---
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: fold_1
  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: 0.9793103448275862
    - name: Recall
      type: recall
      value: 0.9803921568627452
---

<!-- 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. -->

# fold_1

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: 1.0725
- Accuracy: 0.9793
- F1 Score: 0.9805
- Recall: 0.9804

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|
| 2.9237        | 1.0   | 19   | 2.7962          | 0.3138   | 0.2811   | 0.3322 |
| 2.7282        | 2.0   | 38   | 2.5914          | 0.4172   | 0.3767   | 0.4048 |
| 2.4043        | 3.0   | 57   | 2.3162          | 0.6034   | 0.5791   | 0.5798 |
| 2.0554        | 4.0   | 76   | 1.9844          | 0.7552   | 0.7492   | 0.7470 |
| 1.6234        | 5.0   | 95   | 1.6522          | 0.8276   | 0.8322   | 0.8284 |
| 1.3940        | 6.0   | 114  | 1.4163          | 0.9069   | 0.9144   | 0.9180 |
| 1.2166        | 7.0   | 133  | 1.2960          | 0.9345   | 0.9396   | 0.9426 |
| 1.1550        | 8.0   | 152  | 1.2153          | 0.9586   | 0.9616   | 0.9608 |
| 1.1312        | 9.0   | 171  | 1.1790          | 0.9621   | 0.9641   | 0.9632 |
| 1.0838        | 10.0  | 190  | 1.1564          | 0.9690   | 0.9706   | 0.9706 |
| 1.0738        | 11.0  | 209  | 1.1289          | 0.9655   | 0.9673   | 0.9669 |
| 1.0487        | 12.0  | 228  | 1.1151          | 0.9690   | 0.9711   | 0.9706 |
| 1.0299        | 13.0  | 247  | 1.1217          | 0.9724   | 0.9745   | 0.9767 |
| 1.0164        | 14.0  | 266  | 1.1016          | 0.9759   | 0.9774   | 0.9779 |
| 0.9999        | 15.0  | 285  | 1.1021          | 0.9621   | 0.9647   | 0.9632 |
| 0.9976        | 16.0  | 304  | 1.0900          | 0.9724   | 0.9744   | 0.9755 |
| 0.9823        | 17.0  | 323  | 1.0973          | 0.9586   | 0.9614   | 0.9596 |
| 0.9854        | 18.0  | 342  | 1.0897          | 0.9621   | 0.9644   | 0.9620 |
| 0.9863        | 19.0  | 361  | 1.0825          | 0.9759   | 0.9774   | 0.9779 |
| 0.9700        | 20.0  | 380  | 1.0744          | 0.9759   | 0.9774   | 0.9779 |
| 0.9759        | 21.0  | 399  | 1.0824          | 0.9655   | 0.9677   | 0.9657 |
| 0.9796        | 22.0  | 418  | 1.0725          | 0.9793   | 0.9805   | 0.9804 |
| 0.9706        | 23.0  | 437  | 1.0655          | 0.9724   | 0.9731   | 0.9729 |
| 0.9635        | 24.0  | 456  | 1.0727          | 0.9759   | 0.9759   | 0.9742 |
| 0.9672        | 25.0  | 475  | 1.0723          | 0.9724   | 0.9731   | 0.9729 |
| 0.9662        | 26.0  | 494  | 1.0667          | 0.9724   | 0.9731   | 0.9729 |
| 0.9655        | 27.0  | 513  | 1.0770          | 0.9724   | 0.9730   | 0.9717 |
| 0.9641        | 28.0  | 532  | 1.0689          | 0.9759   | 0.9762   | 0.9766 |
| 0.9675        | 29.0  | 551  | 1.0696          | 0.9690   | 0.9698   | 0.9680 |
| 0.9609        | 30.0  | 570  | 1.0618          | 0.9690   | 0.9700   | 0.9693 |
| 0.9661        | 31.0  | 589  | 1.0660          | 0.9724   | 0.9731   | 0.9729 |
| 0.9630        | 32.0  | 608  | 1.0684          | 0.9724   | 0.9731   | 0.9729 |
| 0.9706        | 33.0  | 627  | 1.0765          | 0.9724   | 0.9730   | 0.9717 |
| 0.9575        | 34.0  | 646  | 1.0753          | 0.9655   | 0.9669   | 0.9668 |
| 0.9607        | 35.0  | 665  | 1.0766          | 0.9690   | 0.9700   | 0.9693 |
| 0.9589        | 36.0  | 684  | 1.0685          | 0.9759   | 0.9761   | 0.9754 |
| 0.9569        | 37.0  | 703  | 1.0672          | 0.9759   | 0.9761   | 0.9754 |
| 0.9572        | 38.0  | 722  | 1.0670          | 0.9724   | 0.9732   | 0.9742 |
| 0.9606        | 39.0  | 741  | 1.0680          | 0.9690   | 0.9700   | 0.9693 |
| 0.9552        | 40.0  | 760  | 1.0706          | 0.9724   | 0.9732   | 0.9729 |


### Framework versions

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