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# smids_5x_deit_small_sgd_00001_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: 0.9917
- Accuracy: 0.5233

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1042        | 1.0   | 375   | 1.0744          | 0.425    |
| 1.0586        | 2.0   | 750   | 1.0707          | 0.4317   |
| 1.0589        | 3.0   | 1125  | 1.0672          | 0.4317   |
| 1.0667        | 4.0   | 1500  | 1.0637          | 0.435    |
| 1.0705        | 5.0   | 1875  | 1.0604          | 0.435    |
| 1.0556        | 6.0   | 2250  | 1.0572          | 0.44     |
| 1.037         | 7.0   | 2625  | 1.0541          | 0.4467   |
| 1.0193        | 8.0   | 3000  | 1.0509          | 0.4533   |
| 1.0476        | 9.0   | 3375  | 1.0479          | 0.455    |
| 1.0596        | 10.0  | 3750  | 1.0451          | 0.4617   |
| 1.0294        | 11.0  | 4125  | 1.0422          | 0.465    |
| 1.0295        | 12.0  | 4500  | 1.0395          | 0.47     |
| 1.0343        | 13.0  | 4875  | 1.0368          | 0.47     |
| 1.0353        | 14.0  | 5250  | 1.0343          | 0.4717   |
| 1.029         | 15.0  | 5625  | 1.0318          | 0.4767   |
| 1.027         | 16.0  | 6000  | 1.0293          | 0.48     |
| 1.0226        | 17.0  | 6375  | 1.0270          | 0.4833   |
| 0.9782        | 18.0  | 6750  | 1.0246          | 0.4917   |
| 1.019         | 19.0  | 7125  | 1.0224          | 0.4983   |
| 0.9985        | 20.0  | 7500  | 1.0203          | 0.5017   |
| 0.9654        | 21.0  | 7875  | 1.0183          | 0.5033   |
| 1.0053        | 22.0  | 8250  | 1.0163          | 0.5083   |
| 1.0112        | 23.0  | 8625  | 1.0144          | 0.5117   |
| 1.0077        | 24.0  | 9000  | 1.0126          | 0.515    |
| 0.9958        | 25.0  | 9375  | 1.0109          | 0.515    |
| 0.987         | 26.0  | 9750  | 1.0092          | 0.515    |
| 0.9915        | 27.0  | 10125 | 1.0076          | 0.5133   |
| 0.9929        | 28.0  | 10500 | 1.0061          | 0.5133   |
| 0.9979        | 29.0  | 10875 | 1.0047          | 0.5117   |
| 0.9641        | 30.0  | 11250 | 1.0034          | 0.5117   |
| 0.9905        | 31.0  | 11625 | 1.0021          | 0.5117   |
| 1.0051        | 32.0  | 12000 | 1.0009          | 0.5117   |
| 1.0023        | 33.0  | 12375 | 0.9998          | 0.5133   |
| 0.9697        | 34.0  | 12750 | 0.9988          | 0.5167   |
| 0.9834        | 35.0  | 13125 | 0.9979          | 0.5167   |
| 0.985         | 36.0  | 13500 | 0.9970          | 0.5183   |
| 0.9928        | 37.0  | 13875 | 0.9962          | 0.5183   |
| 0.9649        | 38.0  | 14250 | 0.9954          | 0.52     |
| 1.0054        | 39.0  | 14625 | 0.9948          | 0.5217   |
| 0.9871        | 40.0  | 15000 | 0.9942          | 0.5217   |
| 0.977         | 41.0  | 15375 | 0.9937          | 0.5217   |
| 0.9799        | 42.0  | 15750 | 0.9932          | 0.5217   |
| 0.9791        | 43.0  | 16125 | 0.9928          | 0.525    |
| 0.9745        | 44.0  | 16500 | 0.9925          | 0.525    |
| 0.9751        | 45.0  | 16875 | 0.9922          | 0.525    |
| 0.9977        | 46.0  | 17250 | 0.9920          | 0.5233   |
| 0.9954        | 47.0  | 17625 | 0.9919          | 0.5233   |
| 0.9619        | 48.0  | 18000 | 0.9918          | 0.5233   |
| 0.9797        | 49.0  | 18375 | 0.9917          | 0.5233   |
| 0.9489        | 50.0  | 18750 | 0.9917          | 0.5233   |


### 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