---
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: 0.8373493975903614
    - name: Precision
      type: precision
      value: 0.8745971666076694
    - name: Recall
      type: recall
      value: 0.7993336310123969
    - name: F1
      type: f1
      value: 0.8036849674785987
---

<!-- 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: 1.1993
- Accuracy: 0.8373
- Precision: 0.8746
- Recall: 0.7993
- F1: 0.8037

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 3    | 1.6294          | 0.6747   | 0.6434    | 0.6238 | 0.5944 |
| No log        | 2.0   | 6    | 1.4495          | 0.7530   | 0.7776    | 0.7018 | 0.6875 |
| No log        | 3.0   | 9    | 1.3163          | 0.8373   | 0.8563    | 0.7993 | 0.8022 |
| 1.5378        | 4.0   | 12   | 1.2327          | 0.8373   | 0.8736    | 0.7993 | 0.8035 |
| 1.5378        | 5.0   | 15   | 1.1993          | 0.8373   | 0.8746    | 0.7993 | 0.8037 |


### Framework versions

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