---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-high-vit
  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: 0.8421052631578947
---

<!-- 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-high-vit

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6555
- Accuracy: 0.8421

## 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: 0.0002
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.8073        | 0.9787 | 23   | 1.4742          | 0.5211   |
| 0.9801        | 2.0    | 47   | 1.2410          | 0.5526   |
| 0.5808        | 2.9787 | 70   | 0.9728          | 0.7053   |
| 0.3797        | 4.0    | 94   | 0.7751          | 0.7632   |
| 0.2559        | 4.9787 | 117  | 0.8020          | 0.7684   |
| 0.1131        | 6.0    | 141  | 0.7116          | 0.8105   |
| 0.1207        | 6.9787 | 164  | 0.7258          | 0.8105   |
| 0.1068        | 8.0    | 188  | 0.6817          | 0.8316   |
| 0.0559        | 8.9787 | 211  | 0.6589          | 0.8368   |
| 0.0529        | 9.7872 | 230  | 0.6555          | 0.8421   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1