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# smids_10x_deit_tiny_adamax_001_fold3

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0576
- Accuracy: 0.9133

## 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: 0.001
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.366         | 1.0   | 750   | 0.3478          | 0.86     |
| 0.3154        | 2.0   | 1500  | 0.3552          | 0.8633   |
| 0.2007        | 3.0   | 2250  | 0.4696          | 0.845    |
| 0.2296        | 4.0   | 3000  | 0.3387          | 0.8783   |
| 0.2607        | 5.0   | 3750  | 0.4239          | 0.865    |
| 0.2365        | 6.0   | 4500  | 0.3514          | 0.895    |
| 0.1244        | 7.0   | 5250  | 0.3538          | 0.8917   |
| 0.1356        | 8.0   | 6000  | 0.3984          | 0.895    |
| 0.0526        | 9.0   | 6750  | 0.4940          | 0.875    |
| 0.1195        | 10.0  | 7500  | 0.5287          | 0.8783   |
| 0.0559        | 11.0  | 8250  | 0.6054          | 0.8817   |
| 0.0512        | 12.0  | 9000  | 0.6374          | 0.8717   |
| 0.0498        | 13.0  | 9750  | 0.6405          | 0.8817   |
| 0.0296        | 14.0  | 10500 | 0.6601          | 0.895    |
| 0.0626        | 15.0  | 11250 | 0.7807          | 0.89     |
| 0.055         | 16.0  | 12000 | 0.7694          | 0.905    |
| 0.0444        | 17.0  | 12750 | 0.6413          | 0.905    |
| 0.0317        | 18.0  | 13500 | 0.7330          | 0.9033   |
| 0.0108        | 19.0  | 14250 | 0.7464          | 0.8917   |
| 0.0236        | 20.0  | 15000 | 0.7591          | 0.885    |
| 0.01          | 21.0  | 15750 | 0.8264          | 0.9067   |
| 0.0226        | 22.0  | 16500 | 0.7921          | 0.8933   |
| 0.0127        | 23.0  | 17250 | 0.7486          | 0.9033   |
| 0.0025        | 24.0  | 18000 | 0.8018          | 0.8983   |
| 0.0004        | 25.0  | 18750 | 0.7411          | 0.9083   |
| 0.0           | 26.0  | 19500 | 0.8554          | 0.895    |
| 0.0           | 27.0  | 20250 | 0.9122          | 0.9017   |
| 0.0           | 28.0  | 21000 | 0.8611          | 0.9067   |
| 0.0041        | 29.0  | 21750 | 0.8741          | 0.9033   |
| 0.0           | 30.0  | 22500 | 0.7969          | 0.9167   |
| 0.012         | 31.0  | 23250 | 0.8521          | 0.91     |
| 0.0058        | 32.0  | 24000 | 0.9974          | 0.8983   |
| 0.0           | 33.0  | 24750 | 0.9864          | 0.9      |
| 0.0           | 34.0  | 25500 | 0.8709          | 0.91     |
| 0.0           | 35.0  | 26250 | 0.9411          | 0.9117   |
| 0.0           | 36.0  | 27000 | 1.0050          | 0.9033   |
| 0.0           | 37.0  | 27750 | 0.9456          | 0.905    |
| 0.0           | 38.0  | 28500 | 0.9323          | 0.9083   |
| 0.0           | 39.0  | 29250 | 0.9349          | 0.9117   |
| 0.0           | 40.0  | 30000 | 0.9420          | 0.9117   |
| 0.0           | 41.0  | 30750 | 0.9601          | 0.9133   |
| 0.0           | 42.0  | 31500 | 0.9780          | 0.9133   |
| 0.0           | 43.0  | 32250 | 0.9953          | 0.9133   |
| 0.0           | 44.0  | 33000 | 1.0029          | 0.915    |
| 0.0           | 45.0  | 33750 | 1.0208          | 0.9133   |
| 0.0           | 46.0  | 34500 | 1.0335          | 0.9133   |
| 0.0           | 47.0  | 35250 | 1.0420          | 0.9133   |
| 0.0           | 48.0  | 36000 | 1.0522          | 0.9133   |
| 0.0           | 49.0  | 36750 | 1.0572          | 0.9117   |
| 0.0           | 50.0  | 37500 | 1.0576          | 0.9133   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm