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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: swin-tiny-patch4-window7-224_ft_mango_leaf_disease |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9986111111111111 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# swin-tiny-patch4-window7-224_ft_mango_leaf_disease |
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This model was trained from scratch on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0089 |
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- Accuracy: 0.9986 |
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## Model description |
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Multiclass image classification model based on [swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) and fine-tuned with Mango🥭 Leaf🍃🍂 Disease Dataset. |
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Model was trained on 8 classes based on mango leaves health : |
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Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, Sooty Mould, Healthy |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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Traning and evaluation data are from this Kaggle dataset [Mango🥭 Leaf🍃🍂 Disease Dataset](https://www.kaggle.com/datasets/aryashah2k/mango-leaf-disease-dataset). |
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Amount of images used was 90% of total images (3600 of 4000, 450 images from each class). |
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## Training procedure |
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Dataset split : 75% train set, 20% validation set, 5% test set. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 143 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 0.93 | 10 | 0.1208 | 0.9931 | |
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| 0.1082 | 1.95 | 21 | 0.0551 | 0.9958 | |
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| 0.1082 | 2.98 | 32 | 0.0297 | 0.9958 | |
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| 0.0342 | 4.0 | 43 | 0.0189 | 0.9986 | |
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| 0.0342 | 4.93 | 53 | 0.0156 | 0.9972 | |
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| 0.0164 | 5.95 | 64 | 0.0122 | 0.9972 | |
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| 0.0164 | 6.98 | 75 | 0.0100 | 0.9986 | |
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| 0.0099 | 8.0 | 86 | 0.0096 | 0.9986 | |
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| 0.0099 | 8.93 | 96 | 0.0090 | 0.9986 | |
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| 0.0085 | 9.3 | 100 | 0.0089 | 0.9986 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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