Swin Tiny Plant Disease Classification Model - Open-source and free for accurate identification of plant diseases, with an accuracy rate of 96.9%

Swin Tiny Patch4 Window7 224 Finetuned Plantdisease

Developed by gianlab

A plant disease image classification model based on the Swin Transformer architecture, fine-tuned on a plant disease dataset with an accuracy of 96.9%

Image Classification

Transformers

Open Source License:Apache-2.0 #Plant Disease Identification #High-Precision Classification #Agricultural Image Analysis

Downloads 40

Release Time : 6/24/2022

Model Overview

This model is used to identify various plant diseases, including common diseases in tomatoes, potatoes, and bell peppers, helping agricultural practitioners quickly diagnose plant health conditions

Model Features

High Accuracy

Achieves 96.9% accuracy in plant disease classification tasks

Multi-Disease Recognition

Can identify 15 different plant health conditions and disease types

Based on Swin Transformer

Utilizes advanced vision Transformer architecture with excellent feature extraction capabilities

Model Capabilities

Plant Disease Image Classification

Plant Health Condition Detection

Agricultural Disease Diagnosis

Use Cases

Agricultural Technology

Plant Disease Diagnosis

Automatically identifies potential disease types by taking photos of plant leaves

96.9% accuracy, helping farmers detect and address diseases promptly

Agricultural Research Assistance

Used for disease classification and statistics in plant pathology research

🚀 swin-tiny-patch4-window7-224-finetuned-plantdisease

This model is a fine - tuned version of microsoft/swin-tiny-patch4-window7-224 for plant disease classification, achieving high accuracy on the imagefolder dataset.

🚀 Quick Start

This model is a fine - tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.1032
Accuracy: 0.9690

📚 Documentation

Model description

This model was created by importing the dataset of the photos of diseased plants into Google Colab from kaggle here: https://www.kaggle.com/datasets/emmarex/plantdisease. I then used the image classification tutorial here: https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb

obtaining the following notebook:

https://colab.research.google.com/drive/14ItHnpARBBGaYQCiJwJsnWiiNQnlrIyP?usp=sharing

The possible classified diseases are: Tomato Tomato YellowLeaf Curl Virus , Tomato Late blight , Pepper bell Bacterial spot, Tomato Early blight, Potato healthy, Tomato healthy , Tomato Target_Spot , Potato Early blight , Tomato Tomato mosaic virus, Pepper bell healthy, Potato Late blight, Tomato Septoria leaf spot , Tomato Leaf Mold , Tomato Spider mites Two spotted spider mite , Tomato Bacterial spot .

Leaf example

leaf

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.1903	1.0	145	0.1032	0.9690

Framework versions

Transformers 4.20.1
Pytorch 1.11.0+cu113
Datasets 2.3.2
Tokenizers 0.12.1

📄 License

This model is licensed under the Apache 2.0 license.

📦 Model Information

Property	Details
Model Type	Fine - tuned swin-tiny-patch4-window7-224 for plant disease classification
Training Data	imagefolder dataset from https://www.kaggle.com/datasets/emmarex/plantdisease
Metrics	Accuracy: 0.9689922480620154 (on evaluation set)

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