---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: vit-base-kidney-stone
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8133333333333334
    - name: Precision
      type: precision
      value: 0.8451020337181513
    - name: Recall
      type: recall
      value: 0.8133333333333334
    - name: F1
      type: f1
      value: 0.8083110647337813
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 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.
It achieves the following results on the evaluation set:
- Loss: 0.6356
- Accuracy: 0.8133
- Precision: 0.8451
- Recall: 0.8133
- F1: 0.8083

## Model description

More information needed

## 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: 0.0002
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2529        | 0.33  | 100  | 0.6368          | 0.7996   | 0.8486    | 0.7996 | 0.8000 |
| 0.071         | 0.67  | 200  | 0.6456          | 0.8142   | 0.8425    | 0.8142 | 0.8020 |
| 0.032         | 1.0   | 300  | 0.6356          | 0.8133   | 0.8451    | 0.8133 | 0.8083 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.1
- Datasets 3.1.0
- Tokenizers 0.15.2