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# vit-base-kidney-stone-4-Jonathan_El-Beze_-w256_1k_v1-_SEC

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: 0.2940
- Accuracy: 0.9242
- Precision: 0.9321
- Recall: 0.9242
- F1: 0.9251

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1207        | 0.3333  | 100  | 0.5525          | 0.8333   | 0.8760    | 0.8333 | 0.8303 |
| 0.0178        | 0.6667  | 200  | 0.3368          | 0.8883   | 0.9298    | 0.8883 | 0.8927 |
| 0.0396        | 1.0     | 300  | 0.3187          | 0.9108   | 0.9213    | 0.9108 | 0.9104 |
| 0.0074        | 1.3333  | 400  | 1.1846          | 0.7583   | 0.8167    | 0.7583 | 0.7339 |
| 0.0125        | 1.6667  | 500  | 0.2940          | 0.9242   | 0.9321    | 0.9242 | 0.9251 |
| 0.0029        | 2.0     | 600  | 0.5031          | 0.8958   | 0.9051    | 0.8958 | 0.8929 |
| 0.0021        | 2.3333  | 700  | 0.5150          | 0.9008   | 0.9114    | 0.9008 | 0.8977 |
| 0.0016        | 2.6667  | 800  | 0.4894          | 0.9092   | 0.9191    | 0.9092 | 0.9069 |
| 0.0013        | 3.0     | 900  | 0.5048          | 0.9092   | 0.9194    | 0.9092 | 0.9067 |
| 0.0011        | 3.3333  | 1000 | 0.5066          | 0.9092   | 0.9187    | 0.9092 | 0.9070 |
| 0.001         | 3.6667  | 1100 | 0.5179          | 0.9092   | 0.9189    | 0.9092 | 0.9070 |
| 0.0008        | 4.0     | 1200 | 0.5369          | 0.9092   | 0.9198    | 0.9092 | 0.9069 |
| 0.0007        | 4.3333  | 1300 | 0.5459          | 0.9092   | 0.9198    | 0.9092 | 0.9069 |
| 0.0006        | 4.6667  | 1400 | 0.5508          | 0.9092   | 0.9198    | 0.9092 | 0.9069 |
| 0.0006        | 5.0     | 1500 | 0.5557          | 0.91     | 0.9203    | 0.91   | 0.9079 |
| 0.0005        | 5.3333  | 1600 | 0.5605          | 0.9108   | 0.9210    | 0.9108 | 0.9088 |
| 0.0004        | 5.6667  | 1700 | 0.5647          | 0.9108   | 0.9210    | 0.9108 | 0.9088 |
| 0.0004        | 6.0     | 1800 | 0.5735          | 0.9108   | 0.9210    | 0.9108 | 0.9088 |
| 0.0004        | 6.3333  | 1900 | 0.5797          | 0.9108   | 0.9210    | 0.9108 | 0.9088 |
| 0.0003        | 6.6667  | 2000 | 0.5840          | 0.9108   | 0.9210    | 0.9108 | 0.9088 |
| 0.0003        | 7.0     | 2100 | 0.5877          | 0.9108   | 0.9210    | 0.9108 | 0.9088 |
| 0.0003        | 7.3333  | 2200 | 0.5942          | 0.9108   | 0.9210    | 0.9108 | 0.9088 |
| 0.0003        | 7.6667  | 2300 | 0.6003          | 0.9117   | 0.9222    | 0.9117 | 0.9096 |
| 0.0003        | 8.0     | 2400 | 0.5999          | 0.9108   | 0.9210    | 0.9108 | 0.9088 |
| 0.0002        | 8.3333  | 2500 | 0.6042          | 0.91     | 0.9203    | 0.91   | 0.9080 |
| 0.0002        | 8.6667  | 2600 | 0.6076          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0002        | 9.0     | 2700 | 0.6098          | 0.9108   | 0.9210    | 0.9108 | 0.9088 |
| 0.0002        | 9.3333  | 2800 | 0.6135          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0002        | 9.6667  | 2900 | 0.6157          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0002        | 10.0    | 3000 | 0.6191          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0002        | 10.3333 | 3100 | 0.6216          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0002        | 10.6667 | 3200 | 0.6241          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0002        | 11.0    | 3300 | 0.6265          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0002        | 11.3333 | 3400 | 0.6291          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0001        | 11.6667 | 3500 | 0.6308          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0001        | 12.0    | 3600 | 0.6325          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0001        | 12.3333 | 3700 | 0.6339          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0001        | 12.6667 | 3800 | 0.6351          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0001        | 13.0    | 3900 | 0.6371          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0001        | 13.3333 | 4000 | 0.6376          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0001        | 13.6667 | 4100 | 0.6393          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0001        | 14.0    | 4200 | 0.6403          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0001        | 14.3333 | 4300 | 0.6410          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0001        | 14.6667 | 4400 | 0.6413          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |
| 0.0001        | 15.0    | 4500 | 0.6414          | 0.9108   | 0.9215    | 0.9108 | 0.9088 |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.6.0+cu126
- Datasets 3.1.0
- Tokenizers 0.21.0

sec-subtype_iiia sec-subtype_iia sec-subtype_ivc sec-subtype_ivd sec-subtype_ia sec-subtype_va