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Platzi Vit Model Julio Test
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Platzi Vit Model Julio Test

Developed by platzi
This is an image classification model fine-tuned on a bean dataset based on Google's ViT model, achieving a high accuracy of 99.25% on the validation set.
Image Classification
Transformers
Open Source License:Apache-2.0#Bean Image Classification#High-precision ViT#Agricultural Quality Inspection
Downloads 18
Release Time : 3/2/2023

Model Overview

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on a bean dataset, primarily used for bean image classification tasks.

Model Features

High Accuracy
Achieved a classification accuracy of 99.25% on the bean dataset validation set.
Based on ViT Architecture
Uses the Vision Transformer architecture, which has powerful image feature extraction capabilities.
Lightweight Fine-tuning
Only requires a few epochs of fine-tuning on the pre-trained model to achieve excellent performance.

Model Capabilities

Bean Image Classification
Plant Image Recognition

Use Cases

Agriculture
Bean Variety Identification
Used for automatically identifying different varieties of bean crops.
Accuracy as high as 99.25%
Agricultural Product Quality Inspection
Can be used for grading and inspecting the quality of agricultural products.
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