---
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_2
  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: 0.9551724137931035
    - name: Recall
      type: recall
      value: 0.9610994397759105
---

<!-- 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_2

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: 1.1330
- Accuracy: 0.9552
- F1 Score: 0.9591
- Recall: 0.9611

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|
| 3.0764        | 1.0   | 19   | 3.0505          | 0.2069   | 0.1685   | 0.1989 |
| 2.8915        | 2.0   | 38   | 2.8592          | 0.3276   | 0.2738   | 0.2986 |
| 2.5571        | 3.0   | 57   | 2.5948          | 0.4724   | 0.4113   | 0.4311 |
| 2.1836        | 4.0   | 76   | 2.2633          | 0.6241   | 0.6056   | 0.6021 |
| 1.7834        | 5.0   | 95   | 1.9104          | 0.7759   | 0.7743   | 0.7669 |
| 1.4444        | 6.0   | 114  | 1.6022          | 0.8448   | 0.8531   | 0.8439 |
| 1.2156        | 7.0   | 133  | 1.4031          | 0.8724   | 0.8811   | 0.8695 |
| 1.1415        | 8.0   | 152  | 1.3280          | 0.8793   | 0.8863   | 0.8743 |
| 1.0863        | 9.0   | 171  | 1.2605          | 0.9069   | 0.9147   | 0.9123 |
| 1.0580        | 10.0  | 190  | 1.2364          | 0.9207   | 0.9275   | 0.9257 |
| 1.0411        | 11.0  | 209  | 1.2385          | 0.9172   | 0.9225   | 0.9171 |
| 1.0299        | 12.0  | 228  | 1.1877          | 0.9310   | 0.9368   | 0.9355 |
| 1.0096        | 13.0  | 247  | 1.1798          | 0.9345   | 0.9406   | 0.9428 |
| 0.9902        | 14.0  | 266  | 1.1914          | 0.9241   | 0.9291   | 0.9233 |
| 0.9951        | 15.0  | 285  | 1.1893          | 0.9241   | 0.9291   | 0.9233 |
| 1.0118        | 16.0  | 304  | 1.1582          | 0.9414   | 0.9465   | 0.9477 |
| 1.0025        | 17.0  | 323  | 1.1507          | 0.9483   | 0.9525   | 0.9539 |
| 0.9899        | 18.0  | 342  | 1.1432          | 0.9448   | 0.9490   | 0.9478 |
| 0.9766        | 19.0  | 361  | 1.1452          | 0.9448   | 0.9490   | 0.9478 |
| 0.9757        | 20.0  | 380  | 1.1502          | 0.9448   | 0.9497   | 0.9515 |
| 0.9742        | 21.0  | 399  | 1.1370          | 0.9483   | 0.9522   | 0.9515 |
| 0.9779        | 22.0  | 418  | 1.1409          | 0.9414   | 0.9459   | 0.9442 |
| 0.9699        | 23.0  | 437  | 1.1304          | 0.9448   | 0.9490   | 0.9478 |
| 0.9857        | 24.0  | 456  | 1.1345          | 0.9414   | 0.9459   | 0.9442 |
| 0.9649        | 25.0  | 475  | 1.1319          | 0.9483   | 0.9524   | 0.9539 |
| 0.9665        | 26.0  | 494  | 1.1383          | 0.9448   | 0.9495   | 0.9514 |
| 0.9725        | 27.0  | 513  | 1.1625          | 0.9414   | 0.9468   | 0.9502 |
| 0.9801        | 28.0  | 532  | 1.1431          | 0.9483   | 0.9525   | 0.9539 |
| 0.9628        | 29.0  | 551  | 1.1296          | 0.9483   | 0.9524   | 0.9527 |
| 0.9617        | 30.0  | 570  | 1.1306          | 0.9448   | 0.9495   | 0.9503 |
| 0.9642        | 31.0  | 589  | 1.1330          | 0.9552   | 0.9591   | 0.9611 |
| 0.9632        | 32.0  | 608  | 1.1253          | 0.9483   | 0.9527   | 0.9525 |
| 0.9768        | 33.0  | 627  | 1.1471          | 0.9483   | 0.9528   | 0.9551 |
| 0.9642        | 34.0  | 646  | 1.1479          | 0.9483   | 0.9528   | 0.9551 |
| 0.9688        | 35.0  | 665  | 1.1393          | 0.9483   | 0.9527   | 0.9525 |
| 0.9639        | 36.0  | 684  | 1.1510          | 0.9483   | 0.9531   | 0.9562 |
| 0.9625        | 37.0  | 703  | 1.1417          | 0.9448   | 0.9497   | 0.9489 |
| 0.9624        | 38.0  | 722  | 1.1501          | 0.9517   | 0.9561   | 0.9586 |
| 0.9635        | 39.0  | 741  | 1.1514          | 0.9448   | 0.9498   | 0.9513 |
| 0.9616        | 40.0  | 760  | 1.1471          | 0.9414   | 0.9466   | 0.9478 |
| 0.9619        | 41.0  | 779  | 1.1508          | 0.9414   | 0.9468   | 0.9491 |
| 0.9585        | 42.0  | 798  | 1.1411          | 0.9448   | 0.9499   | 0.9502 |


### Framework versions

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