---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-RU4-40
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7833333333333333
---

<!-- 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-patch16-224-RU4-40

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: 49869186446092573277078519432609792.0000
- Accuracy: 0.7833

## 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: 5.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 40

### Training results

| Training Loss                            | Epoch | Step | Validation Loss                          | Accuracy |
|:----------------------------------------:|:-----:|:----:|:----------------------------------------:|:--------:|
| 62336480581735638025587599492513792.0000 | 0.99  | 19   | 49869186446092573277078519432609792.0000 | 0.4667   |
| 58440439651504818626990770153848832.0000 | 1.97  | 38   | 49869186446092573277078519432609792.0000 | 0.6      |
| 50648389482308178156834519410278400.0000 | 2.96  | 57   | 49869186446092573277078519432609792.0000 | 0.7333   |
| 56102820639337698928052607882625024.0000 | 4.0   | 77   | 49869186446092573277078519432609792.0000 | 0.75     |
| 62336476620327508623021905073405952.0000 | 4.99  | 96   | 49869186446092573277078519432609792.0000 | 0.7667   |
| 56882041501889867672610159036727296.0000 | 5.97  | 115  | 49869186446092573277078519432609792.0000 | 0.75     |
| 51817195026983603992051887699394560.0000 | 6.96  | 134  | 49869186446092573277078519432609792.0000 | 0.75     |
| 52986004533067168453206987262394368.0000 | 8.0   | 154  | 49869186446092573277078519432609792.0000 | 0.6667   |
| 55713231995806303211455059473727488.0000 | 8.99  | 173  | 49869186446092573277078519432609792.0000 | 0.6833   |
| 54934019056070393272028897157840896.0000 | 9.97  | 192  | 49869186446092573277078519432609792.0000 | 0.7833   |
| 51427598460635967917067024161832960.0000 | 10.96 | 211  | 49869186446092573277078519432609792.0000 | 0.7167   |
| 51817198988391733394617582118502400.0000 | 12.0  | 231  | 49869186446092573277078519432609792.0000 | 0.7      |
| 57661238595993278448517617385734144.0000 | 12.99 | 250  | 49869186446092573277078519432609792.0000 | 0.7333   |
| 58050851007973422910393221744951296.0000 | 13.97 | 269  | 49869186446092573277078519432609792.0000 | 0.75     |
| 56882045463297987851803816601059328.0000 | 14.96 | 288  | 49869186446092573277078519432609792.0000 | 0.7333   |
| 56102844407786447673330663832944640.0000 | 16.0  | 308  | 49869186446092573277078519432609792.0000 | 0.75     |
| 56492437012725972792493906660950016.0000 | 16.99 | 327  | 49869186446092573277078519432609792.0000 | 0.7833   |
| 51817195026983603992051887699394560.0000 | 17.97 | 346  | 49869186446092573277078519432609792.0000 | 0.7667   |
| 48310774431549178637090014703386624.0000 | 18.96 | 365  | 49869186446092573277078519432609792.0000 | 0.7667   |
| 58440451535729188387943779701620736.0000 | 20.0  | 385  | 49869186446092573277078519432609792.0000 | 0.7667   |
| 51037990010063943634385077366947840.0000 | 20.99 | 404  | 49869186446092573277078519432609792.0000 | 0.7667   |
| 52206811400371877856493186477719552.0000 | 21.97 | 423  | 49869186446092573277078519432609792.0000 | 0.7333   |
| 54934023017478513451222554722172928.0000 | 22.96 | 442  | 49869186446092573277078519432609792.0000 | 0.75     |
| 58830048102076833686300680093958144.0000 | 24.0  | 462  | 49869186446092573277078519432609792.0000 | 0.75     |
| 52986004533067168453206987262394368.0000 | 24.99 | 481  | 49869186446092573277078519432609792.0000 | 0.7667   |
| 54544418528314618571106302346395648.0000 | 25.97 | 500  | 49869186446092573277078519432609792.0000 | 0.7667   |
| 51644037916400559103047539831603200.0000 | 26.96 | 519  | 49869186446092573277078519432609792.0000 | 0.75     |
| 56102840446378327494137006268612608.0000 | 28.0  | 539  | 49869186446092573277078519432609792.0000 | 0.7667   |
| 59998853646752268744890085237850112.0000 | 28.99 | 558  | 49869186446092573277078519432609792.0000 | 0.7833   |
| 51427598460635967917067024161832960.0000 | 29.97 | 577  | 49869186446092573277078519432609792.0000 | 0.7833   |
| 53375620906455442317648286040719360.0000 | 30.96 | 596  | 49869186446092573277078519432609792.0000 | 0.7667   |
| 58830044140668713507107022529626112.0000 | 32.0  | 616  | 49869186446092573277078519432609792.0000 | 0.75     |
| 52596404005311393752284392450949120.0000 | 32.99 | 635  | 49869186446092573277078519432609792.0000 | 0.7333   |
| 57661234634585149045951922966626304.0000 | 33.97 | 654  | 49869186446092573277078519432609792.0000 | 0.7833   |
| 50648393443716298336028176974610432.0000 | 34.96 | 673  | 49869186446092573277078519432609792.0000 | 0.7667   |
| 56882045463297987851803816601059328.0000 | 36.0  | 693  | 49869186446092573277078519432609792.0000 | 0.7667   |
| 55713231995806303211455059473727488.0000 | 36.99 | 712  | 49869186446092573277078519432609792.0000 | 0.7833   |
| 51427594499227838514501329742725120.0000 | 37.97 | 731  | 49869186446092573277078519432609792.0000 | 0.7833   |
| 52596407966719523154850086870056960.0000 | 38.96 | 750  | 49869186446092573277078519432609792.0000 | 0.7833   |
| 53505475864816321566964104575320064.0000 | 39.48 | 760  | 49869186446092573277078519432609792.0000 | 0.7833   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0