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# smids_3x_deit_base_sgd_0001_fold2

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6553
- Accuracy: 0.7621

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0893        | 1.0   | 225   | 1.0953          | 0.3594   |
| 1.0864        | 2.0   | 450   | 1.0828          | 0.3794   |
| 1.0622        | 3.0   | 675   | 1.0705          | 0.4160   |
| 1.0459        | 4.0   | 900   | 1.0580          | 0.4509   |
| 1.0341        | 5.0   | 1125  | 1.0447          | 0.4759   |
| 1.0064        | 6.0   | 1350  | 1.0303          | 0.5108   |
| 1.0054        | 7.0   | 1575  | 1.0147          | 0.5341   |
| 0.9891        | 8.0   | 1800  | 0.9981          | 0.5674   |
| 0.9621        | 9.0   | 2025  | 0.9806          | 0.5890   |
| 0.9335        | 10.0  | 2250  | 0.9622          | 0.6040   |
| 0.9046        | 11.0  | 2475  | 0.9439          | 0.6206   |
| 0.9082        | 12.0  | 2700  | 0.9257          | 0.6240   |
| 0.8902        | 13.0  | 2925  | 0.9080          | 0.6323   |
| 0.874         | 14.0  | 3150  | 0.8909          | 0.6406   |
| 0.8732        | 15.0  | 3375  | 0.8749          | 0.6556   |
| 0.8692        | 16.0  | 3600  | 0.8594          | 0.6622   |
| 0.8439        | 17.0  | 3825  | 0.8446          | 0.6755   |
| 0.8058        | 18.0  | 4050  | 0.8304          | 0.6839   |
| 0.7984        | 19.0  | 4275  | 0.8173          | 0.7005   |
| 0.7908        | 20.0  | 4500  | 0.8048          | 0.7055   |
| 0.7871        | 21.0  | 4725  | 0.7931          | 0.7171   |
| 0.8093        | 22.0  | 4950  | 0.7821          | 0.7238   |
| 0.7796        | 23.0  | 5175  | 0.7716          | 0.7288   |
| 0.7539        | 24.0  | 5400  | 0.7616          | 0.7421   |
| 0.756         | 25.0  | 5625  | 0.7522          | 0.7438   |
| 0.733         | 26.0  | 5850  | 0.7434          | 0.7454   |
| 0.7398        | 27.0  | 6075  | 0.7351          | 0.7488   |
| 0.7143        | 28.0  | 6300  | 0.7274          | 0.7488   |
| 0.7155        | 29.0  | 6525  | 0.7202          | 0.7504   |
| 0.7131        | 30.0  | 6750  | 0.7134          | 0.7488   |
| 0.6651        | 31.0  | 6975  | 0.7071          | 0.7488   |
| 0.6923        | 32.0  | 7200  | 0.7012          | 0.7521   |
| 0.674         | 33.0  | 7425  | 0.6957          | 0.7521   |
| 0.6414        | 34.0  | 7650  | 0.6907          | 0.7537   |
| 0.6882        | 35.0  | 7875  | 0.6860          | 0.7571   |
| 0.6787        | 36.0  | 8100  | 0.6817          | 0.7571   |
| 0.6546        | 37.0  | 8325  | 0.6777          | 0.7587   |
| 0.6468        | 38.0  | 8550  | 0.6741          | 0.7587   |
| 0.6253        | 39.0  | 8775  | 0.6708          | 0.7554   |
| 0.6533        | 40.0  | 9000  | 0.6679          | 0.7571   |
| 0.6108        | 41.0  | 9225  | 0.6653          | 0.7571   |
| 0.6451        | 42.0  | 9450  | 0.6630          | 0.7587   |
| 0.6128        | 43.0  | 9675  | 0.6611          | 0.7604   |
| 0.6504        | 44.0  | 9900  | 0.6594          | 0.7604   |
| 0.6566        | 45.0  | 10125 | 0.6581          | 0.7621   |
| 0.6606        | 46.0  | 10350 | 0.6570          | 0.7621   |
| 0.615         | 47.0  | 10575 | 0.6562          | 0.7621   |
| 0.653         | 48.0  | 10800 | 0.6557          | 0.7621   |
| 0.6663        | 49.0  | 11025 | 0.6554          | 0.7621   |
| 0.6208        | 50.0  | 11250 | 0.6553          | 0.7621   |


### 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