Crop_Disease_model_1 Open-source Crop Disease Recognition Model - Help Accurately Identify Crop Disease Conditions

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Crop Disease Model 1

Developed by vishnun0027

A crop disease recognition model fine-tuned based on Google Vision Transformer (ViT) architecture with 70% accuracy

Image Classification

Transformers

Open Source License:Apache-2.0 #Crop disease identification #ViT vision model #High-precision classification

Downloads 50

Release Time : 6/24/2024

Model Overview

This model is a fine-tuned version of the google/vit-base-patch16-224-in21k pre-trained model on a crop disease dataset, primarily used for plant disease image classification tasks

Model Features

Based on ViT architecture

Uses Vision Transformer architecture, suitable for image classification tasks

Transfer learning

Fine-tuned based on ImageNet-21k pre-trained model with good feature extraction capabilities

Medium accuracy

Achieves 70% accuracy on the evaluation set, suitable for basic disease recognition applications

Model Capabilities

Crop disease image classification

Plant health status identification

Visual feature extraction

Use Cases

Agricultural technology

Field disease detection

Identify potential diseases by photographing crop leaves

70% accuracy

Plant health monitoring

Regularly monitor the health status of crops in greenhouses or fields

🚀 Crop_Disease_model_1

This model is a fine - tuned version of [google/vit - base - patch16 - 224 - in21k](https://huggingface.co/google/vit - base - patch16 - 224 - in21k) on an unknown dataset, aiming to solve crop disease recognition problems and provide accurate prediction results.

🚀 Quick Start

This model is a fine - tuned version of [google/vit - base - patch16 - 224 - in21k](https://huggingface.co/google/vit - base - patch16 - 224 - in21k) on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.2482
Accuracy: 0.7

📚 Documentation

Model description

More information needed

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: 18

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.974	0.9787	23	2.9288	0.1573
2.8301	2.0	47	2.6713	0.5173
2.3995	2.9787	70	2.3223	0.5707
2.112	4.0	94	2.0321	0.604
1.8965	4.9787	117	1.8377	0.6133
1.6807	6.0	141	1.6895	0.6307
1.4942	6.9787	164	1.5807	0.6693
1.3849	8.0	188	1.5080	0.664
1.2975	8.9787	211	1.4605	0.6613
1.1747	10.0	235	1.3888	0.692
1.1457	10.9787	258	1.3622	0.692
1.0602	12.0	282	1.3318	0.6893
1.0296	12.9787	305	1.2968	0.7133
0.9556	14.0	329	1.2999	0.676
0.9317	14.9787	352	1.2625	0.7053
0.9134	16.0	376	1.2656	0.696
0.914	16.9787	399	1.2593	0.7013
0.9013	17.6170	414	1.2482	0.7

Framework versions

Transformers 4.41.2
Pytorch 2.1.2
Datasets 2.19.2
Tokenizers 0.19.1

📄 License

This model is licensed under the Apache 2.0 license.

Property	Details
Model Type	Fine - tuned version of google/vit - base - patch16 - 224 - in21k
License	Apache 2.0

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