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# smids_10x_deit_small_sgd_001_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: 0.3291
- Accuracy: 0.8767

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5469        | 1.0   | 750   | 0.5533          | 0.7983   |
| 0.4148        | 2.0   | 1500  | 0.4326          | 0.8367   |
| 0.3982        | 3.0   | 2250  | 0.3912          | 0.8467   |
| 0.355         | 4.0   | 3000  | 0.3693          | 0.8533   |
| 0.3032        | 5.0   | 3750  | 0.3569          | 0.8583   |
| 0.2345        | 6.0   | 4500  | 0.3466          | 0.8617   |
| 0.2053        | 7.0   | 5250  | 0.3412          | 0.865    |
| 0.2443        | 8.0   | 6000  | 0.3381          | 0.8633   |
| 0.259         | 9.0   | 6750  | 0.3314          | 0.875    |
| 0.2146        | 10.0  | 7500  | 0.3275          | 0.8717   |
| 0.2301        | 11.0  | 8250  | 0.3262          | 0.8733   |
| 0.298         | 12.0  | 9000  | 0.3264          | 0.8733   |
| 0.2031        | 13.0  | 9750  | 0.3234          | 0.8783   |
| 0.1941        | 14.0  | 10500 | 0.3276          | 0.8783   |
| 0.1822        | 15.0  | 11250 | 0.3209          | 0.88     |
| 0.2209        | 16.0  | 12000 | 0.3226          | 0.8767   |
| 0.1294        | 17.0  | 12750 | 0.3179          | 0.8817   |
| 0.1726        | 18.0  | 13500 | 0.3224          | 0.88     |
| 0.2222        | 19.0  | 14250 | 0.3196          | 0.8833   |
| 0.1604        | 20.0  | 15000 | 0.3199          | 0.8817   |
| 0.1742        | 21.0  | 15750 | 0.3204          | 0.8783   |
| 0.1599        | 22.0  | 16500 | 0.3188          | 0.88     |
| 0.1753        | 23.0  | 17250 | 0.3189          | 0.8817   |
| 0.1975        | 24.0  | 18000 | 0.3189          | 0.8817   |
| 0.1797        | 25.0  | 18750 | 0.3190          | 0.8817   |
| 0.1646        | 26.0  | 19500 | 0.3244          | 0.8817   |
| 0.1585        | 27.0  | 20250 | 0.3244          | 0.885    |
| 0.1303        | 28.0  | 21000 | 0.3225          | 0.8817   |
| 0.1144        | 29.0  | 21750 | 0.3207          | 0.8817   |
| 0.1409        | 30.0  | 22500 | 0.3230          | 0.8817   |
| 0.1303        | 31.0  | 23250 | 0.3219          | 0.8833   |
| 0.1405        | 32.0  | 24000 | 0.3260          | 0.8817   |
| 0.1503        | 33.0  | 24750 | 0.3248          | 0.88     |
| 0.1402        | 34.0  | 25500 | 0.3257          | 0.8817   |
| 0.1266        | 35.0  | 26250 | 0.3227          | 0.88     |
| 0.1495        | 36.0  | 27000 | 0.3271          | 0.8817   |
| 0.1021        | 37.0  | 27750 | 0.3248          | 0.8833   |
| 0.1616        | 38.0  | 28500 | 0.3242          | 0.885    |
| 0.158         | 39.0  | 29250 | 0.3254          | 0.88     |
| 0.1668        | 40.0  | 30000 | 0.3256          | 0.8833   |
| 0.1276        | 41.0  | 30750 | 0.3297          | 0.88     |
| 0.1072        | 42.0  | 31500 | 0.3307          | 0.88     |
| 0.1457        | 43.0  | 32250 | 0.3289          | 0.8783   |
| 0.1691        | 44.0  | 33000 | 0.3278          | 0.8817   |
| 0.1442        | 45.0  | 33750 | 0.3288          | 0.88     |
| 0.1231        | 46.0  | 34500 | 0.3279          | 0.88     |
| 0.1011        | 47.0  | 35250 | 0.3276          | 0.8767   |
| 0.1059        | 48.0  | 36000 | 0.3287          | 0.8767   |
| 0.1263        | 49.0  | 36750 | 0.3292          | 0.8767   |
| 0.1053        | 50.0  | 37500 | 0.3291          | 0.8767   |


### 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