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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
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 0.93 | 10 | 0.1208 | 0.9931 |
0.1082 | 1.95 | 21 | 0.0551 | 0.9958 |
0.1082 | 2.98 | 32 | 0.0297 | 0.9958 |
0.0342 | 4.0 | 43 | 0.0189 | 0.9986 |
0.0342 | 4.93 | 53 | 0.0156 | 0.9972 |
0.0164 | 5.95 | 64 | 0.0122 | 0.9972 |
0.0164 | 6.98 | 75 | 0.0100 | 0.9986 |
0.0099 | 8.0 | 86 | 0.0096 | 0.9986 |
0.0099 | 8.93 | 96 | 0.0090 | 0.9986 |
0.0085 | 9.3 | 100 | 0.0089 | 0.9986 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3