---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: vit-base-aihub_model-v2
  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: 0.963855421686747
    - name: Precision
      type: precision
      value: 0.9609609235289817
    - name: Recall
      type: recall
      value: 0.9613676432460462
    - name: F1
      type: f1
      value: 0.9604284776111401
---

<!-- 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-v2

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: 0.3076
- Accuracy: 0.9639
- Precision: 0.9610
- Recall: 0.9614
- F1: 0.9604

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 3    | 1.2753          | 0.8373   | 0.8563    | 0.7993 | 0.8022 |
| No log        | 2.0   | 6    | 1.1252          | 0.8675   | 0.8895    | 0.8300 | 0.8333 |
| No log        | 3.0   | 9    | 0.9427          | 0.8976   | 0.9185    | 0.8696 | 0.8760 |
| 1.1721        | 4.0   | 12   | 0.7995          | 0.9398   | 0.9474    | 0.9195 | 0.9246 |
| 1.1721        | 5.0   | 15   | 0.6820          | 0.9699   | 0.9704    | 0.9613 | 0.9642 |
| 1.1721        | 6.0   | 18   | 0.5927          | 0.9639   | 0.9603    | 0.9583 | 0.9587 |
| 0.7084        | 7.0   | 21   | 0.5239          | 0.9759   | 0.9725    | 0.9729 | 0.9725 |
| 0.7084        | 8.0   | 24   | 0.4743          | 0.9699   | 0.9665    | 0.9671 | 0.9665 |
| 0.7084        | 9.0   | 27   | 0.4436          | 0.9578   | 0.9558    | 0.9556 | 0.9544 |
| 0.4668        | 10.0  | 30   | 0.4070          | 0.9639   | 0.9610    | 0.9614 | 0.9604 |
| 0.4668        | 11.0  | 33   | 0.3817          | 0.9699   | 0.9665    | 0.9671 | 0.9665 |
| 0.4668        | 12.0  | 36   | 0.3625          | 0.9699   | 0.9665    | 0.9671 | 0.9665 |
| 0.4668        | 13.0  | 39   | 0.3536          | 0.9578   | 0.9558    | 0.9556 | 0.9544 |
| 0.3611        | 14.0  | 42   | 0.3384          | 0.9578   | 0.9558    | 0.9556 | 0.9544 |
| 0.3611        | 15.0  | 45   | 0.3249          | 0.9699   | 0.9665    | 0.9671 | 0.9665 |
| 0.3611        | 16.0  | 48   | 0.3164          | 0.9699   | 0.9665    | 0.9671 | 0.9665 |
| 0.3063        | 17.0  | 51   | 0.3142          | 0.9639   | 0.9610    | 0.9614 | 0.9604 |
| 0.3063        | 18.0  | 54   | 0.3122          | 0.9639   | 0.9610    | 0.9614 | 0.9604 |
| 0.3063        | 19.0  | 57   | 0.3093          | 0.9639   | 0.9610    | 0.9614 | 0.9604 |
| 0.294         | 20.0  | 60   | 0.3076          | 0.9639   | 0.9610    | 0.9614 | 0.9604 |


### Framework versions

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