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# smids_5x_deit_tiny_adamax_00001_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: 0.8310
- Accuracy: 0.8883

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3076        | 1.0   | 375   | 0.3691          | 0.8367   |
| 0.2287        | 2.0   | 750   | 0.2879          | 0.8783   |
| 0.189         | 3.0   | 1125  | 0.2852          | 0.885    |
| 0.1751        | 4.0   | 1500  | 0.2883          | 0.8867   |
| 0.0982        | 5.0   | 1875  | 0.3260          | 0.8833   |
| 0.0575        | 6.0   | 2250  | 0.3348          | 0.875    |
| 0.0552        | 7.0   | 2625  | 0.3820          | 0.8883   |
| 0.0223        | 8.0   | 3000  | 0.4434          | 0.89     |
| 0.0296        | 9.0   | 3375  | 0.4885          | 0.8883   |
| 0.005         | 10.0  | 3750  | 0.5225          | 0.8917   |
| 0.0192        | 11.0  | 4125  | 0.5900          | 0.8883   |
| 0.006         | 12.0  | 4500  | 0.6146          | 0.885    |
| 0.0003        | 13.0  | 4875  | 0.6354          | 0.8867   |
| 0.0012        | 14.0  | 5250  | 0.6738          | 0.8867   |
| 0.0003        | 15.0  | 5625  | 0.7057          | 0.8767   |
| 0.0001        | 16.0  | 6000  | 0.6776          | 0.8967   |
| 0.0001        | 17.0  | 6375  | 0.7281          | 0.885    |
| 0.0151        | 18.0  | 6750  | 0.7671          | 0.88     |
| 0.0           | 19.0  | 7125  | 0.7446          | 0.885    |
| 0.0           | 20.0  | 7500  | 0.7595          | 0.885    |
| 0.0           | 21.0  | 7875  | 0.7847          | 0.8833   |
| 0.0104        | 22.0  | 8250  | 0.8045          | 0.8867   |
| 0.0001        | 23.0  | 8625  | 0.8013          | 0.885    |
| 0.0           | 24.0  | 9000  | 0.8150          | 0.8833   |
| 0.0           | 25.0  | 9375  | 0.8170          | 0.885    |
| 0.0           | 26.0  | 9750  | 0.8095          | 0.8883   |
| 0.0           | 27.0  | 10125 | 0.8047          | 0.8867   |
| 0.0           | 28.0  | 10500 | 0.8115          | 0.8867   |
| 0.0           | 29.0  | 10875 | 0.8193          | 0.8867   |
| 0.0071        | 30.0  | 11250 | 0.8281          | 0.8883   |
| 0.0           | 31.0  | 11625 | 0.8141          | 0.89     |
| 0.0           | 32.0  | 12000 | 0.8187          | 0.89     |
| 0.0           | 33.0  | 12375 | 0.8183          | 0.8883   |
| 0.0           | 34.0  | 12750 | 0.8223          | 0.885    |
| 0.0           | 35.0  | 13125 | 0.8200          | 0.8867   |
| 0.0           | 36.0  | 13500 | 0.8256          | 0.8883   |
| 0.0           | 37.0  | 13875 | 0.8249          | 0.8883   |
| 0.0017        | 38.0  | 14250 | 0.8193          | 0.8883   |
| 0.0           | 39.0  | 14625 | 0.8227          | 0.885    |
| 0.0           | 40.0  | 15000 | 0.8247          | 0.89     |
| 0.0           | 41.0  | 15375 | 0.8288          | 0.885    |
| 0.0           | 42.0  | 15750 | 0.8238          | 0.8917   |
| 0.0           | 43.0  | 16125 | 0.8250          | 0.8883   |
| 0.0           | 44.0  | 16500 | 0.8262          | 0.8917   |
| 0.0           | 45.0  | 16875 | 0.8283          | 0.8917   |
| 0.0           | 46.0  | 17250 | 0.8299          | 0.8867   |
| 0.0027        | 47.0  | 17625 | 0.8304          | 0.8867   |
| 0.0           | 48.0  | 18000 | 0.8306          | 0.8867   |
| 0.0           | 49.0  | 18375 | 0.8310          | 0.8867   |
| 0.004         | 50.0  | 18750 | 0.8310          | 0.8883   |


### 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