---
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: 0.7766666666666666
    - name: Precision
      type: precision
      value: 0.7660774253731343
    - name: Recall
      type: recall
      value: 0.7766666666666666
---

<!-- 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: 0.5054
- Accuracy: 0.7767
- Precision: 0.7661
- Recall: 0.7767
- F1 Score: 0.7395

## 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    | 0.6002          | 0.7208   | 0.6144    | 0.7208 | 0.6282   |
| No log        | 2.0   | 8    | 0.5620          | 0.7333   | 0.5378    | 0.7333 | 0.6205   |
| No log        | 3.0   | 12   | 0.5641          | 0.7208   | 0.6144    | 0.7208 | 0.6282   |
| No log        | 4.0   | 16   | 0.5504          | 0.7208   | 0.6453    | 0.7208 | 0.6460   |
| No log        | 5.0   | 20   | 0.5444          | 0.7292   | 0.6795    | 0.7292 | 0.6754   |
| No log        | 6.0   | 24   | 0.5713          | 0.7333   | 0.5378    | 0.7333 | 0.6205   |
| No log        | 7.0   | 28   | 0.5488          | 0.7375   | 0.8067    | 0.7375 | 0.6302   |
| 0.5813        | 8.0   | 32   | 0.5408          | 0.7417   | 0.8090    | 0.7417 | 0.6397   |
| 0.5813        | 9.0   | 36   | 0.5387          | 0.7542   | 0.7292    | 0.7542 | 0.7015   |
| 0.5813        | 10.0  | 40   | 0.5314          | 0.75     | 0.7212    | 0.75   | 0.6943   |
| 0.5813        | 11.0  | 44   | 0.5283          | 0.7792   | 0.7813    | 0.7792 | 0.7318   |
| 0.5813        | 12.0  | 48   | 0.5227          | 0.7667   | 0.7819    | 0.7667 | 0.7019   |
| 0.5813        | 13.0  | 52   | 0.5283          | 0.7583   | 0.7336    | 0.7583 | 0.7308   |
| 0.5813        | 14.0  | 56   | 0.5263          | 0.7583   | 0.7393    | 0.7583 | 0.7045   |
| 0.5092        | 15.0  | 60   | 0.5205          | 0.7667   | 0.7819    | 0.7667 | 0.7019   |
| 0.5092        | 16.0  | 64   | 0.5236          | 0.7625   | 0.8206    | 0.7625 | 0.6837   |
| 0.5092        | 17.0  | 68   | 0.5241          | 0.7667   | 0.8230    | 0.7667 | 0.6919   |
| 0.5092        | 18.0  | 72   | 0.4962          | 0.7708   | 0.7639    | 0.7708 | 0.7217   |
| 0.5092        | 19.0  | 76   | 0.4942          | 0.7708   | 0.7878    | 0.7708 | 0.7094   |
| 0.5092        | 20.0  | 80   | 0.4909          | 0.7667   | 0.7503    | 0.7667 | 0.7221   |
| 0.5092        | 21.0  | 84   | 0.4964          | 0.7583   | 0.7343    | 0.7583 | 0.7334   |
| 0.5092        | 22.0  | 88   | 0.4928          | 0.7583   | 0.7393    | 0.7583 | 0.7045   |
| 0.4804        | 23.0  | 92   | 0.4938          | 0.7542   | 0.7292    | 0.7542 | 0.7015   |
| 0.4804        | 24.0  | 96   | 0.4949          | 0.7583   | 0.7327    | 0.7583 | 0.7253   |
| 0.4804        | 25.0  | 100  | 0.4946          | 0.7542   | 0.7268    | 0.7542 | 0.7220   |
| 0.4804        | 26.0  | 104  | 0.4935          | 0.7583   | 0.7330    | 0.7583 | 0.7281   |
| 0.4804        | 27.0  | 108  | 0.4927          | 0.7542   | 0.7268    | 0.7542 | 0.7220   |
| 0.4804        | 28.0  | 112  | 0.4943          | 0.7542   | 0.7268    | 0.7542 | 0.7220   |
| 0.4804        | 29.0  | 116  | 0.4951          | 0.7542   | 0.7268    | 0.7542 | 0.7220   |
| 0.4442        | 30.0  | 120  | 0.4952          | 0.75     | 0.7203    | 0.75   | 0.7158   |


### Framework versions

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