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# smids_10x_deit_small_rms_0001_fold3

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0751
- Accuracy: 0.905

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2232        | 1.0   | 750   | 0.2644          | 0.9033   |
| 0.1749        | 2.0   | 1500  | 0.3888          | 0.87     |
| 0.0708        | 3.0   | 2250  | 0.3860          | 0.9083   |
| 0.0864        | 4.0   | 3000  | 0.4018          | 0.9017   |
| 0.027         | 5.0   | 3750  | 0.5540          | 0.905    |
| 0.0152        | 6.0   | 4500  | 0.5713          | 0.9133   |
| 0.0637        | 7.0   | 5250  | 0.5356          | 0.9017   |
| 0.0914        | 8.0   | 6000  | 0.5134          | 0.9117   |
| 0.0042        | 9.0   | 6750  | 0.5226          | 0.905    |
| 0.0385        | 10.0  | 7500  | 0.6176          | 0.9067   |
| 0.0198        | 11.0  | 8250  | 0.7633          | 0.89     |
| 0.0448        | 12.0  | 9000  | 0.7337          | 0.8783   |
| 0.0021        | 13.0  | 9750  | 0.6859          | 0.8917   |
| 0.0019        | 14.0  | 10500 | 0.7372          | 0.8933   |
| 0.0006        | 15.0  | 11250 | 0.7460          | 0.885    |
| 0.0195        | 16.0  | 12000 | 0.7805          | 0.8933   |
| 0.0202        | 17.0  | 12750 | 0.8243          | 0.895    |
| 0.016         | 18.0  | 13500 | 0.7845          | 0.89     |
| 0.0037        | 19.0  | 14250 | 0.7538          | 0.8883   |
| 0.0001        | 20.0  | 15000 | 0.6925          | 0.8967   |
| 0.0006        | 21.0  | 15750 | 0.8393          | 0.8933   |
| 0.0           | 22.0  | 16500 | 0.7236          | 0.9      |
| 0.0024        | 23.0  | 17250 | 0.8639          | 0.885    |
| 0.0014        | 24.0  | 18000 | 0.8799          | 0.8917   |
| 0.0236        | 25.0  | 18750 | 0.6893          | 0.9033   |
| 0.0001        | 26.0  | 19500 | 0.7435          | 0.9033   |
| 0.0001        | 27.0  | 20250 | 0.6829          | 0.89     |
| 0.0194        | 28.0  | 21000 | 0.8267          | 0.8967   |
| 0.0002        | 29.0  | 21750 | 0.8000          | 0.8983   |
| 0.0001        | 30.0  | 22500 | 0.8336          | 0.89     |
| 0.0           | 31.0  | 23250 | 0.8017          | 0.9      |
| 0.0           | 32.0  | 24000 | 0.8257          | 0.9117   |
| 0.0           | 33.0  | 24750 | 0.8456          | 0.905    |
| 0.0           | 34.0  | 25500 | 0.7637          | 0.91     |
| 0.0           | 35.0  | 26250 | 0.8426          | 0.9067   |
| 0.0219        | 36.0  | 27000 | 0.8594          | 0.9067   |
| 0.0           | 37.0  | 27750 | 0.8437          | 0.9083   |
| 0.0           | 38.0  | 28500 | 0.9026          | 0.9117   |
| 0.0           | 39.0  | 29250 | 0.9566          | 0.9067   |
| 0.0           | 40.0  | 30000 | 0.9200          | 0.915    |
| 0.0           | 41.0  | 30750 | 0.9067          | 0.92     |
| 0.0           | 42.0  | 31500 | 0.9289          | 0.91     |
| 0.0           | 43.0  | 32250 | 0.9815          | 0.91     |
| 0.0           | 44.0  | 33000 | 0.9712          | 0.91     |
| 0.0           | 45.0  | 33750 | 1.0254          | 0.9067   |
| 0.0           | 46.0  | 34500 | 1.0353          | 0.9083   |
| 0.0           | 47.0  | 35250 | 1.0450          | 0.905    |
| 0.0           | 48.0  | 36000 | 1.0661          | 0.905    |
| 0.0           | 49.0  | 36750 | 1.0715          | 0.905    |
| 0.0           | 50.0  | 37500 | 1.0751          | 0.905    |


### 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