---
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: 0.5615577889447236
    - name: F1
      type: f1
      value:
        f1: 0.5213901124963216
---

<!-- 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: 0.9396
- Accuracy: {'accuracy': 0.5615577889447236}
- F1: {'f1': 0.5213901124963216}

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy                         | F1                         |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:|
| 1.221         | 1.0   | 796  | 0.9396          | {'accuracy': 0.5615577889447236} | {'f1': 0.5213901124963216} |


### Framework versions

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