---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- recall
model-index:
- name: vit_fold_3
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: None
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9827586206896551
    - name: Recall
      type: recall
      value: 0.9840639240770935
---

<!-- 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_fold_3

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0572
- Accuracy: 0.9828
- F1 Score: 0.9840
- Recall: 0.9841

## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 150
- num_epochs: 100
- label_smoothing_factor: 0.15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|
| 2.9536        | 1.0   | 19   | 2.9528          | 0.3      | 0.2473   | 0.3174 |
| 2.7882        | 2.0   | 38   | 2.6926          | 0.3690   | 0.3570   | 0.3867 |
| 2.4219        | 3.0   | 57   | 2.3561          | 0.5793   | 0.5998   | 0.5921 |
| 2.0092        | 4.0   | 76   | 1.9862          | 0.7586   | 0.7827   | 0.7875 |
| 1.6129        | 5.0   | 95   | 1.6200          | 0.8724   | 0.8863   | 0.8884 |
| 1.3723        | 6.0   | 114  | 1.3893          | 0.9138   | 0.9220   | 0.9240 |
| 1.2462        | 7.0   | 133  | 1.2759          | 0.9276   | 0.9335   | 0.9325 |
| 1.1739        | 8.0   | 152  | 1.2234          | 0.9379   | 0.9427   | 0.9411 |
| 1.1189        | 9.0   | 171  | 1.1838          | 0.9448   | 0.9486   | 0.9460 |
| 1.1044        | 10.0  | 190  | 1.1554          | 0.9517   | 0.9557   | 0.9545 |
| 1.0877        | 11.0  | 209  | 1.1483          | 0.9483   | 0.9529   | 0.9533 |
| 1.0598        | 12.0  | 228  | 1.1355          | 0.9552   | 0.9591   | 0.9595 |
| 1.0458        | 13.0  | 247  | 1.1253          | 0.9586   | 0.9624   | 0.9645 |
| 1.0242        | 14.0  | 266  | 1.0983          | 0.9724   | 0.9742   | 0.9730 |
| 1.0151        | 15.0  | 285  | 1.0982          | 0.9621   | 0.9644   | 0.9606 |
| 1.0158        | 16.0  | 304  | 1.0982          | 0.9621   | 0.9656   | 0.9682 |
| 1.0131        | 17.0  | 323  | 1.0913          | 0.9655   | 0.9685   | 0.9707 |
| 1.0065        | 18.0  | 342  | 1.0878          | 0.9655   | 0.9685   | 0.9707 |
| 0.9920        | 19.0  | 361  | 1.0783          | 0.9690   | 0.9708   | 0.9680 |
| 0.9799        | 20.0  | 380  | 1.0697          | 0.9793   | 0.9808   | 0.9791 |
| 0.9830        | 21.0  | 399  | 1.0664          | 0.9793   | 0.9808   | 0.9791 |
| 0.9798        | 22.0  | 418  | 1.0630          | 0.9793   | 0.9810   | 0.9816 |
| 0.9896        | 23.0  | 437  | 1.0678          | 0.9724   | 0.9750   | 0.9767 |
| 0.9769        | 24.0  | 456  | 1.0585          | 0.9793   | 0.9809   | 0.9803 |
| 0.9736        | 25.0  | 475  | 1.0572          | 0.9828   | 0.9840   | 0.9841 |
| 0.9697        | 26.0  | 494  | 1.0557          | 0.9793   | 0.9809   | 0.9803 |
| 0.9685        | 27.0  | 513  | 1.0585          | 0.9793   | 0.9808   | 0.9791 |
| 0.9706        | 28.0  | 532  | 1.0609          | 0.9724   | 0.9749   | 0.9754 |
| 0.9833        | 29.0  | 551  | 1.0702          | 0.9724   | 0.9750   | 0.9767 |
| 0.9717        | 30.0  | 570  | 1.0728          | 0.9759   | 0.9776   | 0.9753 |
| 0.9727        | 31.0  | 589  | 1.0614          | 0.9759   | 0.9779   | 0.9779 |
| 0.9645        | 32.0  | 608  | 1.0531          | 0.9793   | 0.9809   | 0.9803 |
| 0.9730        | 33.0  | 627  | 1.0505          | 0.9828   | 0.9839   | 0.9828 |
| 0.9642        | 34.0  | 646  | 1.0454          | 0.9828   | 0.9839   | 0.9828 |
| 0.9687        | 35.0  | 665  | 1.0516          | 0.9759   | 0.9779   | 0.9779 |
| 0.9650        | 36.0  | 684  | 1.0544          | 0.9724   | 0.9748   | 0.9754 |
| 0.9651        | 37.0  | 703  | 1.0512          | 0.9793   | 0.9809   | 0.9803 |
| 0.9675        | 38.0  | 722  | 1.0516          | 0.9793   | 0.9808   | 0.9791 |
| 0.9622        | 39.0  | 741  | 1.0499          | 0.9759   | 0.9780   | 0.9792 |
| 0.9641        | 40.0  | 760  | 1.0488          | 0.9828   | 0.9839   | 0.9828 |
| 0.9640        | 41.0  | 779  | 1.0479          | 0.9828   | 0.9839   | 0.9828 |
| 0.9619        | 42.0  | 798  | 1.0464          | 0.9828   | 0.9839   | 0.9828 |
| 0.9602        | 43.0  | 817  | 1.0481          | 0.9793   | 0.9809   | 0.9803 |
| 0.9670        | 44.0  | 836  | 1.0482          | 0.9828   | 0.9839   | 0.9828 |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2