# Vit-model-julio-test: An Open - source Image Classification Model Based on the ViT architecture, it achieves an accuracy of 97.74% on the validation set.

Vit Model Julio Test

Developed by osarez-group

This model is an image classification model fine-tuned on the beans dataset based on Google's ViT architecture, achieving 97.74% accuracy on the validation set.

Image Classification

Transformers

Open Source License:Apache-2.0 #Plant disease identification #High-precision classification #Agricultural image analysis

Downloads 18

Release Time : 3/2/2023

Model Overview

This is an image classification model based on the Vision Transformer (ViT) architecture, specifically fine-tuned for the beans dataset to identify the health status of legume plants.

Model Features

High accuracy

Achieved 97.74% classification accuracy on the beans validation set

Based on ViT architecture

Uses the Vision Transformer architecture to effectively capture global features in images

Few-shot fine-tuning

Requires only a small amount of training data to achieve good performance

Model Capabilities

Image classification

Plant health status identification

Use Cases

Agriculture

Legume plant disease detection

Automatically identifies the health status of legume plants and detects potential diseases

Validation accuracy 97.74%

Property	Details
Model Type	Fine - tuned version of google/vit - base - patch16 - 224 - in21k
Training Data	beans dataset
Metrics	accuracy
License	apache - 2.0

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0881	3.85	500	0.1218	0.9774

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Vit Model Julio Test

Model Overview

Model Features

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