---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: train_model_yonsei
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: dataset
      split: test
      args: dataset
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.87
---

<!-- 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. -->

# train_model_yonsei

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: 0.5148
- Accuracy: 0.87

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5711        | 0.98  | 11   | 1.4796          | 0.69     |
| 1.3855        | 1.96  | 22   | 1.2302          | 0.74     |
| 1.1544        | 2.93  | 33   | 1.0229          | 0.77     |
| 0.9292        | 4.0   | 45   | 0.8371          | 0.8      |
| 0.7715        | 4.98  | 56   | 0.7186          | 0.84     |
| 0.6521        | 5.96  | 67   | 0.6353          | 0.85     |
| 0.5736        | 6.93  | 78   | 0.5895          | 0.86     |
| 0.4745        | 8.0   | 90   | 0.5891          | 0.85     |
| 0.4361        | 8.98  | 101  | 0.5370          | 0.87     |
| 0.4431        | 9.78  | 110  | 0.5148          | 0.87     |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3