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# smids_10x_deit_tiny_sgd_001_fold4

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: 0.3938
- Accuracy: 0.8667

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5453        | 1.0   | 750   | 0.5623          | 0.7633   |
| 0.3882        | 2.0   | 1500  | 0.4483          | 0.8183   |
| 0.3799        | 3.0   | 2250  | 0.4088          | 0.8317   |
| 0.3643        | 4.0   | 3000  | 0.3893          | 0.8383   |
| 0.2628        | 5.0   | 3750  | 0.3770          | 0.8467   |
| 0.2344        | 6.0   | 4500  | 0.3757          | 0.8467   |
| 0.2158        | 7.0   | 5250  | 0.3640          | 0.8583   |
| 0.2518        | 8.0   | 6000  | 0.3700          | 0.86     |
| 0.2784        | 9.0   | 6750  | 0.3645          | 0.8617   |
| 0.2124        | 10.0  | 7500  | 0.3619          | 0.86     |
| 0.2508        | 11.0  | 8250  | 0.3628          | 0.8583   |
| 0.2963        | 12.0  | 9000  | 0.3717          | 0.86     |
| 0.2464        | 13.0  | 9750  | 0.3675          | 0.86     |
| 0.2153        | 14.0  | 10500 | 0.3661          | 0.8633   |
| 0.1783        | 15.0  | 11250 | 0.3637          | 0.8633   |
| 0.1889        | 16.0  | 12000 | 0.3675          | 0.865    |
| 0.1615        | 17.0  | 12750 | 0.3615          | 0.8633   |
| 0.1602        | 18.0  | 13500 | 0.3665          | 0.8683   |
| 0.2382        | 19.0  | 14250 | 0.3640          | 0.8633   |
| 0.1431        | 20.0  | 15000 | 0.3640          | 0.8667   |
| 0.1246        | 21.0  | 15750 | 0.3698          | 0.865    |
| 0.1642        | 22.0  | 16500 | 0.3698          | 0.8617   |
| 0.1435        | 23.0  | 17250 | 0.3719          | 0.8617   |
| 0.184         | 24.0  | 18000 | 0.3745          | 0.865    |
| 0.1543        | 25.0  | 18750 | 0.3749          | 0.8617   |
| 0.1463        | 26.0  | 19500 | 0.3762          | 0.8633   |
| 0.1225        | 27.0  | 20250 | 0.3737          | 0.8667   |
| 0.1542        | 28.0  | 21000 | 0.3785          | 0.865    |
| 0.1065        | 29.0  | 21750 | 0.3788          | 0.87     |
| 0.1351        | 30.0  | 22500 | 0.3799          | 0.8667   |
| 0.1281        | 31.0  | 23250 | 0.3825          | 0.8667   |
| 0.1337        | 32.0  | 24000 | 0.3866          | 0.8633   |
| 0.1066        | 33.0  | 24750 | 0.3848          | 0.8667   |
| 0.1503        | 34.0  | 25500 | 0.3856          | 0.87     |
| 0.0933        | 35.0  | 26250 | 0.3837          | 0.8717   |
| 0.1119        | 36.0  | 27000 | 0.3871          | 0.87     |
| 0.0916        | 37.0  | 27750 | 0.3845          | 0.87     |
| 0.1419        | 38.0  | 28500 | 0.3888          | 0.8683   |
| 0.1831        | 39.0  | 29250 | 0.3865          | 0.87     |
| 0.1443        | 40.0  | 30000 | 0.3886          | 0.8683   |
| 0.1089        | 41.0  | 30750 | 0.3938          | 0.865    |
| 0.0931        | 42.0  | 31500 | 0.3903          | 0.8683   |
| 0.1349        | 43.0  | 32250 | 0.3917          | 0.8683   |
| 0.1005        | 44.0  | 33000 | 0.3917          | 0.8667   |
| 0.12          | 45.0  | 33750 | 0.3918          | 0.8667   |
| 0.1354        | 46.0  | 34500 | 0.3924          | 0.8667   |
| 0.0817        | 47.0  | 35250 | 0.3922          | 0.8667   |
| 0.0828        | 48.0  | 36000 | 0.3931          | 0.8667   |
| 0.0941        | 49.0  | 36750 | 0.3938          | 0.8667   |
| 0.0837        | 50.0  | 37500 | 0.3938          | 0.8667   |


### 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