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# vit-base-lfw-face

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: 0.2539
- Accuracy: 0.9742

## 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.4943        | 0.41  | 100  | 2.8682          | 0.5572   |
| 2.6744        | 0.83  | 200  | 1.9178          | 0.7601   |
| 1.7785        | 1.24  | 300  | 1.2370          | 0.8819   |
| 1.4402        | 1.66  | 400  | 0.8530          | 0.9336   |
| 0.8081        | 2.07  | 500  | 0.6033          | 0.9446   |
| 0.6349        | 2.49  | 600  | 0.4308          | 0.9631   |
| 0.526         | 2.9   | 700  | 0.3396          | 0.9705   |
| 0.3106        | 3.32  | 800  | 0.2789          | 0.9742   |
| 0.2481        | 3.73  | 900  | 0.2539          | 0.9742   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

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