---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-plantdisease
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9689922480620154
---

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

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/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

## 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](foglia-2.png)

## 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