---
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-ve-U10-12
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7843137254901961
---

<!-- 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-ve-U10-12

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: 0.6632
- Accuracy: 0.7843

## 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: 5.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3629        | 0.95  | 15   | 1.2289          | 0.4706   |
| 1.1038        | 1.97  | 31   | 1.0413          | 0.5882   |
| 0.9375        | 2.98  | 47   | 0.8989          | 0.5882   |
| 0.6917        | 4.0   | 63   | 0.8520          | 0.7059   |
| 0.5862        | 4.95  | 78   | 0.6827          | 0.7255   |
| 0.4042        | 5.97  | 94   | 0.7281          | 0.7255   |
| 0.2987        | 6.98  | 110  | 0.7262          | 0.7647   |
| 0.2571        | 8.0   | 126  | 0.7604          | 0.7255   |
| 0.2326        | 8.95  | 141  | 0.6632          | 0.7843   |
| 0.1994        | 9.97  | 157  | 0.6744          | 0.7451   |
| 0.1968        | 10.98 | 173  | 0.6864          | 0.7451   |
| 0.1847        | 11.43 | 180  | 0.6647          | 0.7451   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0