuracy

Achieves 82.5% accuracy on the evaluation set

Based on ViT Architecture

Uses the Vision Transformer architecture, suitable for processing image data

Fine-tuned Model

Fine-tuned on a specific dataset, suitable for professional image classification tasks

Model Capabilities

Image Classification

Feature Extraction

Use Cases

Medical Image Analysis

Histopathological Image Classification

Can be used for histopathological image classification tasks in the medical field

Accuracy 82.5%

General Image Recognition

Object Classification

Can be used for general object image classification tasks

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0481	1.67	10	0.4926	0.825
0.3714	3.33	20	0.3388	0.9
0.0642	5.0	30	0.3255	0.875
0.0199	6.67	40	0.4111	0.875
0.0074	8.33	50	0.3334	0.925
0.0024	10.0	60	0.3710	0.9
0.0131	11.67	70	0.5366	0.85
0.0067	13.33	80	0.5172	0.875
0.0152	15.0	90	0.4835	0.9
0.0058	16.67	100	0.3979	0.875
0.0005	18.33	110	0.5964	0.825
0.0008	20.0	120	0.7340	0.825

Property	Details
Model Name	histo_train_vit
Task	Image Classification
Dataset	imagefolder
Metrics	Accuracy: 0.825

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Histo Train Vit

Model Overview

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Training and evaluation data

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