---
license: apache-2.0
base_model: google/vit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- f1
model-index:
- name: vit-SUPER02
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: F1
      type: f1
      value: 1.0
---

<!-- 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-SUPER02

This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- F1: 1.0

## 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: 0.0002
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0798        | 0.16  | 50   | 0.0393          | 0.9904 |
| 0.0161        | 0.31  | 100  | 0.0176          | 0.9936 |
| 0.0017        | 0.47  | 150  | 0.0020          | 0.9984 |
| 0.0012        | 0.62  | 200  | 0.0026          | 0.9985 |
| 0.0001        | 0.78  | 250  | 0.0001          | 1.0    |
| 0.0001        | 0.93  | 300  | 0.0001          | 1.0    |
| 0.0001        | 1.09  | 350  | 0.0001          | 1.0    |
| 0.0           | 1.24  | 400  | 0.0000          | 1.0    |
| 0.0           | 1.4   | 450  | 0.0000          | 1.0    |
| 0.0           | 1.55  | 500  | 0.0000          | 1.0    |
| 0.0           | 1.71  | 550  | 0.0000          | 1.0    |
| 0.0           | 1.86  | 600  | 0.0000          | 1.0    |
| 0.0           | 2.02  | 650  | 0.0000          | 1.0    |
| 0.0           | 2.17  | 700  | 0.0000          | 1.0    |
| 0.0           | 2.33  | 750  | 0.0000          | 1.0    |
| 0.0           | 2.48  | 800  | 0.0000          | 1.0    |
| 0.0           | 2.64  | 850  | 0.0000          | 1.0    |
| 0.0           | 2.8   | 900  | 0.0000          | 1.0    |
| 0.0           | 2.95  | 950  | 0.0000          | 1.0    |
| 0.0           | 3.11  | 1000 | 0.0000          | 1.0    |
| 0.0           | 3.26  | 1050 | 0.0000          | 1.0    |
| 0.0           | 3.42  | 1100 | 0.0000          | 1.0    |
| 0.0           | 3.57  | 1150 | 0.0000          | 1.0    |
| 0.0           | 3.73  | 1200 | 0.0000          | 1.0    |
| 0.0           | 3.88  | 1250 | 0.0000          | 1.0    |
| 0.0           | 4.04  | 1300 | 0.0000          | 1.0    |
| 0.0           | 4.19  | 1350 | 0.0000          | 1.0    |
| 0.0           | 4.35  | 1400 | 0.0000          | 1.0    |
| 0.0           | 4.5   | 1450 | 0.0000          | 1.0    |
| 0.0           | 4.66  | 1500 | 0.0000          | 1.0    |
| 0.0           | 4.81  | 1550 | 0.0000          | 1.0    |
| 0.0           | 4.97  | 1600 | 0.0000          | 1.0    |
| 0.0           | 5.12  | 1650 | 0.0000          | 1.0    |
| 0.0           | 5.28  | 1700 | 0.0000          | 1.0    |
| 0.0           | 5.43  | 1750 | 0.0000          | 1.0    |
| 0.0           | 5.59  | 1800 | 0.0000          | 1.0    |
| 0.0           | 5.75  | 1850 | 0.0000          | 1.0    |
| 0.0           | 5.9   | 1900 | 0.0000          | 1.0    |
| 0.0           | 6.06  | 1950 | 0.0000          | 1.0    |
| 0.0           | 6.21  | 2000 | 0.0000          | 1.0    |
| 0.0           | 6.37  | 2050 | 0.0000          | 1.0    |
| 0.0           | 6.52  | 2100 | 0.0000          | 1.0    |
| 0.0           | 6.68  | 2150 | 0.0000          | 1.0    |
| 0.0           | 6.83  | 2200 | 0.0000          | 1.0    |
| 0.0           | 6.99  | 2250 | 0.0000          | 1.0    |
| 0.0           | 7.14  | 2300 | 0.0000          | 1.0    |
| 0.0           | 7.3   | 2350 | 0.0000          | 1.0    |
| 0.0           | 7.45  | 2400 | 0.0000          | 1.0    |
| 0.0           | 7.61  | 2450 | 0.0000          | 1.0    |
| 0.0           | 7.76  | 2500 | 0.0000          | 1.0    |
| 0.0           | 7.92  | 2550 | 0.0000          | 1.0    |
| 0.0           | 8.07  | 2600 | 0.0000          | 1.0    |
| 0.0           | 8.23  | 2650 | 0.0000          | 1.0    |
| 0.0           | 8.39  | 2700 | 0.0000          | 1.0    |
| 0.0           | 8.54  | 2750 | 0.0000          | 1.0    |
| 0.0           | 8.7   | 2800 | 0.0000          | 1.0    |
| 0.0           | 8.85  | 2850 | 0.0000          | 1.0    |
| 0.0           | 9.01  | 2900 | 0.0000          | 1.0    |
| 0.0           | 9.16  | 2950 | 0.0000          | 1.0    |
| 0.0           | 9.32  | 3000 | 0.0000          | 1.0    |
| 0.0           | 9.47  | 3050 | 0.0000          | 1.0    |
| 0.0           | 9.63  | 3100 | 0.0000          | 1.0    |
| 0.0           | 9.78  | 3150 | 0.0000          | 1.0    |
| 0.0           | 9.94  | 3200 | 0.0000          | 1.0    |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1