---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7441860465116279
---

<!-- 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

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5859
- Accuracy: 0.7442

## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- 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: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.96  | 6    | 0.5859          | 0.7442   |
| 0.605         | 1.92  | 12   | 0.5842          | 0.7442   |
| 0.605         | 2.88  | 18   | 0.5919          | 0.7442   |
| 0.5428        | 4.0   | 25   | 0.5885          | 0.7442   |
| 0.5584        | 4.96  | 31   | 0.5886          | 0.7442   |
| 0.5584        | 5.92  | 37   | 0.5915          | 0.7442   |
| 0.5593        | 6.88  | 43   | 0.5935          | 0.7442   |
| 0.5097        | 8.0   | 50   | 0.5947          | 0.7442   |
| 0.5097        | 8.96  | 56   | 0.5949          | 0.7442   |
| 0.5205        | 9.6   | 60   | 0.5949          | 0.7442   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1