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# smids_1x_deit_small_adamax_0001_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: 1.0934
- Accuracy: 0.8633

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4258        | 1.0   | 75   | 0.3716          | 0.85     |
| 0.2443        | 2.0   | 150  | 0.3887          | 0.8583   |
| 0.2372        | 3.0   | 225  | 0.4276          | 0.865    |
| 0.0872        | 4.0   | 300  | 0.5553          | 0.8433   |
| 0.0572        | 5.0   | 375  | 0.6684          | 0.8267   |
| 0.0647        | 6.0   | 450  | 0.6506          | 0.8467   |
| 0.0399        | 7.0   | 525  | 0.6617          | 0.8633   |
| 0.015         | 8.0   | 600  | 0.7633          | 0.8733   |
| 0.0316        | 9.0   | 675  | 0.8713          | 0.855    |
| 0.0011        | 10.0  | 750  | 0.7531          | 0.8717   |
| 0.0167        | 11.0  | 825  | 0.8491          | 0.855    |
| 0.0001        | 12.0  | 900  | 0.8412          | 0.8617   |
| 0.0003        | 13.0  | 975  | 0.9058          | 0.8633   |
| 0.0001        | 14.0  | 1050 | 0.9354          | 0.8733   |
| 0.0001        | 15.0  | 1125 | 0.9281          | 0.86     |
| 0.0001        | 16.0  | 1200 | 0.9960          | 0.8567   |
| 0.0001        | 17.0  | 1275 | 0.9791          | 0.86     |
| 0.0042        | 18.0  | 1350 | 0.9854          | 0.86     |
| 0.0           | 19.0  | 1425 | 0.9815          | 0.855    |
| 0.0           | 20.0  | 1500 | 0.9947          | 0.8617   |
| 0.0           | 21.0  | 1575 | 1.0042          | 0.865    |
| 0.0           | 22.0  | 1650 | 1.0148          | 0.865    |
| 0.0           | 23.0  | 1725 | 1.0209          | 0.8633   |
| 0.0029        | 24.0  | 1800 | 1.0235          | 0.8633   |
| 0.0033        | 25.0  | 1875 | 1.0287          | 0.8633   |
| 0.0           | 26.0  | 1950 | 1.0248          | 0.865    |
| 0.0           | 27.0  | 2025 | 1.0372          | 0.865    |
| 0.0027        | 28.0  | 2100 | 1.0367          | 0.8633   |
| 0.0041        | 29.0  | 2175 | 1.0380          | 0.8617   |
| 0.0           | 30.0  | 2250 | 1.0450          | 0.8633   |
| 0.0           | 31.0  | 2325 | 1.0524          | 0.865    |
| 0.0           | 32.0  | 2400 | 1.0579          | 0.865    |
| 0.0           | 33.0  | 2475 | 1.0579          | 0.8617   |
| 0.0           | 34.0  | 2550 | 1.0595          | 0.8617   |
| 0.0           | 35.0  | 2625 | 1.0612          | 0.8617   |
| 0.0           | 36.0  | 2700 | 1.0672          | 0.8633   |
| 0.0032        | 37.0  | 2775 | 1.0708          | 0.865    |
| 0.0           | 38.0  | 2850 | 1.0762          | 0.865    |
| 0.0           | 39.0  | 2925 | 1.0803          | 0.865    |
| 0.0           | 40.0  | 3000 | 1.0821          | 0.865    |
| 0.0027        | 41.0  | 3075 | 1.0818          | 0.8633   |
| 0.0           | 42.0  | 3150 | 1.0859          | 0.8633   |
| 0.0           | 43.0  | 3225 | 1.0874          | 0.8633   |
| 0.0           | 44.0  | 3300 | 1.0889          | 0.8633   |
| 0.0           | 45.0  | 3375 | 1.0897          | 0.8617   |
| 0.0           | 46.0  | 3450 | 1.0915          | 0.8633   |
| 0.0           | 47.0  | 3525 | 1.0919          | 0.8633   |
| 0.0           | 48.0  | 3600 | 1.0926          | 0.8633   |
| 0.0           | 49.0  | 3675 | 1.0933          | 0.8633   |
| 0.0           | 50.0  | 3750 | 1.0934          | 0.8633   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

abnormal_sperm non-sperm normal_sperm