---
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: 0.9033333333333333
    - name: Precision
      type: precision
      value: 0.892075919335706
    - name: Recall
      type: recall
      value: 0.9033333333333333
---

<!-- 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: 0.2446
- Accuracy: 0.9033
- Precision: 0.8921
- Recall: 0.9033
- F1 Score: 0.8889

## 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    | 0.5037          | 0.8667   | 0.8150    | 0.8667 | 0.8224   |
| No log        | 2.0   | 8    | 0.3500          | 0.8708   | 0.7584    | 0.8708 | 0.8107   |
| No log        | 3.0   | 12   | 0.3154          | 0.8708   | 0.7584    | 0.8708 | 0.8107   |
| 0.5284        | 4.0   | 16   | 0.2974          | 0.8833   | 0.8659    | 0.8833 | 0.8497   |
| 0.5284        | 5.0   | 20   | 0.2954          | 0.8875   | 0.8731    | 0.8875 | 0.8768   |
| 0.5284        | 6.0   | 24   | 0.2721          | 0.8958   | 0.8871    | 0.8958 | 0.8716   |
| 0.5284        | 7.0   | 28   | 0.2679          | 0.8875   | 0.8817    | 0.8875 | 0.8527   |
| 0.3362        | 8.0   | 32   | 0.2634          | 0.8875   | 0.8817    | 0.8875 | 0.8527   |
| 0.3362        | 9.0   | 36   | 0.2507          | 0.9042   | 0.8953    | 0.9042 | 0.8879   |
| 0.3362        | 10.0  | 40   | 0.2439          | 0.9083   | 0.9006    | 0.9083 | 0.8941   |
| 0.3362        | 11.0  | 44   | 0.2589          | 0.8917   | 0.8861    | 0.8917 | 0.8884   |
| 0.3017        | 12.0  | 48   | 0.2428          | 0.9083   | 0.9005    | 0.9083 | 0.9024   |
| 0.3017        | 13.0  | 52   | 0.2543          | 0.9      | 0.8949    | 0.9    | 0.8970   |
| 0.3017        | 14.0  | 56   | 0.2651          | 0.8958   | 0.8944    | 0.8958 | 0.8951   |
| 0.278         | 15.0  | 60   | 0.2637          | 0.8958   | 0.8944    | 0.8958 | 0.8951   |


### Framework versions

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