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# smids_10x_deit_base_adamax_00001_fold5

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7940
- Accuracy: 0.9017

## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2093        | 1.0   | 750   | 0.2691          | 0.8933   |
| 0.1756        | 2.0   | 1500  | 0.2406          | 0.905    |
| 0.137         | 3.0   | 2250  | 0.2712          | 0.905    |
| 0.0854        | 4.0   | 3000  | 0.3429          | 0.9033   |
| 0.0523        | 5.0   | 3750  | 0.4008          | 0.9083   |
| 0.0156        | 6.0   | 4500  | 0.4936          | 0.9      |
| 0.0114        | 7.0   | 5250  | 0.5881          | 0.8983   |
| 0.0007        | 8.0   | 6000  | 0.5995          | 0.9017   |
| 0.0011        | 9.0   | 6750  | 0.6327          | 0.8933   |
| 0.0013        | 10.0  | 7500  | 0.6424          | 0.9033   |
| 0.0001        | 11.0  | 8250  | 0.6384          | 0.9067   |
| 0.0           | 12.0  | 9000  | 0.6782          | 0.905    |
| 0.0           | 13.0  | 9750  | 0.7151          | 0.9067   |
| 0.0           | 14.0  | 10500 | 0.7016          | 0.9017   |
| 0.0           | 15.0  | 11250 | 0.7152          | 0.9033   |
| 0.0           | 16.0  | 12000 | 0.7512          | 0.9      |
| 0.0095        | 17.0  | 12750 | 0.7354          | 0.8983   |
| 0.0           | 18.0  | 13500 | 0.7495          | 0.9033   |
| 0.0           | 19.0  | 14250 | 0.7609          | 0.9      |
| 0.0           | 20.0  | 15000 | 0.7954          | 0.895    |
| 0.0           | 21.0  | 15750 | 0.7575          | 0.8983   |
| 0.0           | 22.0  | 16500 | 0.7508          | 0.9      |
| 0.0           | 23.0  | 17250 | 0.7396          | 0.9033   |
| 0.0           | 24.0  | 18000 | 0.6948          | 0.9017   |
| 0.0           | 25.0  | 18750 | 0.7758          | 0.9017   |
| 0.0           | 26.0  | 19500 | 0.7490          | 0.905    |
| 0.0           | 27.0  | 20250 | 0.7438          | 0.905    |
| 0.0           | 28.0  | 21000 | 0.7884          | 0.9017   |
| 0.0           | 29.0  | 21750 | 0.7693          | 0.9      |
| 0.008         | 30.0  | 22500 | 0.8123          | 0.9017   |
| 0.0           | 31.0  | 23250 | 0.7714          | 0.9      |
| 0.0           | 32.0  | 24000 | 0.7832          | 0.905    |
| 0.0           | 33.0  | 24750 | 0.7708          | 0.9      |
| 0.0           | 34.0  | 25500 | 0.7992          | 0.9      |
| 0.0           | 35.0  | 26250 | 0.7850          | 0.9      |
| 0.0           | 36.0  | 27000 | 0.7791          | 0.9017   |
| 0.0028        | 37.0  | 27750 | 0.7669          | 0.9      |
| 0.0           | 38.0  | 28500 | 0.7718          | 0.9      |
| 0.0           | 39.0  | 29250 | 0.8025          | 0.9033   |
| 0.0           | 40.0  | 30000 | 0.8035          | 0.9017   |
| 0.0           | 41.0  | 30750 | 0.7913          | 0.9017   |
| 0.0           | 42.0  | 31500 | 0.7895          | 0.9033   |
| 0.0           | 43.0  | 32250 | 0.7964          | 0.9017   |
| 0.0           | 44.0  | 33000 | 0.7921          | 0.9017   |
| 0.0           | 45.0  | 33750 | 0.7925          | 0.9033   |
| 0.0           | 46.0  | 34500 | 0.7930          | 0.9033   |
| 0.0           | 47.0  | 35250 | 0.7931          | 0.9017   |
| 0.0           | 48.0  | 36000 | 0.7925          | 0.9017   |
| 0.0           | 49.0  | 36750 | 0.7920          | 0.9033   |
| 0.0           | 50.0  | 37500 | 0.7940          | 0.9017   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm