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# smids_5x_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: 0.4830
- Accuracy: 0.8233

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0422        | 1.0   | 375   | 1.0355          | 0.445    |
| 0.9877        | 2.0   | 750   | 0.9987          | 0.5017   |
| 0.9301        | 3.0   | 1125  | 0.9591          | 0.5367   |
| 0.9069        | 4.0   | 1500  | 0.9204          | 0.5917   |
| 0.8815        | 5.0   | 1875  | 0.8838          | 0.6217   |
| 0.8208        | 6.0   | 2250  | 0.8478          | 0.6383   |
| 0.7819        | 7.0   | 2625  | 0.8141          | 0.6817   |
| 0.7955        | 8.0   | 3000  | 0.7823          | 0.7033   |
| 0.7492        | 9.0   | 3375  | 0.7528          | 0.7233   |
| 0.7403        | 10.0  | 3750  | 0.7259          | 0.7317   |
| 0.7047        | 11.0  | 4125  | 0.7009          | 0.745    |
| 0.6669        | 12.0  | 4500  | 0.6790          | 0.76     |
| 0.6557        | 13.0  | 4875  | 0.6594          | 0.7667   |
| 0.6563        | 14.0  | 5250  | 0.6418          | 0.77     |
| 0.5999        | 15.0  | 5625  | 0.6263          | 0.7667   |
| 0.589         | 16.0  | 6000  | 0.6125          | 0.77     |
| 0.5618        | 17.0  | 6375  | 0.5999          | 0.7767   |
| 0.5666        | 18.0  | 6750  | 0.5885          | 0.7817   |
| 0.6067        | 19.0  | 7125  | 0.5784          | 0.7867   |
| 0.5796        | 20.0  | 7500  | 0.5694          | 0.79     |
| 0.547         | 21.0  | 7875  | 0.5612          | 0.7883   |
| 0.5698        | 22.0  | 8250  | 0.5540          | 0.7867   |
| 0.5377        | 23.0  | 8625  | 0.5473          | 0.7917   |
| 0.5508        | 24.0  | 9000  | 0.5411          | 0.7967   |
| 0.5752        | 25.0  | 9375  | 0.5355          | 0.7983   |
| 0.5019        | 26.0  | 9750  | 0.5303          | 0.8      |
| 0.5146        | 27.0  | 10125 | 0.5255          | 0.8017   |
| 0.5114        | 28.0  | 10500 | 0.5210          | 0.8033   |
| 0.4588        | 29.0  | 10875 | 0.5170          | 0.8033   |
| 0.5045        | 30.0  | 11250 | 0.5133          | 0.805    |
| 0.5118        | 31.0  | 11625 | 0.5098          | 0.805    |
| 0.4619        | 32.0  | 12000 | 0.5067          | 0.8083   |
| 0.4796        | 33.0  | 12375 | 0.5037          | 0.81     |
| 0.5217        | 34.0  | 12750 | 0.5011          | 0.81     |
| 0.4423        | 35.0  | 13125 | 0.4986          | 0.8133   |
| 0.4692        | 36.0  | 13500 | 0.4964          | 0.815    |
| 0.4889        | 37.0  | 13875 | 0.4944          | 0.815    |
| 0.487         | 38.0  | 14250 | 0.4925          | 0.82     |
| 0.5206        | 39.0  | 14625 | 0.4909          | 0.82     |
| 0.4988        | 40.0  | 15000 | 0.4894          | 0.82     |
| 0.4485        | 41.0  | 15375 | 0.4881          | 0.8217   |
| 0.4284        | 42.0  | 15750 | 0.4870          | 0.8217   |
| 0.4979        | 43.0  | 16125 | 0.4860          | 0.8217   |
| 0.454         | 44.0  | 16500 | 0.4851          | 0.8217   |
| 0.4865        | 45.0  | 16875 | 0.4845          | 0.8217   |
| 0.4847        | 46.0  | 17250 | 0.4839          | 0.8217   |
| 0.5681        | 47.0  | 17625 | 0.4835          | 0.8217   |
| 0.4795        | 48.0  | 18000 | 0.4832          | 0.8217   |
| 0.4757        | 49.0  | 18375 | 0.4831          | 0.8233   |
| 0.4471        | 50.0  | 18750 | 0.4830          | 0.8233   |


### 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