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:
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
