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# asl_aplhabet_img_classifier_v3

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7922
- Accuracy: 0.7549

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 272  | 3.0038          | 0.3802   |
| 3.0097        | 2.0   | 544  | 2.5739          | 0.5880   |
| 3.0097        | 3.0   | 816  | 2.2886          | 0.6464   |
| 2.3653        | 4.0   | 1088 | 2.0810          | 0.7099   |
| 2.3653        | 5.0   | 1360 | 1.9355          | 0.7407   |
| 1.9884        | 6.0   | 1632 | 1.8371          | 0.7582   |
| 1.9884        | 7.0   | 1904 | 1.7752          | 0.7701   |
| 1.8003        | 8.0   | 2176 | 1.7531          | 0.7674   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

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