---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: vit_base_aihub_model_py
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9985872380503885
    - name: Precision
      type: precision
      value: 0.9989954885489135
    - name: Recall
      type: recall
      value: 0.998161142953993
    - name: F1
      type: f1
      value: 0.9985770990024514
---

<!-- 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_aihub_model_py

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0217
- Accuracy: 0.9986
- Precision: 0.9990
- Recall: 0.9982
- F1: 0.9986

## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1235        | 1.0   | 149  | 0.0936          | 0.9858   | 0.9845    | 0.9814 | 0.9830 |
| 0.067         | 2.0   | 299  | 0.0622          | 0.9878   | 0.9909    | 0.9813 | 0.9859 |
| 0.049         | 3.0   | 448  | 0.0322          | 0.9968   | 0.9969    | 0.9959 | 0.9964 |
| 0.0477        | 4.0   | 598  | 0.0249          | 0.9978   | 0.9985    | 0.9965 | 0.9975 |
| 0.0336        | 4.98  | 745  | 0.0217          | 0.9986   | 0.9990    | 0.9982 | 0.9986 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3