---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
model-index:
- name: vit-base-patch16-224
  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: 0.7066666666666667
    - name: Precision
      type: precision
      value: 0.5034113712374582
    - name: Recall
      type: recall
      value: 0.7066666666666667
---

<!-- 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

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: 0.5891
- Accuracy: 0.7067
- Precision: 0.5034
- Recall: 0.7067
- F1 Score: 0.5880

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log        | 1.0   | 4    | 0.5970          | 0.725    | 0.5256    | 0.725  | 0.6094   |
| No log        | 2.0   | 8    | 0.5990          | 0.7292   | 0.8028    | 0.7292 | 0.6191   |
| No log        | 3.0   | 12   | 0.5648          | 0.725    | 0.5256    | 0.725  | 0.6094   |
| 0.6217        | 4.0   | 16   | 0.6035          | 0.7042   | 0.6625    | 0.7042 | 0.6709   |
| 0.6217        | 5.0   | 20   | 0.5560          | 0.7333   | 0.8050    | 0.7333 | 0.6286   |
| 0.6217        | 6.0   | 24   | 0.5656          | 0.7167   | 0.6184    | 0.7167 | 0.6194   |
| 0.6217        | 7.0   | 28   | 0.5552          | 0.7292   | 0.8028    | 0.7292 | 0.6191   |
| 0.5729        | 8.0   | 32   | 0.5532          | 0.7292   | 0.7126    | 0.7292 | 0.6263   |
| 0.5729        | 9.0   | 36   | 0.5634          | 0.7292   | 0.6863    | 0.7292 | 0.6453   |
| 0.5729        | 10.0  | 40   | 0.5589          | 0.7333   | 0.7009    | 0.7333 | 0.6536   |
| 0.5729        | 11.0  | 44   | 0.5676          | 0.7292   | 0.6848    | 0.7292 | 0.6612   |
| 0.5599        | 12.0  | 48   | 0.5655          | 0.7333   | 0.6952    | 0.7333 | 0.6688   |
| 0.5599        | 13.0  | 52   | 0.5692          | 0.7333   | 0.6954    | 0.7333 | 0.6816   |
| 0.5599        | 14.0  | 56   | 0.5746          | 0.725    | 0.6864    | 0.725  | 0.6863   |
| 0.5382        | 15.0  | 60   | 0.5752          | 0.7208   | 0.6832    | 0.7208 | 0.6864   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3