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# smids_10x_deit_tiny_rms_0001_fold5

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4754
- Accuracy: 0.83

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8611        | 1.0   | 750   | 0.8101          | 0.59     |
| 0.7337        | 2.0   | 1500  | 0.7328          | 0.5933   |
| 0.6092        | 3.0   | 2250  | 0.7436          | 0.6133   |
| 0.4788        | 4.0   | 3000  | 0.6067          | 0.7117   |
| 0.5206        | 5.0   | 3750  | 0.5059          | 0.79     |
| 0.4534        | 6.0   | 4500  | 0.5164          | 0.7733   |
| 0.4278        | 7.0   | 5250  | 0.5196          | 0.7883   |
| 0.421         | 8.0   | 6000  | 0.4508          | 0.82     |
| 0.3903        | 9.0   | 6750  | 0.4527          | 0.8067   |
| 0.3955        | 10.0  | 7500  | 0.4845          | 0.7867   |
| 0.4005        | 11.0  | 8250  | 0.4149          | 0.835    |
| 0.3221        | 12.0  | 9000  | 0.4996          | 0.815    |
| 0.3587        | 13.0  | 9750  | 0.4119          | 0.8433   |
| 0.2736        | 14.0  | 10500 | 0.4909          | 0.85     |
| 0.2907        | 15.0  | 11250 | 0.4508          | 0.8217   |
| 0.2288        | 16.0  | 12000 | 0.5192          | 0.825    |
| 0.2378        | 17.0  | 12750 | 0.4692          | 0.8383   |
| 0.2297        | 18.0  | 13500 | 0.5883          | 0.8217   |
| 0.2065        | 19.0  | 14250 | 0.4741          | 0.8483   |
| 0.1668        | 20.0  | 15000 | 0.5264          | 0.82     |
| 0.1867        | 21.0  | 15750 | 0.5865          | 0.8217   |
| 0.1397        | 22.0  | 16500 | 0.5812          | 0.8283   |
| 0.15          | 23.0  | 17250 | 0.7165          | 0.83     |
| 0.083         | 24.0  | 18000 | 0.6760          | 0.8267   |
| 0.3309        | 25.0  | 18750 | 0.8204          | 0.79     |
| 0.6907        | 26.0  | 19500 | 0.6426          | 0.7217   |
| 0.4092        | 27.0  | 20250 | 0.7365          | 0.72     |
| 0.4562        | 28.0  | 21000 | 0.4704          | 0.7817   |
| 0.4588        | 29.0  | 21750 | 0.4367          | 0.7983   |
| 0.4089        | 30.0  | 22500 | 0.5011          | 0.7883   |
| 0.4005        | 31.0  | 23250 | 0.5056          | 0.805    |
| 0.2986        | 32.0  | 24000 | 0.4328          | 0.83     |
| 0.3319        | 33.0  | 24750 | 0.5242          | 0.805    |
| 0.2453        | 34.0  | 25500 | 0.4883          | 0.8383   |
| 0.2459        | 35.0  | 26250 | 0.4886          | 0.8233   |
| 0.2873        | 36.0  | 27000 | 0.5046          | 0.8217   |
| 0.1814        | 37.0  | 27750 | 0.5539          | 0.8133   |
| 0.2073        | 38.0  | 28500 | 0.5379          | 0.8233   |
| 0.2284        | 39.0  | 29250 | 0.5765          | 0.815    |
| 0.1577        | 40.0  | 30000 | 0.6100          | 0.8333   |
| 0.1325        | 41.0  | 30750 | 0.6730          | 0.8333   |
| 0.1239        | 42.0  | 31500 | 0.7561          | 0.8333   |
| 0.0741        | 43.0  | 32250 | 0.8428          | 0.8217   |
| 0.0999        | 44.0  | 33000 | 0.9214          | 0.8167   |
| 0.0863        | 45.0  | 33750 | 0.9203          | 0.8367   |
| 0.0855        | 46.0  | 34500 | 1.0656          | 0.83     |
| 0.064         | 47.0  | 35250 | 1.2523          | 0.815    |
| 0.0407        | 48.0  | 36000 | 1.2659          | 0.83     |
| 0.0522        | 49.0  | 36750 | 1.4174          | 0.835    |
| 0.0025        | 50.0  | 37500 | 1.4754          | 0.83     |


### 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