---
library_name: transformers
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-Michel_Daudon_-w256_1k_v1-_MIX
  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: 0.83375
    - name: Precision
      type: precision
      value: 0.8588680878951838
    - name: Recall
      type: recall
      value: 0.83375
    - name: F1
      type: f1
      value: 0.8355968544321966
---

<!-- 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-Michel_Daudon_-w256_1k_v1-_MIX

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: 0.4940
- Accuracy: 0.8337
- Precision: 0.8589
- Recall: 0.8337
- F1: 0.8356

## 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: 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     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1919        | 0.3333  | 100  | 0.4940          | 0.8337   | 0.8589    | 0.8337 | 0.8356 |
| 0.1697        | 0.6667  | 200  | 0.6993          | 0.8092   | 0.8485    | 0.8092 | 0.8059 |
| 0.1514        | 1.0     | 300  | 0.5555          | 0.8442   | 0.8565    | 0.8442 | 0.8443 |
| 0.0991        | 1.3333  | 400  | 0.5918          | 0.8467   | 0.8741    | 0.8467 | 0.8453 |
| 0.0415        | 1.6667  | 500  | 0.6080          | 0.8558   | 0.8690    | 0.8558 | 0.8553 |
| 0.1112        | 2.0     | 600  | 0.9788          | 0.7983   | 0.8485    | 0.7983 | 0.8028 |
| 0.0658        | 2.3333  | 700  | 1.0272          | 0.8004   | 0.8310    | 0.8004 | 0.8002 |
| 0.0977        | 2.6667  | 800  | 0.6861          | 0.8479   | 0.8570    | 0.8479 | 0.8482 |
| 0.03          | 3.0     | 900  | 0.8317          | 0.8025   | 0.8225    | 0.8025 | 0.8048 |
| 0.0253        | 3.3333  | 1000 | 0.8574          | 0.8242   | 0.8408    | 0.8242 | 0.8254 |
| 0.0564        | 3.6667  | 1100 | 0.8591          | 0.8392   | 0.8513    | 0.8392 | 0.8343 |
| 0.0285        | 4.0     | 1200 | 1.3453          | 0.7512   | 0.8090    | 0.7512 | 0.7484 |
| 0.002         | 4.3333  | 1300 | 0.9746          | 0.8192   | 0.8381    | 0.8192 | 0.8227 |
| 0.0214        | 4.6667  | 1400 | 0.7404          | 0.8646   | 0.8641    | 0.8646 | 0.8572 |
| 0.0282        | 5.0     | 1500 | 1.0063          | 0.8233   | 0.8486    | 0.8233 | 0.8219 |
| 0.03          | 5.3333  | 1600 | 1.0066          | 0.8025   | 0.8376    | 0.8025 | 0.8058 |
| 0.028         | 5.6667  | 1700 | 1.1451          | 0.8108   | 0.8325    | 0.8108 | 0.8067 |
| 0.0078        | 6.0     | 1800 | 1.0700          | 0.805    | 0.8220    | 0.805  | 0.8045 |
| 0.0008        | 6.3333  | 1900 | 1.0180          | 0.8146   | 0.8303    | 0.8146 | 0.8165 |
| 0.0008        | 6.6667  | 2000 | 0.9882          | 0.8246   | 0.8401    | 0.8246 | 0.8236 |
| 0.0006        | 7.0     | 2100 | 1.0366          | 0.8283   | 0.8424    | 0.8283 | 0.8270 |
| 0.0009        | 7.3333  | 2200 | 1.1136          | 0.8121   | 0.8309    | 0.8121 | 0.8143 |
| 0.0068        | 7.6667  | 2300 | 1.0873          | 0.8117   | 0.8128    | 0.8117 | 0.8015 |
| 0.0006        | 8.0     | 2400 | 0.8601          | 0.8325   | 0.8383    | 0.8325 | 0.8292 |
| 0.0187        | 8.3333  | 2500 | 0.9700          | 0.8258   | 0.8375    | 0.8258 | 0.8241 |
| 0.0005        | 8.6667  | 2600 | 0.8825          | 0.8175   | 0.8339    | 0.8175 | 0.8199 |
| 0.0005        | 9.0     | 2700 | 1.0314          | 0.8242   | 0.8455    | 0.8242 | 0.8230 |
| 0.0004        | 9.3333  | 2800 | 1.0323          | 0.8233   | 0.8443    | 0.8233 | 0.8230 |
| 0.0003        | 9.6667  | 2900 | 1.0397          | 0.8229   | 0.8433    | 0.8229 | 0.8229 |
| 0.0003        | 10.0    | 3000 | 1.0473          | 0.8237   | 0.8437    | 0.8237 | 0.8239 |
| 0.0003        | 10.3333 | 3100 | 1.0536          | 0.8229   | 0.8428    | 0.8229 | 0.8233 |
| 0.0003        | 10.6667 | 3200 | 1.0605          | 0.8229   | 0.8429    | 0.8229 | 0.8234 |
| 0.0003        | 11.0    | 3300 | 1.0667          | 0.8229   | 0.8429    | 0.8229 | 0.8234 |
| 0.0002        | 11.3333 | 3400 | 1.0711          | 0.8237   | 0.8436    | 0.8237 | 0.8243 |
| 0.0002        | 11.6667 | 3500 | 1.0750          | 0.8246   | 0.8441    | 0.8246 | 0.8251 |
| 0.0002        | 12.0    | 3600 | 1.0804          | 0.825    | 0.8443    | 0.825  | 0.8257 |
| 0.0002        | 12.3333 | 3700 | 1.0839          | 0.825    | 0.8440    | 0.825  | 0.8257 |
| 0.0002        | 12.6667 | 3800 | 1.0875          | 0.8246   | 0.8436    | 0.8246 | 0.8253 |
| 0.0002        | 13.0    | 3900 | 1.0909          | 0.8246   | 0.8436    | 0.8246 | 0.8253 |
| 0.0002        | 13.3333 | 4000 | 1.0930          | 0.8246   | 0.8436    | 0.8246 | 0.8253 |
| 0.0002        | 13.6667 | 4100 | 1.0954          | 0.8237   | 0.8429    | 0.8237 | 0.8246 |
| 0.0002        | 14.0    | 4200 | 1.0975          | 0.8237   | 0.8429    | 0.8237 | 0.8246 |
| 0.0002        | 14.3333 | 4300 | 1.0988          | 0.8237   | 0.8429    | 0.8237 | 0.8246 |
| 0.0002        | 14.6667 | 4400 | 1.0997          | 0.8237   | 0.8429    | 0.8237 | 0.8246 |
| 0.0002        | 15.0    | 4500 | 1.1000          | 0.8237   | 0.8429    | 0.8237 | 0.8246 |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.6.0+cu126
- Datasets 3.2.0
- Tokenizers 0.21.0