---
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: 0.78
    - name: Precision
      type: precision
      value: 0.781535758027584
    - name: Recall
      type: recall
      value: 0.78
---

<!-- 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: 0.4819
- Accuracy: 0.78
- Precision: 0.7815
- Recall: 0.78
- F1 Score: 0.7807

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log        | 1.0   | 4    | 0.5936          | 0.7292   | 0.8028    | 0.7292 | 0.6191   |
| No log        | 2.0   | 8    | 0.5702          | 0.7208   | 0.6468    | 0.7208 | 0.6283   |
| No log        | 3.0   | 12   | 0.5834          | 0.7125   | 0.6933    | 0.7125 | 0.7000   |
| No log        | 4.0   | 16   | 0.5471          | 0.7375   | 0.7034    | 0.7375 | 0.6846   |
| No log        | 5.0   | 20   | 0.5487          | 0.725    | 0.6938    | 0.725  | 0.6982   |
| No log        | 6.0   | 24   | 0.5253          | 0.7458   | 0.7182    | 0.7458 | 0.7116   |
| No log        | 7.0   | 28   | 0.5556          | 0.7417   | 0.7393    | 0.7417 | 0.7404   |
| 0.5648        | 8.0   | 32   | 0.5183          | 0.7417   | 0.7155    | 0.7417 | 0.7165   |
| 0.5648        | 9.0   | 36   | 0.5159          | 0.7667   | 0.7504    | 0.7667 | 0.7522   |
| 0.5648        | 10.0  | 40   | 0.5137          | 0.7708   | 0.7579    | 0.7708 | 0.7609   |
| 0.5648        | 11.0  | 44   | 0.5014          | 0.7833   | 0.7693    | 0.7833 | 0.7643   |
| 0.5648        | 12.0  | 48   | 0.5157          | 0.75     | 0.7524    | 0.75   | 0.7511   |
| 0.5648        | 13.0  | 52   | 0.5151          | 0.7417   | 0.7441    | 0.7417 | 0.7428   |
| 0.5648        | 14.0  | 56   | 0.4908          | 0.7792   | 0.7653    | 0.7792 | 0.7663   |
| 0.3814        | 15.0  | 60   | 0.4901          | 0.7833   | 0.7723    | 0.7833 | 0.7747   |
| 0.3814        | 16.0  | 64   | 0.4993          | 0.7667   | 0.7689    | 0.7667 | 0.7677   |
| 0.3814        | 17.0  | 68   | 0.4814          | 0.7792   | 0.7642    | 0.7792 | 0.7627   |
| 0.3814        | 18.0  | 72   | 0.5165          | 0.7583   | 0.7796    | 0.7583 | 0.7656   |
| 0.3814        | 19.0  | 76   | 0.4817          | 0.7958   | 0.7915    | 0.7958 | 0.7933   |
| 0.3814        | 20.0  | 80   | 0.4748          | 0.8083   | 0.8036    | 0.8083 | 0.8054   |
| 0.3814        | 21.0  | 84   | 0.4831          | 0.8042   | 0.8033    | 0.8042 | 0.8037   |
| 0.3814        | 22.0  | 88   | 0.4795          | 0.8083   | 0.8013    | 0.8083 | 0.8032   |
| 0.2354        | 23.0  | 92   | 0.5048          | 0.7708   | 0.7790    | 0.7708 | 0.7743   |
| 0.2354        | 24.0  | 96   | 0.4838          | 0.8042   | 0.7974    | 0.8042 | 0.7995   |
| 0.2354        | 25.0  | 100  | 0.4894          | 0.7833   | 0.7833    | 0.7833 | 0.7833   |
| 0.2354        | 26.0  | 104  | 0.4852          | 0.8      | 0.7914    | 0.8    | 0.7933   |
| 0.2354        | 27.0  | 108  | 0.4882          | 0.8      | 0.7982    | 0.8    | 0.7990   |
| 0.2354        | 28.0  | 112  | 0.4932          | 0.7875   | 0.7929    | 0.7875 | 0.7898   |
| 0.2354        | 29.0  | 116  | 0.4883          | 0.8083   | 0.8036    | 0.8083 | 0.8054   |
| 0.1479        | 30.0  | 120  | 0.4886          | 0.8042   | 0.7974    | 0.8042 | 0.7995   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3