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# my_awesome_face_model

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: 1.3503
- Accuracy: 0.4938

## 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: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 192
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.9   | 3    | 2.0832          | 0.1      |
| No log        | 1.8   | 6    | 2.0749          | 0.1      |
| 2.0704        | 3.0   | 10   | 2.0478          | 0.1812   |
| 2.0704        | 3.9   | 13   | 2.0288          | 0.2      |
| 2.0704        | 4.8   | 16   | 1.9896          | 0.2812   |
| 1.9609        | 6.0   | 20   | 1.9322          | 0.3125   |
| 1.9609        | 6.9   | 23   | 1.8715          | 0.3438   |
| 1.9609        | 7.8   | 26   | 1.8056          | 0.3688   |
| 1.7478        | 9.0   | 30   | 1.7196          | 0.4125   |
| 1.7478        | 9.9   | 33   | 1.6635          | 0.3937   |
| 1.7478        | 10.8  | 36   | 1.6160          | 0.3875   |
| 1.5379        | 12.0  | 40   | 1.6014          | 0.425    |
| 1.5379        | 12.9  | 43   | 1.5505          | 0.4188   |
| 1.5379        | 13.8  | 46   | 1.5187          | 0.4188   |
| 1.3858        | 15.0  | 50   | 1.4938          | 0.4562   |
| 1.3858        | 15.9  | 53   | 1.4853          | 0.4562   |
| 1.3858        | 16.8  | 56   | 1.4664          | 0.4875   |
| 1.2551        | 18.0  | 60   | 1.4488          | 0.4375   |
| 1.2551        | 18.9  | 63   | 1.4531          | 0.4375   |
| 1.2551        | 19.8  | 66   | 1.3847          | 0.525    |
| 1.1573        | 21.0  | 70   | 1.3790          | 0.4813   |
| 1.1573        | 21.9  | 73   | 1.4168          | 0.4625   |
| 1.1573        | 22.8  | 76   | 1.4159          | 0.5      |
| 1.0833        | 24.0  | 80   | 1.3935          | 0.4813   |
| 1.0833        | 24.9  | 83   | 1.3965          | 0.4688   |
| 1.0833        | 25.8  | 86   | 1.3705          | 0.4875   |
| 1.0091        | 27.0  | 90   | 1.3879          | 0.4625   |
| 1.0091        | 27.9  | 93   | 1.3777          | 0.5062   |
| 1.0091        | 28.8  | 96   | 1.3867          | 0.4813   |
| 0.9632        | 30.0  | 100  | 1.3659          | 0.5188   |
| 0.9632        | 30.9  | 103  | 1.3398          | 0.4875   |
| 0.9632        | 31.8  | 106  | 1.3555          | 0.4688   |
| 0.9259        | 33.0  | 110  | 1.3621          | 0.5062   |
| 0.9259        | 33.9  | 113  | 1.3569          | 0.4938   |
| 0.9259        | 34.8  | 116  | 1.3397          | 0.5312   |
| 0.8994        | 36.0  | 120  | 1.3314          | 0.525    |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1

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