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# SPIE_MULTICLASS_GOOGLE_2_2

This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2186
- Accuracy: 0.9296

## 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: 32
- eval_batch_size: 32
- seed: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.9163        | 0.96   | 18   | 0.7277          | 0.7586   |
| 0.496         | 1.9733 | 37   | 0.3519          | 0.8782   |
| 0.2984        | 2.9867 | 56   | 0.2645          | 0.9061   |
| 0.2294        | 4.0    | 75   | 0.2249          | 0.9239   |
| 0.2132        | 4.8    | 90   | 0.2186          | 0.9296   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0

burn through contamination good weld lack of fusion lack of penetration misalignment