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# smids_10x_deit_small_sgd_00001_fold4

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: 0.9309
- Accuracy: 0.5667

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.049         | 1.0   | 750   | 1.0657          | 0.4217   |
| 1.0271        | 2.0   | 1500  | 1.0619          | 0.4233   |
| 1.0309        | 3.0   | 2250  | 1.0577          | 0.4233   |
| 1.0685        | 4.0   | 3000  | 1.0531          | 0.4233   |
| 1.0213        | 5.0   | 3750  | 1.0484          | 0.425    |
| 1.0075        | 6.0   | 4500  | 1.0438          | 0.4267   |
| 1.0135        | 7.0   | 5250  | 1.0390          | 0.4283   |
| 1.0193        | 8.0   | 6000  | 1.0343          | 0.43     |
| 1.0172        | 9.0   | 6750  | 1.0296          | 0.4383   |
| 0.995         | 10.0  | 7500  | 1.0249          | 0.4417   |
| 0.9861        | 11.0  | 8250  | 1.0204          | 0.4467   |
| 0.9925        | 12.0  | 9000  | 1.0158          | 0.4533   |
| 0.9841        | 13.0  | 9750  | 1.0115          | 0.465    |
| 0.9738        | 14.0  | 10500 | 1.0072          | 0.4733   |
| 0.9779        | 15.0  | 11250 | 1.0030          | 0.4783   |
| 0.9393        | 16.0  | 12000 | 0.9988          | 0.485    |
| 0.968         | 17.0  | 12750 | 0.9949          | 0.485    |
| 0.9542        | 18.0  | 13500 | 0.9909          | 0.4883   |
| 0.9456        | 19.0  | 14250 | 0.9871          | 0.4917   |
| 0.9805        | 20.0  | 15000 | 0.9834          | 0.4967   |
| 0.9272        | 21.0  | 15750 | 0.9798          | 0.5      |
| 0.9402        | 22.0  | 16500 | 0.9763          | 0.5083   |
| 0.9463        | 23.0  | 17250 | 0.9729          | 0.5133   |
| 0.9349        | 24.0  | 18000 | 0.9697          | 0.515    |
| 0.9212        | 25.0  | 18750 | 0.9666          | 0.5167   |
| 0.9115        | 26.0  | 19500 | 0.9636          | 0.5183   |
| 0.9201        | 27.0  | 20250 | 0.9607          | 0.5217   |
| 0.9475        | 28.0  | 21000 | 0.9580          | 0.525    |
| 0.9135        | 29.0  | 21750 | 0.9554          | 0.5267   |
| 0.9341        | 30.0  | 22500 | 0.9529          | 0.53     |
| 0.9173        | 31.0  | 23250 | 0.9505          | 0.5317   |
| 0.9276        | 32.0  | 24000 | 0.9483          | 0.535    |
| 0.9211        | 33.0  | 24750 | 0.9462          | 0.5417   |
| 0.9232        | 34.0  | 25500 | 0.9443          | 0.5467   |
| 0.9171        | 35.0  | 26250 | 0.9425          | 0.5483   |
| 0.9007        | 36.0  | 27000 | 0.9408          | 0.5483   |
| 0.9143        | 37.0  | 27750 | 0.9393          | 0.555    |
| 0.8916        | 38.0  | 28500 | 0.9379          | 0.5567   |
| 0.8951        | 39.0  | 29250 | 0.9366          | 0.5567   |
| 0.9014        | 40.0  | 30000 | 0.9355          | 0.5567   |
| 0.8889        | 41.0  | 30750 | 0.9345          | 0.5583   |
| 0.8953        | 42.0  | 31500 | 0.9336          | 0.5583   |
| 0.9154        | 43.0  | 32250 | 0.9329          | 0.5583   |
| 0.8836        | 44.0  | 33000 | 0.9323          | 0.5633   |
| 0.8961        | 45.0  | 33750 | 0.9318          | 0.5667   |
| 0.8837        | 46.0  | 34500 | 0.9314          | 0.5667   |
| 0.8621        | 47.0  | 35250 | 0.9311          | 0.5667   |
| 0.8982        | 48.0  | 36000 | 0.9310          | 0.5667   |
| 0.8793        | 49.0  | 36750 | 0.9309          | 0.5667   |
| 0.8813        | 50.0  | 37500 | 0.9309          | 0.5667   |


### 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