---
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: 0.7333333333333333
---

<!-- 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: 0.5730
- Accuracy: 0.7333

## 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        | 1.0   | 8    | 0.5546          | 0.8      |
| 0.5945        | 2.0   | 16   | 0.5409          | 0.8      |
| 0.5832        | 3.0   | 24   | 0.5467          | 0.8      |
| 0.5338        | 4.0   | 32   | 0.5518          | 0.8      |
| 0.5513        | 5.0   | 40   | 0.5602          | 0.8      |
| 0.5513        | 6.0   | 48   | 0.5607          | 0.7333   |
| 0.5417        | 7.0   | 56   | 0.5707          | 0.7333   |
| 0.5343        | 8.0   | 64   | 0.5748          | 0.7333   |
| 0.5379        | 9.0   | 72   | 0.5736          | 0.7333   |
| 0.5137        | 10.0  | 80   | 0.5730          | 0.7333   |


### Framework versions

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