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# smids_10x_deit_tiny_sgd_0001_fold2

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4154
- Accuracy: 0.8386

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0614        | 1.0   | 750   | 1.0774          | 0.4276   |
| 0.9244        | 2.0   | 1500  | 0.9849          | 0.5008   |
| 0.8719        | 3.0   | 2250  | 0.9059          | 0.5474   |
| 0.8364        | 4.0   | 3000  | 0.8357          | 0.6140   |
| 0.7154        | 5.0   | 3750  | 0.7698          | 0.6589   |
| 0.7009        | 6.0   | 4500  | 0.7160          | 0.7038   |
| 0.6226        | 7.0   | 5250  | 0.6676          | 0.7321   |
| 0.5568        | 8.0   | 6000  | 0.6320          | 0.7521   |
| 0.5746        | 9.0   | 6750  | 0.6030          | 0.7604   |
| 0.5421        | 10.0  | 7500  | 0.5805          | 0.7671   |
| 0.5134        | 11.0  | 8250  | 0.5611          | 0.7737   |
| 0.5557        | 12.0  | 9000  | 0.5444          | 0.7770   |
| 0.5053        | 13.0  | 9750  | 0.5297          | 0.7804   |
| 0.4226        | 14.0  | 10500 | 0.5183          | 0.7887   |
| 0.4645        | 15.0  | 11250 | 0.5092          | 0.7903   |
| 0.4059        | 16.0  | 12000 | 0.5013          | 0.7920   |
| 0.421         | 17.0  | 12750 | 0.4951          | 0.7987   |
| 0.4242        | 18.0  | 13500 | 0.4876          | 0.7970   |
| 0.4439        | 19.0  | 14250 | 0.4811          | 0.7970   |
| 0.4437        | 20.0  | 15000 | 0.4767          | 0.7987   |
| 0.4454        | 21.0  | 15750 | 0.4711          | 0.8037   |
| 0.3749        | 22.0  | 16500 | 0.4658          | 0.8037   |
| 0.3717        | 23.0  | 17250 | 0.4614          | 0.8053   |
| 0.3725        | 24.0  | 18000 | 0.4568          | 0.8053   |
| 0.4228        | 25.0  | 18750 | 0.4527          | 0.8136   |
| 0.4364        | 26.0  | 19500 | 0.4498          | 0.8103   |
| 0.4024        | 27.0  | 20250 | 0.4458          | 0.8203   |
| 0.3741        | 28.0  | 21000 | 0.4427          | 0.8220   |
| 0.38          | 29.0  | 21750 | 0.4402          | 0.8203   |
| 0.3796        | 30.0  | 22500 | 0.4372          | 0.8236   |
| 0.3538        | 31.0  | 23250 | 0.4351          | 0.8253   |
| 0.3869        | 32.0  | 24000 | 0.4332          | 0.8253   |
| 0.3759        | 33.0  | 24750 | 0.4310          | 0.8286   |
| 0.394         | 34.0  | 25500 | 0.4290          | 0.8270   |
| 0.3753        | 35.0  | 26250 | 0.4274          | 0.8270   |
| 0.4036        | 36.0  | 27000 | 0.4252          | 0.8303   |
| 0.3883        | 37.0  | 27750 | 0.4241          | 0.8336   |
| 0.3856        | 38.0  | 28500 | 0.4227          | 0.8336   |
| 0.3479        | 39.0  | 29250 | 0.4214          | 0.8336   |
| 0.4431        | 40.0  | 30000 | 0.4201          | 0.8336   |
| 0.391         | 41.0  | 30750 | 0.4193          | 0.8336   |
| 0.3751        | 42.0  | 31500 | 0.4184          | 0.8336   |
| 0.3523        | 43.0  | 32250 | 0.4178          | 0.8336   |
| 0.3279        | 44.0  | 33000 | 0.4171          | 0.8336   |
| 0.341         | 45.0  | 33750 | 0.4165          | 0.8353   |
| 0.3735        | 46.0  | 34500 | 0.4161          | 0.8353   |
| 0.3807        | 47.0  | 35250 | 0.4158          | 0.8353   |
| 0.373         | 48.0  | 36000 | 0.4155          | 0.8386   |
| 0.3296        | 49.0  | 36750 | 0.4154          | 0.8386   |
| 0.3593        | 50.0  | 37500 | 0.4154          | 0.8386   |


### 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