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# smids_3x_deit_small_sgd_0001_fold2

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: 0.5927
- Accuracy: 0.7820

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0529        | 1.0   | 225   | 1.0464          | 0.4542   |
| 1.0393        | 2.0   | 450   | 1.0215          | 0.4759   |
| 1.0194        | 3.0   | 675   | 0.9971          | 0.5158   |
| 0.9608        | 4.0   | 900   | 0.9729          | 0.5541   |
| 0.9743        | 5.0   | 1125  | 0.9487          | 0.6023   |
| 0.9002        | 6.0   | 1350  | 0.9258          | 0.6206   |
| 0.8961        | 7.0   | 1575  | 0.9030          | 0.6373   |
| 0.9282        | 8.0   | 1800  | 0.8813          | 0.6539   |
| 0.856         | 9.0   | 2025  | 0.8605          | 0.6705   |
| 0.8441        | 10.0  | 2250  | 0.8407          | 0.6772   |
| 0.8723        | 11.0  | 2475  | 0.8225          | 0.6839   |
| 0.7789        | 12.0  | 2700  | 0.8048          | 0.6955   |
| 0.7952        | 13.0  | 2925  | 0.7885          | 0.7055   |
| 0.7937        | 14.0  | 3150  | 0.7729          | 0.7155   |
| 0.8007        | 15.0  | 3375  | 0.7585          | 0.7255   |
| 0.769         | 16.0  | 3600  | 0.7449          | 0.7238   |
| 0.7262        | 17.0  | 3825  | 0.7325          | 0.7255   |
| 0.7259        | 18.0  | 4050  | 0.7208          | 0.7238   |
| 0.7176        | 19.0  | 4275  | 0.7099          | 0.7255   |
| 0.6791        | 20.0  | 4500  | 0.6998          | 0.7271   |
| 0.7106        | 21.0  | 4725  | 0.6905          | 0.7338   |
| 0.6951        | 22.0  | 4950  | 0.6819          | 0.7371   |
| 0.7193        | 23.0  | 5175  | 0.6739          | 0.7471   |
| 0.6759        | 24.0  | 5400  | 0.6663          | 0.7521   |
| 0.6975        | 25.0  | 5625  | 0.6593          | 0.7537   |
| 0.6391        | 26.0  | 5850  | 0.6529          | 0.7571   |
| 0.6617        | 27.0  | 6075  | 0.6469          | 0.7604   |
| 0.6434        | 28.0  | 6300  | 0.6413          | 0.7604   |
| 0.6619        | 29.0  | 6525  | 0.6362          | 0.7587   |
| 0.6444        | 30.0  | 6750  | 0.6315          | 0.7571   |
| 0.6161        | 31.0  | 6975  | 0.6270          | 0.7604   |
| 0.6193        | 32.0  | 7200  | 0.6230          | 0.7671   |
| 0.5926        | 33.0  | 7425  | 0.6193          | 0.7654   |
| 0.5861        | 34.0  | 7650  | 0.6159          | 0.7754   |
| 0.6256        | 35.0  | 7875  | 0.6127          | 0.7770   |
| 0.6099        | 36.0  | 8100  | 0.6099          | 0.7754   |
| 0.5932        | 37.0  | 8325  | 0.6073          | 0.7770   |
| 0.5988        | 38.0  | 8550  | 0.6049          | 0.7804   |
| 0.574         | 39.0  | 8775  | 0.6028          | 0.7787   |
| 0.5835        | 40.0  | 9000  | 0.6009          | 0.7787   |
| 0.5292        | 41.0  | 9225  | 0.5992          | 0.7787   |
| 0.586         | 42.0  | 9450  | 0.5977          | 0.7804   |
| 0.5537        | 43.0  | 9675  | 0.5964          | 0.7820   |
| 0.5573        | 44.0  | 9900  | 0.5953          | 0.7837   |
| 0.5715        | 45.0  | 10125 | 0.5945          | 0.7820   |
| 0.6072        | 46.0  | 10350 | 0.5938          | 0.7820   |
| 0.5714        | 47.0  | 10575 | 0.5933          | 0.7837   |
| 0.5684        | 48.0  | 10800 | 0.5929          | 0.7820   |
| 0.5949        | 49.0  | 11025 | 0.5927          | 0.7820   |
| 0.5423        | 50.0  | 11250 | 0.5927          | 0.7820   |


### 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