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# smids_10x_deit_tiny_adamax_001_fold5

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: 0.8586
- Accuracy: 0.915

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3782        | 1.0   | 750   | 0.3344          | 0.8667   |
| 0.2904        | 2.0   | 1500  | 0.3574          | 0.8483   |
| 0.2048        | 3.0   | 2250  | 0.3230          | 0.8817   |
| 0.2           | 4.0   | 3000  | 0.3479          | 0.8933   |
| 0.2233        | 5.0   | 3750  | 0.3431          | 0.8883   |
| 0.1334        | 6.0   | 4500  | 0.3350          | 0.9017   |
| 0.1268        | 7.0   | 5250  | 0.3335          | 0.8967   |
| 0.077         | 8.0   | 6000  | 0.4549          | 0.8883   |
| 0.0723        | 9.0   | 6750  | 0.3771          | 0.9067   |
| 0.0426        | 10.0  | 7500  | 0.4455          | 0.9017   |
| 0.0977        | 11.0  | 8250  | 0.4334          | 0.9067   |
| 0.0237        | 12.0  | 9000  | 0.5437          | 0.9      |
| 0.0358        | 13.0  | 9750  | 0.5148          | 0.885    |
| 0.0032        | 14.0  | 10500 | 0.6045          | 0.9083   |
| 0.0293        | 15.0  | 11250 | 0.6394          | 0.8933   |
| 0.0156        | 16.0  | 12000 | 0.6836          | 0.89     |
| 0.0548        | 17.0  | 12750 | 0.5770          | 0.9017   |
| 0.0127        | 18.0  | 13500 | 0.6663          | 0.8983   |
| 0.0203        | 19.0  | 14250 | 0.6791          | 0.905    |
| 0.0154        | 20.0  | 15000 | 0.6990          | 0.905    |
| 0.0128        | 21.0  | 15750 | 0.7251          | 0.9017   |
| 0.0003        | 22.0  | 16500 | 0.7324          | 0.8933   |
| 0.0024        | 23.0  | 17250 | 0.7123          | 0.9017   |
| 0.0015        | 24.0  | 18000 | 0.6502          | 0.9133   |
| 0.0109        | 25.0  | 18750 | 0.6676          | 0.9117   |
| 0.0004        | 26.0  | 19500 | 0.6984          | 0.9033   |
| 0.0105        | 27.0  | 20250 | 0.8181          | 0.8967   |
| 0.0029        | 28.0  | 21000 | 0.7764          | 0.9      |
| 0.0304        | 29.0  | 21750 | 0.7986          | 0.8967   |
| 0.008         | 30.0  | 22500 | 0.8233          | 0.895    |
| 0.0008        | 31.0  | 23250 | 0.8494          | 0.9033   |
| 0.0           | 32.0  | 24000 | 0.8041          | 0.91     |
| 0.0           | 33.0  | 24750 | 0.8842          | 0.9167   |
| 0.0           | 34.0  | 25500 | 0.7437          | 0.9233   |
| 0.0           | 35.0  | 26250 | 0.7405          | 0.925    |
| 0.0           | 36.0  | 27000 | 0.7962          | 0.9083   |
| 0.0059        | 37.0  | 27750 | 0.7867          | 0.9233   |
| 0.0           | 38.0  | 28500 | 0.8151          | 0.92     |
| 0.0           | 39.0  | 29250 | 0.8010          | 0.91     |
| 0.0           | 40.0  | 30000 | 0.8483          | 0.9133   |
| 0.0           | 41.0  | 30750 | 0.8225          | 0.9167   |
| 0.0           | 42.0  | 31500 | 0.8207          | 0.9167   |
| 0.0           | 43.0  | 32250 | 0.8290          | 0.915    |
| 0.0           | 44.0  | 33000 | 0.8408          | 0.915    |
| 0.0           | 45.0  | 33750 | 0.8374          | 0.9183   |
| 0.0           | 46.0  | 34500 | 0.8446          | 0.9167   |
| 0.0           | 47.0  | 35250 | 0.8518          | 0.915    |
| 0.0           | 48.0  | 36000 | 0.8526          | 0.915    |
| 0.0           | 49.0  | 36750 | 0.8568          | 0.9167   |
| 0.0           | 50.0  | 37500 | 0.8586          | 0.915    |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm