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# smids_5x_deit_small_rms_0001_fold5

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: 0.8725
- Accuracy: 0.915

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2465        | 1.0   | 375   | 0.3246          | 0.8817   |
| 0.1757        | 2.0   | 750   | 0.3108          | 0.8983   |
| 0.1416        | 3.0   | 1125  | 0.4018          | 0.8917   |
| 0.0805        | 4.0   | 1500  | 0.5614          | 0.8733   |
| 0.0831        | 5.0   | 1875  | 0.4489          | 0.8783   |
| 0.0313        | 6.0   | 2250  | 0.6222          | 0.8833   |
| 0.0541        | 7.0   | 2625  | 0.5461          | 0.9017   |
| 0.0151        | 8.0   | 3000  | 0.5573          | 0.9017   |
| 0.0125        | 9.0   | 3375  | 0.6886          | 0.8817   |
| 0.0415        | 10.0  | 3750  | 0.5097          | 0.8967   |
| 0.0264        | 11.0  | 4125  | 0.6554          | 0.89     |
| 0.0122        | 12.0  | 4500  | 0.5509          | 0.8983   |
| 0.004         | 13.0  | 4875  | 0.6519          | 0.895    |
| 0.0092        | 14.0  | 5250  | 0.8038          | 0.88     |
| 0.0491        | 15.0  | 5625  | 0.5627          | 0.9117   |
| 0.0096        | 16.0  | 6000  | 0.7380          | 0.885    |
| 0.003         | 17.0  | 6375  | 0.6721          | 0.8967   |
| 0.0153        | 18.0  | 6750  | 0.6251          | 0.9017   |
| 0.0001        | 19.0  | 7125  | 0.7496          | 0.8933   |
| 0.0067        | 20.0  | 7500  | 0.7015          | 0.895    |
| 0.0015        | 21.0  | 7875  | 0.7054          | 0.9017   |
| 0.0117        | 22.0  | 8250  | 0.7731          | 0.8933   |
| 0.0015        | 23.0  | 8625  | 0.6632          | 0.9033   |
| 0.0003        | 24.0  | 9000  | 0.7347          | 0.9      |
| 0.0           | 25.0  | 9375  | 0.8344          | 0.8917   |
| 0.0002        | 26.0  | 9750  | 0.7080          | 0.905    |
| 0.0026        | 27.0  | 10125 | 0.8121          | 0.8933   |
| 0.0001        | 28.0  | 10500 | 0.7303          | 0.915    |
| 0.0285        | 29.0  | 10875 | 0.7237          | 0.905    |
| 0.0021        | 30.0  | 11250 | 0.7546          | 0.9      |
| 0.0096        | 31.0  | 11625 | 0.8217          | 0.8933   |
| 0.0           | 32.0  | 12000 | 0.8254          | 0.9017   |
| 0.0           | 33.0  | 12375 | 0.7430          | 0.905    |
| 0.0           | 34.0  | 12750 | 0.7668          | 0.9083   |
| 0.0002        | 35.0  | 13125 | 0.7980          | 0.9117   |
| 0.0018        | 36.0  | 13500 | 0.8507          | 0.9017   |
| 0.0           | 37.0  | 13875 | 0.7364          | 0.9133   |
| 0.0038        | 38.0  | 14250 | 0.7573          | 0.9133   |
| 0.0           | 39.0  | 14625 | 0.7786          | 0.9083   |
| 0.0           | 40.0  | 15000 | 0.7610          | 0.915    |
| 0.0           | 41.0  | 15375 | 0.7902          | 0.9117   |
| 0.0           | 42.0  | 15750 | 0.7539          | 0.9133   |
| 0.0           | 43.0  | 16125 | 0.8516          | 0.9133   |
| 0.0           | 44.0  | 16500 | 0.8335          | 0.9183   |
| 0.0           | 45.0  | 16875 | 0.8847          | 0.905    |
| 0.0           | 46.0  | 17250 | 0.9773          | 0.8967   |
| 0.0028        | 47.0  | 17625 | 0.8669          | 0.915    |
| 0.0           | 48.0  | 18000 | 0.8681          | 0.9133   |
| 0.0           | 49.0  | 18375 | 0.8719          | 0.9133   |
| 0.0019        | 50.0  | 18750 | 0.8725          | 0.915    |


### 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