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# smids_10x_deit_small_sgd_001_fold2

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: 0.3138
- Accuracy: 0.8686

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5167        | 1.0   | 750   | 0.5666          | 0.7737   |
| 0.3469        | 2.0   | 1500  | 0.4420          | 0.8136   |
| 0.3157        | 3.0   | 2250  | 0.3924          | 0.8336   |
| 0.3366        | 4.0   | 3000  | 0.3644          | 0.8469   |
| 0.2937        | 5.0   | 3750  | 0.3504          | 0.8569   |
| 0.2683        | 6.0   | 4500  | 0.3342          | 0.8602   |
| 0.2786        | 7.0   | 5250  | 0.3236          | 0.8636   |
| 0.2458        | 8.0   | 6000  | 0.3168          | 0.8619   |
| 0.2409        | 9.0   | 6750  | 0.3122          | 0.8586   |
| 0.2266        | 10.0  | 7500  | 0.3079          | 0.8652   |
| 0.2724        | 11.0  | 8250  | 0.3033          | 0.8586   |
| 0.2793        | 12.0  | 9000  | 0.3021          | 0.8586   |
| 0.2082        | 13.0  | 9750  | 0.3016          | 0.8619   |
| 0.152         | 14.0  | 10500 | 0.3001          | 0.8669   |
| 0.1732        | 15.0  | 11250 | 0.2977          | 0.8636   |
| 0.1629        | 16.0  | 12000 | 0.2993          | 0.8636   |
| 0.1493        | 17.0  | 12750 | 0.2962          | 0.8669   |
| 0.1762        | 18.0  | 13500 | 0.2975          | 0.8669   |
| 0.1954        | 19.0  | 14250 | 0.2989          | 0.8735   |
| 0.1979        | 20.0  | 15000 | 0.2956          | 0.8636   |
| 0.1452        | 21.0  | 15750 | 0.2997          | 0.8636   |
| 0.1414        | 22.0  | 16500 | 0.2986          | 0.8636   |
| 0.131         | 23.0  | 17250 | 0.2989          | 0.8652   |
| 0.1633        | 24.0  | 18000 | 0.2990          | 0.8652   |
| 0.1429        | 25.0  | 18750 | 0.3003          | 0.8636   |
| 0.2373        | 26.0  | 19500 | 0.3030          | 0.8735   |
| 0.1884        | 27.0  | 20250 | 0.3051          | 0.8702   |
| 0.1254        | 28.0  | 21000 | 0.3031          | 0.8602   |
| 0.1804        | 29.0  | 21750 | 0.3034          | 0.8719   |
| 0.1437        | 30.0  | 22500 | 0.3048          | 0.8686   |
| 0.1608        | 31.0  | 23250 | 0.3012          | 0.8669   |
| 0.1618        | 32.0  | 24000 | 0.3040          | 0.8652   |
| 0.1429        | 33.0  | 24750 | 0.3043          | 0.8602   |
| 0.1612        | 34.0  | 25500 | 0.3075          | 0.8652   |
| 0.1719        | 35.0  | 26250 | 0.3075          | 0.8619   |
| 0.1633        | 36.0  | 27000 | 0.3103          | 0.8669   |
| 0.1619        | 37.0  | 27750 | 0.3071          | 0.8636   |
| 0.1665        | 38.0  | 28500 | 0.3086          | 0.8669   |
| 0.1293        | 39.0  | 29250 | 0.3088          | 0.8669   |
| 0.1641        | 40.0  | 30000 | 0.3125          | 0.8719   |
| 0.1466        | 41.0  | 30750 | 0.3125          | 0.8702   |
| 0.1482        | 42.0  | 31500 | 0.3110          | 0.8652   |
| 0.1022        | 43.0  | 32250 | 0.3124          | 0.8652   |
| 0.1075        | 44.0  | 33000 | 0.3116          | 0.8669   |
| 0.1257        | 45.0  | 33750 | 0.3131          | 0.8669   |
| 0.1217        | 46.0  | 34500 | 0.3119          | 0.8669   |
| 0.1431        | 47.0  | 35250 | 0.3120          | 0.8686   |
| 0.1086        | 48.0  | 36000 | 0.3131          | 0.8686   |
| 0.1041        | 49.0  | 36750 | 0.3136          | 0.8686   |
| 0.1201        | 50.0  | 37500 | 0.3138          | 0.8686   |


### 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