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# smids_3x_deit_small_sgd_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: 0.5687
- Accuracy: 0.7783

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0247        | 1.0   | 225   | 1.0422          | 0.45     |
| 1.0237        | 2.0   | 450   | 1.0174          | 0.4883   |
| 0.9838        | 3.0   | 675   | 0.9928          | 0.52     |
| 0.9769        | 4.0   | 900   | 0.9683          | 0.555    |
| 0.9529        | 5.0   | 1125  | 0.9446          | 0.5867   |
| 0.9169        | 6.0   | 1350  | 0.9209          | 0.6017   |
| 0.9264        | 7.0   | 1575  | 0.8980          | 0.6083   |
| 0.9011        | 8.0   | 1800  | 0.8753          | 0.6267   |
| 0.8821        | 9.0   | 2025  | 0.8542          | 0.63     |
| 0.8381        | 10.0  | 2250  | 0.8337          | 0.66     |
| 0.8339        | 11.0  | 2475  | 0.8147          | 0.6683   |
| 0.8391        | 12.0  | 2700  | 0.7963          | 0.6833   |
| 0.8165        | 13.0  | 2925  | 0.7789          | 0.6933   |
| 0.736         | 14.0  | 3150  | 0.7623          | 0.7083   |
| 0.7819        | 15.0  | 3375  | 0.7468          | 0.725    |
| 0.7441        | 16.0  | 3600  | 0.7323          | 0.72     |
| 0.7169        | 17.0  | 3825  | 0.7189          | 0.7333   |
| 0.7451        | 18.0  | 4050  | 0.7062          | 0.7383   |
| 0.7048        | 19.0  | 4275  | 0.6943          | 0.74     |
| 0.6589        | 20.0  | 4500  | 0.6832          | 0.745    |
| 0.6884        | 21.0  | 4725  | 0.6730          | 0.7433   |
| 0.7041        | 22.0  | 4950  | 0.6635          | 0.745    |
| 0.6833        | 23.0  | 5175  | 0.6547          | 0.75     |
| 0.6669        | 24.0  | 5400  | 0.6465          | 0.7533   |
| 0.6608        | 25.0  | 5625  | 0.6391          | 0.7517   |
| 0.6311        | 26.0  | 5850  | 0.6322          | 0.7567   |
| 0.6676        | 27.0  | 6075  | 0.6258          | 0.7583   |
| 0.6172        | 28.0  | 6300  | 0.6199          | 0.7617   |
| 0.6339        | 29.0  | 6525  | 0.6146          | 0.765    |
| 0.6134        | 30.0  | 6750  | 0.6096          | 0.7717   |
| 0.6169        | 31.0  | 6975  | 0.6051          | 0.775    |
| 0.5976        | 32.0  | 7200  | 0.6008          | 0.7767   |
| 0.601         | 33.0  | 7425  | 0.5969          | 0.7767   |
| 0.6016        | 34.0  | 7650  | 0.5933          | 0.78     |
| 0.5916        | 35.0  | 7875  | 0.5900          | 0.78     |
| 0.6147        | 36.0  | 8100  | 0.5870          | 0.78     |
| 0.5896        | 37.0  | 8325  | 0.5843          | 0.78     |
| 0.5987        | 38.0  | 8550  | 0.5818          | 0.78     |
| 0.5562        | 39.0  | 8775  | 0.5795          | 0.78     |
| 0.6128        | 40.0  | 9000  | 0.5775          | 0.7817   |
| 0.5635        | 41.0  | 9225  | 0.5757          | 0.78     |
| 0.6047        | 42.0  | 9450  | 0.5742          | 0.78     |
| 0.5584        | 43.0  | 9675  | 0.5728          | 0.78     |
| 0.628         | 44.0  | 9900  | 0.5716          | 0.78     |
| 0.5798        | 45.0  | 10125 | 0.5707          | 0.7783   |
| 0.583         | 46.0  | 10350 | 0.5699          | 0.7783   |
| 0.5729        | 47.0  | 10575 | 0.5694          | 0.7783   |
| 0.5825        | 48.0  | 10800 | 0.5690          | 0.7783   |
| 0.6044        | 49.0  | 11025 | 0.5688          | 0.7783   |
| 0.5946        | 50.0  | 11250 | 0.5687          | 0.7783   |


### 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