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# vit-base-aihub_model

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: 0.0080
- Accuracy: 0.9979
- Precision: 0.9982
- Recall: 0.9976
- F1: 0.9979

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.015         | 1.0   | 101  | 0.0229          | 0.9938   | 0.9928    | 0.9929 | 0.9928 |
| 0.0177        | 2.0   | 203  | 0.0316          | 0.9913   | 0.9928    | 0.9880 | 0.9903 |
| 0.0177        | 3.0   | 304  | 0.0127          | 0.9958   | 0.9954    | 0.9955 | 0.9954 |
| 0.0145        | 4.0   | 406  | 0.0129          | 0.9967   | 0.9970    | 0.9960 | 0.9965 |
| 0.0101        | 4.98  | 505  | 0.0080          | 0.9979   | 0.9982    | 0.9976 | 0.9979 |


### Framework versions

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

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