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metadata
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

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

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

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 .

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