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# smids_10x_deit_tiny_rms_0001_fold2

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: 1.7512
- Accuracy: 0.8469

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7916        | 1.0   | 750   | 0.7607          | 0.5990   |
| 0.7137        | 2.0   | 1500  | 0.7292          | 0.6922   |
| 0.7389        | 3.0   | 2250  | 0.6487          | 0.6905   |
| 0.641         | 4.0   | 3000  | 0.6309          | 0.7055   |
| 0.6314        | 5.0   | 3750  | 0.6856          | 0.6905   |
| 0.5224        | 6.0   | 4500  | 0.5750          | 0.7554   |
| 0.5522        | 7.0   | 5250  | 0.5498          | 0.7537   |
| 0.5542        | 8.0   | 6000  | 0.5375          | 0.7587   |
| 0.5077        | 9.0   | 6750  | 0.5514          | 0.7604   |
| 0.4953        | 10.0  | 7500  | 0.5328          | 0.7870   |
| 0.4839        | 11.0  | 8250  | 0.5735          | 0.7454   |
| 0.5867        | 12.0  | 9000  | 0.5344          | 0.7820   |
| 0.5019        | 13.0  | 9750  | 0.5724          | 0.7720   |
| 0.4158        | 14.0  | 10500 | 0.5203          | 0.7870   |
| 0.338         | 15.0  | 11250 | 0.5452          | 0.7903   |
| 0.3872        | 16.0  | 12000 | 0.4927          | 0.8070   |
| 0.3271        | 17.0  | 12750 | 0.5792          | 0.8070   |
| 0.3473        | 18.0  | 13500 | 0.4754          | 0.8203   |
| 0.3721        | 19.0  | 14250 | 0.4949          | 0.7887   |
| 0.3629        | 20.0  | 15000 | 0.4870          | 0.8136   |
| 0.3126        | 21.0  | 15750 | 0.4639          | 0.8303   |
| 0.2547        | 22.0  | 16500 | 0.5296          | 0.8236   |
| 0.3002        | 23.0  | 17250 | 0.5506          | 0.8003   |
| 0.2834        | 24.0  | 18000 | 0.5134          | 0.8186   |
| 0.2447        | 25.0  | 18750 | 0.6771          | 0.8053   |
| 0.211         | 26.0  | 19500 | 0.5840          | 0.8120   |
| 0.2265        | 27.0  | 20250 | 0.6139          | 0.8369   |
| 0.2018        | 28.0  | 21000 | 0.7828          | 0.8203   |
| 0.1385        | 29.0  | 21750 | 0.7229          | 0.8186   |
| 0.1626        | 30.0  | 22500 | 0.6948          | 0.8369   |
| 0.1046        | 31.0  | 23250 | 0.7987          | 0.8419   |
| 0.0986        | 32.0  | 24000 | 0.6857          | 0.8552   |
| 0.1014        | 33.0  | 24750 | 0.9042          | 0.8303   |
| 0.0734        | 34.0  | 25500 | 0.9163          | 0.8403   |
| 0.03          | 35.0  | 26250 | 0.9836          | 0.8353   |
| 0.0303        | 36.0  | 27000 | 0.9935          | 0.8436   |
| 0.0569        | 37.0  | 27750 | 1.1088          | 0.8286   |
| 0.0188        | 38.0  | 28500 | 1.2743          | 0.8319   |
| 0.0192        | 39.0  | 29250 | 1.3350          | 0.8386   |
| 0.032         | 40.0  | 30000 | 1.3569          | 0.8419   |
| 0.01          | 41.0  | 30750 | 1.2223          | 0.8353   |
| 0.0249        | 42.0  | 31500 | 1.4712          | 0.8436   |
| 0.0031        | 43.0  | 32250 | 1.5420          | 0.8403   |
| 0.0008        | 44.0  | 33000 | 1.5564          | 0.8419   |
| 0.026         | 45.0  | 33750 | 1.5884          | 0.8502   |
| 0.0017        | 46.0  | 34500 | 1.7455          | 0.8386   |
| 0.0001        | 47.0  | 35250 | 1.7636          | 0.8353   |
| 0.0001        | 48.0  | 36000 | 1.7269          | 0.8453   |
| 0.0           | 49.0  | 36750 | 1.7457          | 0.8453   |
| 0.0           | 50.0  | 37500 | 1.7512          | 0.8469   |


### 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