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# sign-language-classification

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: 0.1351
- Accuracy: 0.96

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 32

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6016        | 1.0   | 100  | 1.5038          | 0.8      |
| 1.1072        | 2.0   | 200  | 0.6959          | 0.8675   |
| 0.6195        | 3.0   | 300  | 0.5236          | 0.87     |
| 0.5559        | 4.0   | 400  | 0.4819          | 0.87     |
| 0.389         | 5.0   | 500  | 0.3392          | 0.9      |
| 0.3878        | 6.0   | 600  | 0.3600          | 0.9025   |
| 0.3309        | 7.0   | 700  | 0.3312          | 0.9075   |
| 0.3397        | 8.0   | 800  | 0.2596          | 0.9225   |
| 0.3033        | 9.0   | 900  | 0.2056          | 0.935    |
| 0.2765        | 10.0  | 1000 | 0.2802          | 0.9175   |
| 0.2846        | 11.0  | 1100 | 0.3276          | 0.9025   |
| 0.2443        | 12.0  | 1200 | 0.3689          | 0.8975   |
| 0.2682        | 13.0  | 1300 | 0.2805          | 0.915    |
| 0.2053        | 14.0  | 1400 | 0.2437          | 0.9225   |
| 0.2453        | 15.0  | 1500 | 0.2646          | 0.92     |
| 0.1896        | 16.0  | 1600 | 0.2489          | 0.925    |
| 0.1841        | 17.0  | 1700 | 0.2393          | 0.9275   |
| 0.1406        | 18.0  | 1800 | 0.1935          | 0.945    |
| 0.1573        | 19.0  | 1900 | 0.2544          | 0.92     |
| 0.155         | 20.0  | 2000 | 0.1940          | 0.9475   |
| 0.1563        | 21.0  | 2100 | 0.2021          | 0.9325   |
| 0.133         | 22.0  | 2200 | 0.2413          | 0.9325   |
| 0.117         | 23.0  | 2300 | 0.1939          | 0.9375   |
| 0.1455        | 24.0  | 2400 | 0.1685          | 0.9575   |
| 0.144         | 25.0  | 2500 | 0.1787          | 0.9475   |
| 0.1119        | 26.0  | 2600 | 0.1511          | 0.96     |
| 0.1053        | 27.0  | 2700 | 0.1308          | 0.965    |
| 0.0964        | 28.0  | 2800 | 0.1042          | 0.9725   |
| 0.0938        | 29.0  | 2900 | 0.1751          | 0.9425   |
| 0.0881        | 30.0  | 3000 | 0.1066          | 0.965    |
| 0.0854        | 31.0  | 3100 | 0.1116          | 0.97     |
| 0.1002        | 32.0  | 3200 | 0.1351          | 0.96     |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2

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