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# vit-base-crack-classification-aug

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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0165
- Accuracy: 0.9907

## 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: 0.0002
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4964        | 1.0   | 212  | 0.3400          | 0.8796   |
| 0.249         | 2.0   | 424  | 0.1651          | 0.9236   |
| 0.1216        | 3.0   | 636  | 0.0585          | 0.9676   |
| 0.0488        | 4.0   | 848  | 0.0382          | 0.9769   |
| 0.0304        | 5.0   | 1060 | 0.0302          | 0.9907   |
| 0.0107        | 6.0   | 1272 | 0.0294          | 0.9838   |
| 0.0093        | 7.0   | 1484 | 0.0165          | 0.9907   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

crack scratch tire flat dent glass shatter lamp broken