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# smids_10x_deit_small_sgd_001_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: 0.2764
- Accuracy: 0.895

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5289        | 1.0   | 750   | 0.5577          | 0.7883   |
| 0.411         | 2.0   | 1500  | 0.4355          | 0.835    |
| 0.3696        | 3.0   | 2250  | 0.3887          | 0.85     |
| 0.3417        | 4.0   | 3000  | 0.3643          | 0.8517   |
| 0.3357        | 5.0   | 3750  | 0.3441          | 0.8617   |
| 0.2644        | 6.0   | 4500  | 0.3299          | 0.865    |
| 0.2577        | 7.0   | 5250  | 0.3164          | 0.8667   |
| 0.2725        | 8.0   | 6000  | 0.3096          | 0.875    |
| 0.2894        | 9.0   | 6750  | 0.3046          | 0.8717   |
| 0.2245        | 10.0  | 7500  | 0.2980          | 0.87     |
| 0.2663        | 11.0  | 8250  | 0.2930          | 0.8817   |
| 0.2488        | 12.0  | 9000  | 0.2925          | 0.8717   |
| 0.2365        | 13.0  | 9750  | 0.2865          | 0.88     |
| 0.2172        | 14.0  | 10500 | 0.2813          | 0.8833   |
| 0.2487        | 15.0  | 11250 | 0.2761          | 0.885    |
| 0.1796        | 16.0  | 12000 | 0.2827          | 0.8817   |
| 0.1959        | 17.0  | 12750 | 0.2794          | 0.8833   |
| 0.1795        | 18.0  | 13500 | 0.2745          | 0.8833   |
| 0.2262        | 19.0  | 14250 | 0.2788          | 0.885    |
| 0.1595        | 20.0  | 15000 | 0.2793          | 0.885    |
| 0.2022        | 21.0  | 15750 | 0.2745          | 0.8833   |
| 0.2023        | 22.0  | 16500 | 0.2758          | 0.8917   |
| 0.1864        | 23.0  | 17250 | 0.2773          | 0.8883   |
| 0.1869        | 24.0  | 18000 | 0.2763          | 0.8967   |
| 0.1883        | 25.0  | 18750 | 0.2788          | 0.89     |
| 0.1768        | 26.0  | 19500 | 0.2728          | 0.8967   |
| 0.1135        | 27.0  | 20250 | 0.2823          | 0.8867   |
| 0.1819        | 28.0  | 21000 | 0.2713          | 0.8933   |
| 0.1691        | 29.0  | 21750 | 0.2729          | 0.8967   |
| 0.1867        | 30.0  | 22500 | 0.2819          | 0.89     |
| 0.1549        | 31.0  | 23250 | 0.2710          | 0.8933   |
| 0.125         | 32.0  | 24000 | 0.2766          | 0.8917   |
| 0.1602        | 33.0  | 24750 | 0.2747          | 0.895    |
| 0.1131        | 34.0  | 25500 | 0.2730          | 0.9      |
| 0.1454        | 35.0  | 26250 | 0.2723          | 0.895    |
| 0.1829        | 36.0  | 27000 | 0.2731          | 0.8967   |
| 0.1           | 37.0  | 27750 | 0.2730          | 0.8967   |
| 0.1344        | 38.0  | 28500 | 0.2751          | 0.8983   |
| 0.1584        | 39.0  | 29250 | 0.2745          | 0.8983   |
| 0.1265        | 40.0  | 30000 | 0.2754          | 0.8967   |
| 0.1671        | 41.0  | 30750 | 0.2769          | 0.8967   |
| 0.147         | 42.0  | 31500 | 0.2744          | 0.8933   |
| 0.1588        | 43.0  | 32250 | 0.2753          | 0.8967   |
| 0.1433        | 44.0  | 33000 | 0.2767          | 0.9      |
| 0.1715        | 45.0  | 33750 | 0.2775          | 0.8967   |
| 0.1027        | 46.0  | 34500 | 0.2766          | 0.9      |
| 0.1628        | 47.0  | 35250 | 0.2771          | 0.8967   |
| 0.1468        | 48.0  | 36000 | 0.2769          | 0.895    |
| 0.1346        | 49.0  | 36750 | 0.2765          | 0.895    |
| 0.0897        | 50.0  | 37500 | 0.2764          | 0.895    |


### 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