| ---
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| license: apache-2.0
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| base_model: google/vit-base-patch16-224-in21k
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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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| - precision
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| - recall
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| - f1
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| model-index:
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| - name: vit-base-kidney-stone
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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: test
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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: 0.8133333333333334
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| - name: Precision
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| type: precision
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| value: 0.8451020337181513
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| - name: Recall
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| type: recall
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| value: 0.8133333333333334
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| - name: F1
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| type: f1
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| value: 0.8083110647337813
|
| ---
|
|
|
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| should probably proofread and complete it, then remove this comment. -->
|
|
|
| # vit-base-kidney-stone
|
|
|
| This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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| It achieves the following results on the evaluation set:
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| - Loss: 0.6356
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| - Accuracy: 0.8133
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| - Precision: 0.8451
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| - Recall: 0.8133
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| - F1: 0.8083
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|
|
| ## Model description
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|
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| More information needed
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|
|
| ## Intended uses & limitations
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|
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| More information needed
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|
|
| ## Training and evaluation data
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|
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| More information needed
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|
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| ## Training procedure
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|
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| ### Training hyperparameters
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|
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| The following hyperparameters were used during training:
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| - learning_rate: 0.0002
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| - train_batch_size: 32
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| - eval_batch_size: 8
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| - seed: 42
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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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| - num_epochs: 1
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| - mixed_precision_training: Native AMP
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|
|
| ### Training results
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|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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| | 0.2529 | 0.33 | 100 | 0.6368 | 0.7996 | 0.8486 | 0.7996 | 0.8000 |
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| | 0.071 | 0.67 | 200 | 0.6456 | 0.8142 | 0.8425 | 0.8142 | 0.8020 |
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| | 0.032 | 1.0 | 300 | 0.6356 | 0.8133 | 0.8451 | 0.8133 | 0.8083 |
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|
|
| ### Framework versions
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|
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| - Transformers 4.37.2
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| - Pytorch 2.1.1
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| - Datasets 3.1.0
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| - Tokenizers 0.15.2
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|
|
