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# vit-weldclassify

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: 1.0639
- Accuracy: 0.8174

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 18
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.8311        | 0.8130  | 100  | 0.9623          | 0.4886   |
| 0.6016        | 1.6260  | 200  | 0.5911          | 0.7215   |
| 0.2602        | 2.4390  | 300  | 1.0585          | 0.6393   |
| 0.1643        | 3.2520  | 400  | 0.9470          | 0.7169   |
| 0.3754        | 4.0650  | 500  | 0.6054          | 0.8082   |
| 0.1446        | 4.8780  | 600  | 0.6845          | 0.7854   |
| 0.138         | 5.6911  | 700  | 0.9011          | 0.7534   |
| 0.033         | 6.5041  | 800  | 0.8366          | 0.8128   |
| 0.0538        | 7.3171  | 900  | 0.9102          | 0.7854   |
| 0.0144        | 8.1301  | 1000 | 0.8510          | 0.8128   |
| 0.0459        | 8.9431  | 1100 | 0.8610          | 0.8219   |
| 0.0022        | 9.7561  | 1200 | 0.9398          | 0.8082   |
| 0.0019        | 10.5691 | 1300 | 0.8714          | 0.8356   |
| 0.0015        | 11.3821 | 1400 | 1.0001          | 0.8128   |
| 0.0013        | 12.1951 | 1500 | 0.9926          | 0.8219   |
| 0.0012        | 13.0081 | 1600 | 1.0175          | 0.8219   |
| 0.0011        | 13.8211 | 1700 | 1.0323          | 0.8219   |
| 0.001         | 14.6341 | 1800 | 1.0453          | 0.8174   |
| 0.0009        | 15.4472 | 1900 | 1.0518          | 0.8174   |
| 0.0009        | 16.2602 | 2000 | 1.0585          | 0.8174   |
| 0.0009        | 17.0732 | 2100 | 1.0623          | 0.8174   |
| 0.0009        | 17.8862 | 2200 | 1.0639          | 0.8174   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

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