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# smids_3x_deit_tiny_rms_0001_fold4

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: 1.5044
- Accuracy: 0.86

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4145        | 1.0   | 225   | 0.4267          | 0.8217   |
| 0.2495        | 2.0   | 450   | 0.4206          | 0.8567   |
| 0.1493        | 3.0   | 675   | 0.5020          | 0.8333   |
| 0.1961        | 4.0   | 900   | 0.4855          | 0.8517   |
| 0.1126        | 5.0   | 1125  | 0.6130          | 0.8317   |
| 0.0879        | 6.0   | 1350  | 0.5053          | 0.8667   |
| 0.0843        | 7.0   | 1575  | 0.6579          | 0.88     |
| 0.17          | 8.0   | 1800  | 0.6072          | 0.8617   |
| 0.0806        | 9.0   | 2025  | 0.8260          | 0.8517   |
| 0.1058        | 10.0  | 2250  | 0.9813          | 0.8283   |
| 0.0764        | 11.0  | 2475  | 0.8684          | 0.8567   |
| 0.005         | 12.0  | 2700  | 0.9093          | 0.8617   |
| 0.0735        | 13.0  | 2925  | 0.8151          | 0.8583   |
| 0.0859        | 14.0  | 3150  | 0.8528          | 0.87     |
| 0.0467        | 15.0  | 3375  | 1.0959          | 0.8533   |
| 0.0058        | 16.0  | 3600  | 1.0784          | 0.8517   |
| 0.0495        | 17.0  | 3825  | 1.1159          | 0.8467   |
| 0.0916        | 18.0  | 4050  | 1.0811          | 0.87     |
| 0.0002        | 19.0  | 4275  | 1.0640          | 0.855    |
| 0.0104        | 20.0  | 4500  | 1.2921          | 0.845    |
| 0.0459        | 21.0  | 4725  | 0.9148          | 0.87     |
| 0.0016        | 22.0  | 4950  | 1.1604          | 0.86     |
| 0.0121        | 23.0  | 5175  | 1.0782          | 0.855    |
| 0.0453        | 24.0  | 5400  | 1.1706          | 0.8633   |
| 0.0028        | 25.0  | 5625  | 1.1993          | 0.8633   |
| 0.0029        | 26.0  | 5850  | 1.1563          | 0.855    |
| 0.0156        | 27.0  | 6075  | 1.2423          | 0.8533   |
| 0.0005        | 28.0  | 6300  | 1.1448          | 0.8683   |
| 0.0001        | 29.0  | 6525  | 1.2011          | 0.8567   |
| 0.0043        | 30.0  | 6750  | 1.2731          | 0.85     |
| 0.0           | 31.0  | 6975  | 1.2673          | 0.8567   |
| 0.0328        | 32.0  | 7200  | 1.2677          | 0.8517   |
| 0.0122        | 33.0  | 7425  | 1.2900          | 0.8567   |
| 0.0           | 34.0  | 7650  | 1.4452          | 0.855    |
| 0.0           | 35.0  | 7875  | 1.2011          | 0.8733   |
| 0.0           | 36.0  | 8100  | 1.2263          | 0.8583   |
| 0.0009        | 37.0  | 8325  | 1.2364          | 0.87     |
| 0.0           | 38.0  | 8550  | 1.2628          | 0.865    |
| 0.0003        | 39.0  | 8775  | 1.3917          | 0.86     |
| 0.0           | 40.0  | 9000  | 1.4138          | 0.8617   |
| 0.0           | 41.0  | 9225  | 1.4172          | 0.8617   |
| 0.0032        | 42.0  | 9450  | 1.3990          | 0.865    |
| 0.0           | 43.0  | 9675  | 1.4412          | 0.86     |
| 0.0033        | 44.0  | 9900  | 1.4073          | 0.8617   |
| 0.0           | 45.0  | 10125 | 1.5048          | 0.86     |
| 0.0           | 46.0  | 10350 | 1.4842          | 0.8617   |
| 0.0           | 47.0  | 10575 | 1.4970          | 0.86     |
| 0.0           | 48.0  | 10800 | 1.4970          | 0.86     |
| 0.0           | 49.0  | 11025 | 1.5025          | 0.86     |
| 0.0           | 50.0  | 11250 | 1.5044          | 0.86     |


### 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