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# smids_10x_deit_small_adamax_001_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: 0.9313
- Accuracy: 0.905

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3135        | 1.0   | 750   | 0.2998          | 0.8733   |
| 0.2768        | 2.0   | 1500  | 0.4449          | 0.8417   |
| 0.2302        | 3.0   | 2250  | 0.3152          | 0.885    |
| 0.1705        | 4.0   | 3000  | 0.4052          | 0.8717   |
| 0.1542        | 5.0   | 3750  | 0.3463          | 0.9017   |
| 0.1492        | 6.0   | 4500  | 0.3620          | 0.8967   |
| 0.0963        | 7.0   | 5250  | 0.3599          | 0.895    |
| 0.0922        | 8.0   | 6000  | 0.4809          | 0.8883   |
| 0.1185        | 9.0   | 6750  | 0.5269          | 0.9017   |
| 0.0382        | 10.0  | 7500  | 0.5699          | 0.8983   |
| 0.0415        | 11.0  | 8250  | 0.4670          | 0.9033   |
| 0.0358        | 12.0  | 9000  | 0.4714          | 0.9083   |
| 0.0301        | 13.0  | 9750  | 0.4780          | 0.915    |
| 0.0178        | 14.0  | 10500 | 0.5327          | 0.9033   |
| 0.0172        | 15.0  | 11250 | 0.6375          | 0.8983   |
| 0.0148        | 16.0  | 12000 | 0.5566          | 0.8967   |
| 0.0235        | 17.0  | 12750 | 0.5739          | 0.9067   |
| 0.0298        | 18.0  | 13500 | 0.7210          | 0.8983   |
| 0.0012        | 19.0  | 14250 | 0.7611          | 0.8883   |
| 0.0391        | 20.0  | 15000 | 0.8089          | 0.8917   |
| 0.0002        | 21.0  | 15750 | 0.6460          | 0.8983   |
| 0.0095        | 22.0  | 16500 | 0.6954          | 0.9067   |
| 0.0251        | 23.0  | 17250 | 0.6718          | 0.9017   |
| 0.0021        | 24.0  | 18000 | 0.6374          | 0.9067   |
| 0.0001        | 25.0  | 18750 | 0.6533          | 0.905    |
| 0.0001        | 26.0  | 19500 | 0.7022          | 0.91     |
| 0.003         | 27.0  | 20250 | 0.8113          | 0.9      |
| 0.0277        | 28.0  | 21000 | 0.7402          | 0.8983   |
| 0.0056        | 29.0  | 21750 | 0.7949          | 0.8967   |
| 0.007         | 30.0  | 22500 | 0.8055          | 0.8967   |
| 0.0001        | 31.0  | 23250 | 0.8426          | 0.9083   |
| 0.0           | 32.0  | 24000 | 0.8618          | 0.905    |
| 0.0           | 33.0  | 24750 | 0.8392          | 0.9083   |
| 0.0           | 34.0  | 25500 | 0.8019          | 0.9067   |
| 0.0           | 35.0  | 26250 | 0.8163          | 0.9067   |
| 0.0           | 36.0  | 27000 | 0.8994          | 0.895    |
| 0.0037        | 37.0  | 27750 | 0.8599          | 0.9067   |
| 0.0           | 38.0  | 28500 | 0.8721          | 0.905    |
| 0.0           | 39.0  | 29250 | 0.8612          | 0.9067   |
| 0.0           | 40.0  | 30000 | 0.9150          | 0.9033   |
| 0.0           | 41.0  | 30750 | 0.8804          | 0.91     |
| 0.0           | 42.0  | 31500 | 0.8814          | 0.9067   |
| 0.0           | 43.0  | 32250 | 0.8966          | 0.9083   |
| 0.0           | 44.0  | 33000 | 0.9028          | 0.9083   |
| 0.0           | 45.0  | 33750 | 0.9087          | 0.9083   |
| 0.0           | 46.0  | 34500 | 0.9131          | 0.9083   |
| 0.0           | 47.0  | 35250 | 0.9195          | 0.9067   |
| 0.0           | 48.0  | 36000 | 0.9269          | 0.9067   |
| 0.0           | 49.0  | 36750 | 0.9312          | 0.9067   |
| 0.0           | 50.0  | 37500 | 0.9313          | 0.905    |


### 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