---
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-ve-U13b-80RX1
  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: 0.8043478260869565
---

<!-- 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-ve-U13b-80RX1

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: 25847261397527471458109882368.0000
- Accuracy: 0.8043

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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 |
|:----------------------------------:|:-----:|:----:|:----------------------------------:|:--------:|
| 62421131753362939742087282688.0000 | 0.99  | 51   | 25847261397527471458109882368.0000 | 0.3696   |
| 68366009359745128248184406016.0000 | 2.0   | 103  | 25847261397527471458109882368.0000 | 0.5435   |
| 56476265480660303086264254464.0000 | 2.99  | 154  | 25847261397527471458109882368.0000 | 0.7174   |
| 59448700505958211923205947392.0000 | 4.0   | 206  | 25847261397527471458109882368.0000 | 0.7391   |
| 57962481104362666995704922112.0000 | 4.99  | 257  | 25847261397527471458109882368.0000 | 0.7391   |
| 56476269258553484104324612096.0000 | 6.0   | 309  | 25847261397527471458109882368.0000 | 0.7174   |
| 60934916129660575832646615040.0000 | 6.99  | 360  | 25847261397527471458109882368.0000 | 0.8043   |
| 69852217427661121325411336192.0000 | 8.0   | 412  | 25847261397527471458109882368.0000 | 0.7174   |
| 59448704283851401737359327232.0000 | 8.99  | 463  | 25847261397527471458109882368.0000 | 0.7609   |
| 81741965084639127505391845376.0000 | 10.0  | 515  | 25847261397527471458109882368.0000 | 0.7609   |
| 57962484882255848013765279744.0000 | 10.99 | 566  | 25847261397527471458109882368.0000 | 0.7609   |
| 57962473548576287367398162432.0000 | 12.0  | 618  | 25847261397527471458109882368.0000 | 0.8043   |
| 57962484882255848013765279744.0000 | 12.99 | 669  | 25847261397527471458109882368.0000 | 0.7391   |
| 60934916129660575832646615040.0000 | 14.0  | 721  | 25847261397527471458109882368.0000 | 0.8043   |
| 57962477326469477181551542272.0000 | 14.99 | 772  | 25847261397527471458109882368.0000 | 0.7826   |
| 50531391652171295598227488768.0000 | 16.0  | 824  | 25847261397527471458109882368.0000 | 0.7826   |
| 57962481104362666995704922112.0000 | 16.99 | 875  | 25847261397527471458109882368.0000 | 0.7174   |
| 47558960404766567779346153472.0000 | 18.0  | 927  | 25847261397527471458109882368.0000 | 0.7174   |
| 68366001803958757415970668544.0000 | 18.99 | 978  | 25847261397527471458109882368.0000 | 0.7826   |
| 60439517218248428756995670016.0000 | 20.0  | 1030 | 25847261397527471458109882368.0000 | 0.7174   |
| 75797095034043309831508459520.0000 | 20.99 | 1081 | 25847261397527471458109882368.0000 | 0.7391   |
| 59448700505958211923205947392.0000 | 22.0  | 1133 | 25847261397527471458109882368.0000 | 0.7609   |
| 63907354932851674483741687808.0000 | 22.99 | 1184 | 25847261397527471458109882368.0000 | 0.7609   |
| 78769533837234408482603532288.0000 | 24.0  | 1236 | 25847261397527471458109882368.0000 | 0.7609   |
| 66879786180256393506530000896.0000 | 24.99 | 1287 | 25847261397527471458109882368.0000 | 0.8043   |
| 56476269258553484104324612096.0000 | 26.0  | 1339 | 25847261397527471458109882368.0000 | 0.7609   |
| 66879786180256393506530000896.0000 | 26.99 | 1390 | 25847261397527471458109882368.0000 | 0.7609   |
| 60934919907553756850706972672.0000 | 28.0  | 1442 | 25847261397527471458109882368.0000 | 0.7174   |
| 54990046079064749362670206976.0000 | 28.99 | 1493 | 25847261397527471458109882368.0000 | 0.7174   |
| 69852232539233862989838811136.0000 | 30.0  | 1545 | 25847261397527471458109882368.0000 | 0.7826   |
| 71338440607149856067065741312.0000 | 30.99 | 1596 | 25847261397527471458109882368.0000 | 0.7609   |
| 66879793736042764338743738368.0000 | 32.0  | 1648 | 25847261397527471458109882368.0000 | 0.7609   |
| 44586525379468658942404460544.0000 | 32.99 | 1699 | 25847261397527471458109882368.0000 | 0.7391   |
| 59448700505958211923205947392.0000 | 34.0  | 1751 | 25847261397527471458109882368.0000 | 0.7391   |
| 63907347377065294855434928128.0000 | 34.99 | 1802 | 25847261397527471458109882368.0000 | 0.7391   |
| 75797095034043309831508459520.0000 | 36.0  | 1854 | 25847261397527471458109882368.0000 | 0.7391   |
| 62421135531256120760147640320.0000 | 36.99 | 1905 | 25847261397527471458109882368.0000 | 0.7174   |
| 53503830455362394249322561536.0000 | 38.0  | 1957 | 25847261397527471458109882368.0000 | 0.7174   |
| 56476265480660303086264254464.0000 | 38.99 | 2008 | 25847261397527471458109882368.0000 | 0.7174   |
| 53503826677469204435169181696.0000 | 39.61 | 2040 | 25847261397527471458109882368.0000 | 0.7174   |


### Framework versions

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