swin-tiny-patch4-window7-224_ft_mango_leaf_disease

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swin-tiny-patch4-window7-224_ft_mango_leaf_disease

This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.0089
• Accuracy: 0.9986

Model description

Multiclass image classification model based on swin-tiny-patch4-window7-224 and fine-tuned with Mango🥭 Leaf🍃🍂 Disease Dataset. Model was trained on 8 classes based on mango leaves health : Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, Sooty Mould, Healthy

Intended uses & limitations

More information needed

Training and evaluation data

Traning and evaluation data are from this Kaggle dataset Mango🥭 Leaf🍃🍂 Disease Dataset. Amount of images used was 90% of total images (3600 of 4000, 450 images from each class).

Training procedure

Dataset split : 75% train set, 20% validation set, 5% test set.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 143
• gradient_accumulation_steps: 4
• total_train_batch_size: 256
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10

Training results

Framework versions

• Transformers 4.33.3
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.13.3