---
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: 0.8566666666666667
    - name: Precision
      type: precision
      value: 0.8522571872571872
    - name: Recall
      type: recall
      value: 0.8566666666666667
---

<!-- 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: 0.4410
- Accuracy: 0.8567
- Precision: 0.8523
- Recall: 0.8567
- F1 Score: 0.8517

## 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    | 0.5841          | 0.7333   | 0.6770    | 0.7333 | 0.6479   |
| No log        | 2.0   | 8    | 0.5727          | 0.7333   | 0.5378    | 0.7333 | 0.6205   |
| No log        | 3.0   | 12   | 0.6089          | 0.7208   | 0.7222    | 0.7208 | 0.7215   |
| No log        | 4.0   | 16   | 0.5332          | 0.7458   | 0.7205    | 0.7458 | 0.6727   |
| No log        | 5.0   | 20   | 0.5314          | 0.7625   | 0.7410    | 0.7625 | 0.7416   |
| No log        | 6.0   | 24   | 0.5284          | 0.7583   | 0.7486    | 0.7583 | 0.6959   |
| No log        | 7.0   | 28   | 0.5220          | 0.775    | 0.7700    | 0.775  | 0.7286   |
| 0.5564        | 8.0   | 32   | 0.5204          | 0.7833   | 0.7740    | 0.7833 | 0.7481   |
| 0.5564        | 9.0   | 36   | 0.5044          | 0.7708   | 0.7616    | 0.7708 | 0.7650   |
| 0.5564        | 10.0  | 40   | 0.4845          | 0.8125   | 0.8051    | 0.8125 | 0.7941   |
| 0.5564        | 11.0  | 44   | 0.4921          | 0.7833   | 0.7726    | 0.7833 | 0.7757   |
| 0.5564        | 12.0  | 48   | 0.4792          | 0.8167   | 0.8098    | 0.8167 | 0.7996   |
| 0.5564        | 13.0  | 52   | 0.4825          | 0.8      | 0.7889    | 0.8    | 0.7901   |
| 0.5564        | 14.0  | 56   | 0.4987          | 0.8083   | 0.7989    | 0.8083 | 0.8002   |
| 0.3176        | 15.0  | 60   | 0.4970          | 0.8208   | 0.8144    | 0.8208 | 0.8050   |
| 0.3176        | 16.0  | 64   | 0.5076          | 0.8083   | 0.7983    | 0.8083 | 0.7923   |
| 0.3176        | 17.0  | 68   | 0.5227          | 0.8083   | 0.7979    | 0.8083 | 0.7941   |
| 0.3176        | 18.0  | 72   | 0.5132          | 0.8042   | 0.7928    | 0.8042 | 0.7905   |
| 0.3176        | 19.0  | 76   | 0.5081          | 0.8167   | 0.8087    | 0.8167 | 0.8014   |
| 0.3176        | 20.0  | 80   | 0.5140          | 0.8292   | 0.8220    | 0.8292 | 0.8187   |
| 0.3176        | 21.0  | 84   | 0.5392          | 0.8125   | 0.8032    | 0.8125 | 0.7977   |
| 0.3176        | 22.0  | 88   | 0.5175          | 0.7958   | 0.7829    | 0.7958 | 0.7815   |
| 0.1778        | 23.0  | 92   | 0.5109          | 0.8125   | 0.8032    | 0.8125 | 0.7977   |
| 0.1778        | 24.0  | 96   | 0.4961          | 0.8292   | 0.8217    | 0.8292 | 0.8213   |
| 0.1778        | 25.0  | 100  | 0.5251          | 0.8083   | 0.7979    | 0.8083 | 0.7941   |
| 0.1778        | 26.0  | 104  | 0.5192          | 0.8167   | 0.8075    | 0.8167 | 0.8046   |
| 0.1778        | 27.0  | 108  | 0.5030          | 0.8333   | 0.8274    | 0.8333 | 0.8286   |
| 0.1778        | 28.0  | 112  | 0.5031          | 0.8375   | 0.8310    | 0.8375 | 0.8300   |
| 0.1778        | 29.0  | 116  | 0.5164          | 0.8208   | 0.8127    | 0.8208 | 0.8083   |
| 0.1109        | 30.0  | 120  | 0.5192          | 0.8208   | 0.8127    | 0.8208 | 0.8083   |


### Framework versions

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