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# smids_3x_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: 1.0167
- Accuracy: 0.5033

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0819        | 1.0   | 225   | 1.0751          | 0.425    |
| 1.0811        | 2.0   | 450   | 1.0723          | 0.4283   |
| 1.0651        | 3.0   | 675   | 1.0696          | 0.43     |
| 1.0585        | 4.0   | 900   | 1.0669          | 0.4317   |
| 1.0233        | 5.0   | 1125  | 1.0644          | 0.4367   |
| 1.0543        | 6.0   | 1350  | 1.0620          | 0.4383   |
| 1.0645        | 7.0   | 1575  | 1.0597          | 0.4433   |
| 1.0639        | 8.0   | 1800  | 1.0574          | 0.445    |
| 1.0491        | 9.0   | 2025  | 1.0553          | 0.4467   |
| 1.0536        | 10.0  | 2250  | 1.0531          | 0.4483   |
| 1.0638        | 11.0  | 2475  | 1.0511          | 0.4533   |
| 1.0457        | 12.0  | 2700  | 1.0491          | 0.4583   |
| 1.0693        | 13.0  | 2925  | 1.0472          | 0.4583   |
| 1.043         | 14.0  | 3150  | 1.0454          | 0.4583   |
| 1.0417        | 15.0  | 3375  | 1.0437          | 0.4667   |
| 1.0387        | 16.0  | 3600  | 1.0420          | 0.465    |
| 1.0423        | 17.0  | 3825  | 1.0404          | 0.4667   |
| 1.0457        | 18.0  | 4050  | 1.0388          | 0.465    |
| 1.0201        | 19.0  | 4275  | 1.0373          | 0.4683   |
| 1.0442        | 20.0  | 4500  | 1.0358          | 0.4683   |
| 1.0444        | 21.0  | 4725  | 1.0344          | 0.4717   |
| 1.0357        | 22.0  | 4950  | 1.0331          | 0.475    |
| 1.0413        | 23.0  | 5175  | 1.0318          | 0.4767   |
| 1.0389        | 24.0  | 5400  | 1.0306          | 0.4767   |
| 1.0161        | 25.0  | 5625  | 1.0294          | 0.4833   |
| 1.021         | 26.0  | 5850  | 1.0283          | 0.485    |
| 1.0545        | 27.0  | 6075  | 1.0273          | 0.4867   |
| 1.0129        | 28.0  | 6300  | 1.0263          | 0.4883   |
| 1.0266        | 29.0  | 6525  | 1.0254          | 0.49     |
| 1.0226        | 30.0  | 6750  | 1.0245          | 0.4917   |
| 1.0147        | 31.0  | 6975  | 1.0236          | 0.4933   |
| 1.0284        | 32.0  | 7200  | 1.0228          | 0.495    |
| 1.0418        | 33.0  | 7425  | 1.0221          | 0.495    |
| 1.0168        | 34.0  | 7650  | 1.0214          | 0.4967   |
| 0.9987        | 35.0  | 7875  | 1.0208          | 0.4967   |
| 0.9922        | 36.0  | 8100  | 1.0202          | 0.4983   |
| 1.0184        | 37.0  | 8325  | 1.0197          | 0.5      |
| 1.0229        | 38.0  | 8550  | 1.0192          | 0.5      |
| 0.9957        | 39.0  | 8775  | 1.0187          | 0.5      |
| 0.9899        | 40.0  | 9000  | 1.0183          | 0.5      |
| 1.0292        | 41.0  | 9225  | 1.0180          | 0.5      |
| 1.0309        | 42.0  | 9450  | 1.0177          | 0.5      |
| 1.0287        | 43.0  | 9675  | 1.0174          | 0.5      |
| 1.0138        | 44.0  | 9900  | 1.0172          | 0.5033   |
| 0.9831        | 45.0  | 10125 | 1.0170          | 0.5033   |
| 1.0147        | 46.0  | 10350 | 1.0169          | 0.5033   |
| 1.015         | 47.0  | 10575 | 1.0168          | 0.5033   |
| 1.0202        | 48.0  | 10800 | 1.0167          | 0.5033   |
| 1.015         | 49.0  | 11025 | 1.0167          | 0.5033   |
| 1.0165        | 50.0  | 11250 | 1.0167          | 0.5033   |


### 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