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# smids_10x_deit_small_sgd_00001_fold3

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: 0.9545
- Accuracy: 0.5483

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0617        | 1.0   | 750   | 1.0842          | 0.3733   |
| 1.0585        | 2.0   | 1500  | 1.0794          | 0.37     |
| 1.0424        | 3.0   | 2250  | 1.0744          | 0.3733   |
| 1.0456        | 4.0   | 3000  | 1.0693          | 0.3767   |
| 1.0291        | 5.0   | 3750  | 1.0643          | 0.3833   |
| 1.0038        | 6.0   | 4500  | 1.0594          | 0.3917   |
| 1.0218        | 7.0   | 5250  | 1.0545          | 0.4017   |
| 1.0056        | 8.0   | 6000  | 1.0497          | 0.4083   |
| 0.9993        | 9.0   | 6750  | 1.0451          | 0.4133   |
| 0.9987        | 10.0  | 7500  | 1.0406          | 0.4233   |
| 1.005         | 11.0  | 8250  | 1.0361          | 0.43     |
| 0.9768        | 12.0  | 9000  | 1.0318          | 0.4367   |
| 0.9767        | 13.0  | 9750  | 1.0276          | 0.4383   |
| 0.9832        | 14.0  | 10500 | 1.0235          | 0.4417   |
| 0.9795        | 15.0  | 11250 | 1.0196          | 0.4517   |
| 0.9438        | 16.0  | 12000 | 1.0158          | 0.47     |
| 0.9511        | 17.0  | 12750 | 1.0122          | 0.4733   |
| 0.9685        | 18.0  | 13500 | 1.0086          | 0.475    |
| 0.9616        | 19.0  | 14250 | 1.0051          | 0.4833   |
| 0.9593        | 20.0  | 15000 | 1.0018          | 0.485    |
| 0.9173        | 21.0  | 15750 | 0.9985          | 0.49     |
| 0.9516        | 22.0  | 16500 | 0.9954          | 0.5017   |
| 0.9352        | 23.0  | 17250 | 0.9923          | 0.5033   |
| 0.9563        | 24.0  | 18000 | 0.9894          | 0.5083   |
| 0.9134        | 25.0  | 18750 | 0.9866          | 0.5117   |
| 0.9284        | 26.0  | 19500 | 0.9839          | 0.515    |
| 0.8974        | 27.0  | 20250 | 0.9813          | 0.52     |
| 0.9371        | 28.0  | 21000 | 0.9789          | 0.52     |
| 0.8946        | 29.0  | 21750 | 0.9765          | 0.5283   |
| 0.9089        | 30.0  | 22500 | 0.9743          | 0.5317   |
| 0.9026        | 31.0  | 23250 | 0.9722          | 0.5333   |
| 0.9027        | 32.0  | 24000 | 0.9702          | 0.5317   |
| 0.9034        | 33.0  | 24750 | 0.9683          | 0.5333   |
| 0.9095        | 34.0  | 25500 | 0.9666          | 0.5333   |
| 0.8767        | 35.0  | 26250 | 0.9650          | 0.5367   |
| 0.8854        | 36.0  | 27000 | 0.9635          | 0.5367   |
| 0.8862        | 37.0  | 27750 | 0.9621          | 0.5367   |
| 0.9211        | 38.0  | 28500 | 0.9608          | 0.5367   |
| 0.8993        | 39.0  | 29250 | 0.9597          | 0.535    |
| 0.8897        | 40.0  | 30000 | 0.9587          | 0.5383   |
| 0.8933        | 41.0  | 30750 | 0.9578          | 0.5417   |
| 0.8954        | 42.0  | 31500 | 0.9571          | 0.5483   |
| 0.887         | 43.0  | 32250 | 0.9564          | 0.5483   |
| 0.902         | 44.0  | 33000 | 0.9558          | 0.5483   |
| 0.8561        | 45.0  | 33750 | 0.9554          | 0.5483   |
| 0.8814        | 46.0  | 34500 | 0.9551          | 0.5483   |
| 0.8975        | 47.0  | 35250 | 0.9548          | 0.5483   |
| 0.8624        | 48.0  | 36000 | 0.9546          | 0.5483   |
| 0.8832        | 49.0  | 36750 | 0.9546          | 0.5483   |
| 0.8754        | 50.0  | 37500 | 0.9545          | 0.5483   |


### 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