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# smids_5x_deit_small_sgd_0001_fold5

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: 0.4899
- Accuracy: 0.8117

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0575        | 1.0   | 375   | 1.0409          | 0.4667   |
| 0.9896        | 2.0   | 750   | 1.0031          | 0.5117   |
| 0.9428        | 3.0   | 1125  | 0.9645          | 0.5567   |
| 0.9186        | 4.0   | 1500  | 0.9265          | 0.615    |
| 0.8922        | 5.0   | 1875  | 0.8895          | 0.6483   |
| 0.8541        | 6.0   | 2250  | 0.8539          | 0.6717   |
| 0.7885        | 7.0   | 2625  | 0.8194          | 0.69     |
| 0.7714        | 8.0   | 3000  | 0.7879          | 0.705    |
| 0.758         | 9.0   | 3375  | 0.7592          | 0.7133   |
| 0.7212        | 10.0  | 3750  | 0.7334          | 0.7217   |
| 0.6793        | 11.0  | 4125  | 0.7102          | 0.7333   |
| 0.6484        | 12.0  | 4500  | 0.6895          | 0.7367   |
| 0.6765        | 13.0  | 4875  | 0.6713          | 0.7467   |
| 0.664         | 14.0  | 5250  | 0.6548          | 0.7533   |
| 0.6332        | 15.0  | 5625  | 0.6395          | 0.7617   |
| 0.5983        | 16.0  | 6000  | 0.6261          | 0.77     |
| 0.6122        | 17.0  | 6375  | 0.6142          | 0.77     |
| 0.5912        | 18.0  | 6750  | 0.6024          | 0.7733   |
| 0.5764        | 19.0  | 7125  | 0.5918          | 0.775    |
| 0.5461        | 20.0  | 7500  | 0.5824          | 0.7783   |
| 0.5245        | 21.0  | 7875  | 0.5733          | 0.7833   |
| 0.5339        | 22.0  | 8250  | 0.5654          | 0.7867   |
| 0.5651        | 23.0  | 8625  | 0.5584          | 0.7867   |
| 0.5365        | 24.0  | 9000  | 0.5518          | 0.7933   |
| 0.4982        | 25.0  | 9375  | 0.5457          | 0.795    |
| 0.5274        | 26.0  | 9750  | 0.5402          | 0.7933   |
| 0.5167        | 27.0  | 10125 | 0.5353          | 0.795    |
| 0.53          | 28.0  | 10500 | 0.5303          | 0.7967   |
| 0.5404        | 29.0  | 10875 | 0.5260          | 0.7967   |
| 0.4414        | 30.0  | 11250 | 0.5222          | 0.8017   |
| 0.5269        | 31.0  | 11625 | 0.5183          | 0.8017   |
| 0.5299        | 32.0  | 12000 | 0.5150          | 0.8017   |
| 0.5311        | 33.0  | 12375 | 0.5120          | 0.8033   |
| 0.499         | 34.0  | 12750 | 0.5091          | 0.8033   |
| 0.4712        | 35.0  | 13125 | 0.5065          | 0.8033   |
| 0.4169        | 36.0  | 13500 | 0.5042          | 0.8017   |
| 0.4803        | 37.0  | 13875 | 0.5020          | 0.8017   |
| 0.4796        | 38.0  | 14250 | 0.5001          | 0.805    |
| 0.4865        | 39.0  | 14625 | 0.4984          | 0.8067   |
| 0.5122        | 40.0  | 15000 | 0.4967          | 0.8083   |
| 0.4785        | 41.0  | 15375 | 0.4953          | 0.8067   |
| 0.4562        | 42.0  | 15750 | 0.4941          | 0.8083   |
| 0.5248        | 43.0  | 16125 | 0.4930          | 0.8117   |
| 0.4817        | 44.0  | 16500 | 0.4922          | 0.8117   |
| 0.4662        | 45.0  | 16875 | 0.4914          | 0.8117   |
| 0.4968        | 46.0  | 17250 | 0.4908          | 0.8117   |
| 0.5157        | 47.0  | 17625 | 0.4904          | 0.8117   |
| 0.4378        | 48.0  | 18000 | 0.4901          | 0.8117   |
| 0.4668        | 49.0  | 18375 | 0.4899          | 0.8117   |
| 0.4722        | 50.0  | 18750 | 0.4899          | 0.8117   |


### 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