---
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: 0.6623
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3659        | 0.97  | 15   | 1.2287          | 0.4706   |
| 1.1063        | 2.0   | 31   | 1.0234          | 0.6863   |
| 0.9468        | 2.97  | 46   | 0.8665          | 0.6863   |
| 0.6825        | 4.0   | 62   | 0.7482          | 0.7059   |
| 0.5534        | 4.97  | 77   | 0.7609          | 0.7059   |
| 0.4019        | 6.0   | 93   | 0.7092          | 0.7255   |
| 0.3323        | 6.97  | 108  | 0.6623          | 0.7843   |
| 0.2743        | 8.0   | 124  | 0.7407          | 0.7059   |
| 0.2411        | 8.97  | 139  | 0.6249          | 0.7647   |
| 0.2021        | 10.0  | 155  | 0.7222          | 0.7451   |
| 0.1925        | 10.97 | 170  | 0.7808          | 0.7059   |
| 0.1748        | 11.61 | 180  | 0.7451          | 0.7255   |


### Framework versions

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