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# smids_10x_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: 0.9331
- Accuracy: 0.5933

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0687        | 1.0   | 750   | 1.0724          | 0.425    |
| 1.0435        | 2.0   | 1500  | 1.0669          | 0.42     |
| 1.0439        | 3.0   | 2250  | 1.0614          | 0.4283   |
| 1.0595        | 4.0   | 3000  | 1.0559          | 0.435    |
| 1.0216        | 5.0   | 3750  | 1.0506          | 0.4383   |
| 1.0179        | 6.0   | 4500  | 1.0454          | 0.4467   |
| 1.0048        | 7.0   | 5250  | 1.0402          | 0.4517   |
| 1.0171        | 8.0   | 6000  | 1.0351          | 0.4533   |
| 1.0075        | 9.0   | 6750  | 1.0302          | 0.4567   |
| 0.9942        | 10.0  | 7500  | 1.0255          | 0.4683   |
| 0.9968        | 11.0  | 8250  | 1.0209          | 0.4783   |
| 0.9853        | 12.0  | 9000  | 1.0163          | 0.485    |
| 0.9829        | 13.0  | 9750  | 1.0118          | 0.4933   |
| 0.9676        | 14.0  | 10500 | 1.0075          | 0.4933   |
| 0.9869        | 15.0  | 11250 | 1.0033          | 0.5      |
| 0.9385        | 16.0  | 12000 | 0.9992          | 0.5117   |
| 0.9422        | 17.0  | 12750 | 0.9953          | 0.5167   |
| 0.9475        | 18.0  | 13500 | 0.9914          | 0.5233   |
| 0.9706        | 19.0  | 14250 | 0.9876          | 0.5267   |
| 0.9823        | 20.0  | 15000 | 0.9840          | 0.53     |
| 0.9281        | 21.0  | 15750 | 0.9805          | 0.535    |
| 0.9429        | 22.0  | 16500 | 0.9770          | 0.54     |
| 0.9545        | 23.0  | 17250 | 0.9738          | 0.545    |
| 0.9266        | 24.0  | 18000 | 0.9706          | 0.545    |
| 0.943         | 25.0  | 18750 | 0.9675          | 0.545    |
| 0.9362        | 26.0  | 19500 | 0.9646          | 0.55     |
| 0.9017        | 27.0  | 20250 | 0.9618          | 0.5517   |
| 0.9415        | 28.0  | 21000 | 0.9592          | 0.555    |
| 0.9141        | 29.0  | 21750 | 0.9566          | 0.555    |
| 0.9329        | 30.0  | 22500 | 0.9543          | 0.5567   |
| 0.931         | 31.0  | 23250 | 0.9520          | 0.5617   |
| 0.9115        | 32.0  | 24000 | 0.9498          | 0.5633   |
| 0.9251        | 33.0  | 24750 | 0.9478          | 0.565    |
| 0.8996        | 34.0  | 25500 | 0.9460          | 0.5717   |
| 0.9232        | 35.0  | 26250 | 0.9442          | 0.5717   |
| 0.8817        | 36.0  | 27000 | 0.9427          | 0.5717   |
| 0.8794        | 37.0  | 27750 | 0.9412          | 0.575    |
| 0.8813        | 38.0  | 28500 | 0.9398          | 0.5767   |
| 0.8952        | 39.0  | 29250 | 0.9386          | 0.58     |
| 0.8846        | 40.0  | 30000 | 0.9375          | 0.5817   |
| 0.8967        | 41.0  | 30750 | 0.9366          | 0.5867   |
| 0.9065        | 42.0  | 31500 | 0.9358          | 0.5883   |
| 0.9123        | 43.0  | 32250 | 0.9351          | 0.59     |
| 0.8878        | 44.0  | 33000 | 0.9345          | 0.59     |
| 0.8772        | 45.0  | 33750 | 0.9340          | 0.59     |
| 0.9035        | 46.0  | 34500 | 0.9336          | 0.5933   |
| 0.9152        | 47.0  | 35250 | 0.9334          | 0.5933   |
| 0.8837        | 48.0  | 36000 | 0.9332          | 0.5933   |
| 0.8879        | 49.0  | 36750 | 0.9331          | 0.5933   |
| 0.8918        | 50.0  | 37500 | 0.9331          | 0.5933   |


### 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