---
base_model: google/vit-base-patch16-224-in21k
datasets:
- imagefolder
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: results
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - type: accuracy
      value: 0.49375
      name: Accuracy
---

<!-- 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. -->

# results

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6499
- Accuracy: 0.4938

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0569        | 1.0   | 20   | 2.0360          | 0.1938   |
| 1.9499        | 2.0   | 40   | 1.9751          | 0.325    |
| 1.8401        | 3.0   | 60   | 1.8969          | 0.4125   |
| 1.7302        | 4.0   | 80   | 1.8159          | 0.4625   |
| 1.6452        | 5.0   | 100  | 1.7533          | 0.4437   |
| 1.5509        | 6.0   | 120  | 1.7124          | 0.4938   |
| 1.4928        | 7.0   | 140  | 1.6806          | 0.5125   |
| 1.4412        | 8.0   | 160  | 1.6631          | 0.4938   |
| 1.407         | 9.0   | 180  | 1.6530          | 0.5      |
| 1.4025        | 10.0  | 200  | 1.6499          | 0.4938   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1