---
tags:
- generated_from_trainer
datasets:
- preprocessed1024_config
metrics:
- accuracy
- f1
model-index:
- name: vit-model
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: preprocessed1024_config
      type: preprocessed1024_config
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value:
        accuracy: 0.6011306532663316
    - name: F1
      type: f1
      value:
        f1: 0.5956396413406886
---

<!-- 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-model

This model is a fine-tuned version of [](https://huggingface.co/) on the preprocessed1024_config dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1353
- Accuracy: {'accuracy': 0.6011306532663316}
- F1: {'f1': 0.5956396413406886}

## 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: 5e-05
- train_batch_size: 8
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy                         | F1                          |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:---------------------------:|
| 1.224         | 1.0   | 796  | 0.9884          | {'accuracy': 0.5276381909547738} | {'f1': 0.40344173017767304} |
| 0.96          | 2.0   | 1592 | 0.9255          | {'accuracy': 0.5621859296482412} | {'f1': 0.5134011716404221}  |
| 0.8878        | 3.0   | 2388 | 0.9308          | {'accuracy': 0.574748743718593}  | {'f1': 0.46867195041352344} |
| 0.809         | 4.0   | 3184 | 0.8904          | {'accuracy': 0.6067839195979899} | {'f1': 0.5799288651427482}  |
| 0.7541        | 5.0   | 3980 | 0.8936          | {'accuracy': 0.5954773869346733} | {'f1': 0.5938876317530138}  |
| 0.6904        | 6.0   | 4776 | 0.8760          | {'accuracy': 0.6118090452261307} | {'f1': 0.6023012293668115}  |
| 0.6195        | 7.0   | 5572 | 1.0032          | {'accuracy': 0.5917085427135679} | {'f1': 0.5834559014249068}  |
| 0.5766        | 8.0   | 6368 | 1.0268          | {'accuracy': 0.6023869346733668} | {'f1': 0.5779800559497847}  |
| 0.4963        | 9.0   | 7164 | 1.0460          | {'accuracy': 0.5992462311557789} | {'f1': 0.5875334711293277}  |
| 0.4323        | 10.0  | 7960 | 1.1353          | {'accuracy': 0.6011306532663316} | {'f1': 0.5956396413406886}  |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.1.0
- Tokenizers 0.12.1