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# vit-base-kidney-stone-5-Michel_Daudon_-w256_1k_v1-_SUR

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: 1.0850
- Accuracy: 0.7195
- Precision: 0.7506
- Recall: 0.7195
- F1: 0.7206

## 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2033        | 0.3333  | 100  | 1.2261          | 0.6361   | 0.6932    | 0.6361 | 0.6400 |
| 0.0929        | 0.6667  | 200  | 1.0850          | 0.7195   | 0.7506    | 0.7195 | 0.7206 |
| 0.0625        | 1.0     | 300  | 1.3736          | 0.6909   | 0.7185    | 0.6909 | 0.6945 |
| 0.1293        | 1.3333  | 400  | 1.6858          | 0.6819   | 0.7413    | 0.6819 | 0.6573 |
| 0.0786        | 1.6667  | 500  | 1.6693          | 0.6746   | 0.7054    | 0.6746 | 0.6852 |
| 0.0769        | 2.0     | 600  | 1.2500          | 0.7653   | 0.7741    | 0.7653 | 0.7659 |
| 0.0675        | 2.3333  | 700  | 1.2728          | 0.7277   | 0.7905    | 0.7277 | 0.7006 |
| 0.0577        | 2.6667  | 800  | 1.7467          | 0.6942   | 0.7236    | 0.6942 | 0.7024 |
| 0.1206        | 3.0     | 900  | 1.9383          | 0.7105   | 0.7649    | 0.7105 | 0.6852 |
| 0.0516        | 3.3333  | 1000 | 1.6047          | 0.6999   | 0.6905    | 0.6999 | 0.6914 |
| 0.0235        | 3.6667  | 1100 | 1.2994          | 0.7686   | 0.7826    | 0.7686 | 0.7676 |
| 0.0016        | 4.0     | 1200 | 1.5717          | 0.7424   | 0.7565    | 0.7424 | 0.7443 |
| 0.0015        | 4.3333  | 1300 | 1.4555          | 0.7809   | 0.7935    | 0.7809 | 0.7757 |
| 0.0276        | 4.6667  | 1400 | 1.2971          | 0.7751   | 0.7664    | 0.7751 | 0.7679 |
| 0.0132        | 5.0     | 1500 | 1.6617          | 0.7555   | 0.7683    | 0.7555 | 0.7538 |
| 0.0015        | 5.3333  | 1600 | 1.5638          | 0.7383   | 0.7585    | 0.7383 | 0.7419 |
| 0.0009        | 5.6667  | 1700 | 1.8707          | 0.7383   | 0.7490    | 0.7383 | 0.7428 |
| 0.0008        | 6.0     | 1800 | 1.8055          | 0.7539   | 0.7631    | 0.7539 | 0.7570 |
| 0.0008        | 6.3333  | 1900 | 1.9551          | 0.7294   | 0.7480    | 0.7294 | 0.7338 |
| 0.0006        | 6.6667  | 2000 | 1.9497          | 0.7318   | 0.7496    | 0.7318 | 0.7361 |
| 0.0007        | 7.0     | 2100 | 1.9260          | 0.7343   | 0.7472    | 0.7343 | 0.7380 |
| 0.0006        | 7.3333  | 2200 | 1.9289          | 0.7326   | 0.7452    | 0.7326 | 0.7360 |
| 0.0024        | 7.6667  | 2300 | 1.8358          | 0.7261   | 0.7435    | 0.7261 | 0.7333 |
| 0.0005        | 8.0     | 2400 | 1.9143          | 0.7302   | 0.7482    | 0.7302 | 0.7359 |
| 0.0004        | 8.3333  | 2500 | 1.9815          | 0.7220   | 0.7419    | 0.7220 | 0.7279 |
| 0.0181        | 8.6667  | 2600 | 2.2374          | 0.6926   | 0.7291    | 0.6926 | 0.6944 |
| 0.0004        | 9.0     | 2700 | 1.9174          | 0.7482   | 0.7919    | 0.7482 | 0.7498 |
| 0.0004        | 9.3333  | 2800 | 1.9026          | 0.7473   | 0.7795    | 0.7473 | 0.7529 |
| 0.0003        | 9.6667  | 2900 | 1.9087          | 0.7522   | 0.7823    | 0.7522 | 0.7575 |
| 0.0004        | 10.0    | 3000 | 1.9171          | 0.7514   | 0.7817    | 0.7514 | 0.7567 |
| 0.0003        | 10.3333 | 3100 | 1.9246          | 0.7539   | 0.7839    | 0.7539 | 0.7591 |
| 0.0003        | 10.6667 | 3200 | 1.9318          | 0.7539   | 0.7839    | 0.7539 | 0.7591 |
| 0.0003        | 11.0    | 3300 | 1.9402          | 0.7506   | 0.7795    | 0.7506 | 0.7562 |
| 0.0002        | 11.3333 | 3400 | 1.9475          | 0.7506   | 0.7784    | 0.7506 | 0.7560 |
| 0.0003        | 11.6667 | 3500 | 1.9540          | 0.7522   | 0.7792    | 0.7522 | 0.7574 |
| 0.0003        | 12.0    | 3600 | 1.9608          | 0.7522   | 0.7792    | 0.7522 | 0.7574 |
| 0.0003        | 12.3333 | 3700 | 1.9678          | 0.7506   | 0.7765    | 0.7506 | 0.7559 |
| 0.0002        | 12.6667 | 3800 | 1.9732          | 0.7514   | 0.7771    | 0.7514 | 0.7567 |
| 0.0002        | 13.0    | 3900 | 1.9782          | 0.7522   | 0.7773    | 0.7522 | 0.7574 |
| 0.0002        | 13.3333 | 4000 | 1.9827          | 0.7514   | 0.7763    | 0.7514 | 0.7566 |
| 0.0002        | 13.6667 | 4100 | 1.9861          | 0.7514   | 0.7759    | 0.7514 | 0.7567 |
| 0.0002        | 14.0    | 4200 | 1.9894          | 0.7506   | 0.7749    | 0.7506 | 0.7560 |
| 0.0002        | 14.3333 | 4300 | 1.9920          | 0.7506   | 0.7749    | 0.7506 | 0.7560 |
| 0.0002        | 14.6667 | 4400 | 1.9933          | 0.7498   | 0.7739    | 0.7498 | 0.7552 |
| 0.0002        | 15.0    | 4500 | 1.9939          | 0.7498   | 0.7739    | 0.7498 | 0.7552 |


### Framework versions

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

sur-subtype_iva sur-subtype_iva2 sur-subtype_ivc sur-subtype_ivd sur-subtype_ia sur-subtype_va