---
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: 0.6984126984126984
---

<!-- 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: 0.6466
- Accuracy: 0.6984

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6893        | 1.0   | 35   | 0.7319          | 0.4127   |
| 0.702         | 2.0   | 70   | 0.6863          | 0.5238   |
| 0.6644        | 3.0   | 105  | 0.6796          | 0.5873   |
| 0.645         | 4.0   | 140  | 0.6722          | 0.5714   |
| 0.6455        | 5.0   | 175  | 0.6545          | 0.6508   |
| 0.6456        | 6.0   | 210  | 0.6536          | 0.6508   |
| 0.6745        | 7.0   | 245  | 0.6463          | 0.6984   |
| 0.6369        | 8.0   | 280  | 0.6525          | 0.6667   |
| 0.6012        | 9.0   | 315  | 0.6486          | 0.6984   |
| 0.6219        | 10.0  | 350  | 0.6466          | 0.6984   |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.4.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1