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# smids_3x_deit_tiny_rms_001_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: 1.6588
- Accuracy: 0.8167

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.972         | 1.0   | 225   | 1.0247          | 0.3783   |
| 0.8568        | 2.0   | 450   | 0.8274          | 0.5533   |
| 0.8081        | 3.0   | 675   | 0.8040          | 0.5483   |
| 0.7625        | 4.0   | 900   | 0.7546          | 0.5933   |
| 0.7576        | 5.0   | 1125  | 0.7605          | 0.64     |
| 0.6884        | 6.0   | 1350  | 0.7873          | 0.5983   |
| 0.766         | 7.0   | 1575  | 0.7270          | 0.6583   |
| 0.6978        | 8.0   | 1800  | 0.7176          | 0.64     |
| 0.6732        | 9.0   | 2025  | 0.7347          | 0.645    |
| 0.6839        | 10.0  | 2250  | 0.7289          | 0.6267   |
| 0.6569        | 11.0  | 2475  | 0.6542          | 0.7183   |
| 0.6318        | 12.0  | 2700  | 0.6186          | 0.7283   |
| 0.6796        | 13.0  | 2925  | 0.6663          | 0.71     |
| 0.6092        | 14.0  | 3150  | 0.6155          | 0.7117   |
| 0.6242        | 15.0  | 3375  | 0.6625          | 0.6967   |
| 0.5314        | 16.0  | 3600  | 0.5775          | 0.7533   |
| 0.5564        | 17.0  | 3825  | 0.5848          | 0.7533   |
| 0.5755        | 18.0  | 4050  | 0.5751          | 0.7583   |
| 0.5677        | 19.0  | 4275  | 0.5731          | 0.7617   |
| 0.5761        | 20.0  | 4500  | 0.5204          | 0.785    |
| 0.4524        | 21.0  | 4725  | 0.5722          | 0.75     |
| 0.4782        | 22.0  | 4950  | 0.5385          | 0.7733   |
| 0.4908        | 23.0  | 5175  | 0.5176          | 0.7933   |
| 0.5195        | 24.0  | 5400  | 0.5242          | 0.7917   |
| 0.4871        | 25.0  | 5625  | 0.5298          | 0.7983   |
| 0.5293        | 26.0  | 5850  | 0.5066          | 0.8      |
| 0.504         | 27.0  | 6075  | 0.4969          | 0.81     |
| 0.4467        | 28.0  | 6300  | 0.5630          | 0.79     |
| 0.4177        | 29.0  | 6525  | 0.5247          | 0.8067   |
| 0.3722        | 30.0  | 6750  | 0.5359          | 0.8117   |
| 0.3286        | 31.0  | 6975  | 0.5623          | 0.795    |
| 0.3205        | 32.0  | 7200  | 0.5594          | 0.8017   |
| 0.3627        | 33.0  | 7425  | 0.5968          | 0.815    |
| 0.2799        | 34.0  | 7650  | 0.5562          | 0.825    |
| 0.2664        | 35.0  | 7875  | 0.6268          | 0.81     |
| 0.2603        | 36.0  | 8100  | 0.6102          | 0.82     |
| 0.2382        | 37.0  | 8325  | 0.6448          | 0.8083   |
| 0.1999        | 38.0  | 8550  | 0.7396          | 0.825    |
| 0.1413        | 39.0  | 8775  | 0.7329          | 0.8167   |
| 0.1906        | 40.0  | 9000  | 0.8804          | 0.81     |
| 0.1179        | 41.0  | 9225  | 0.7998          | 0.84     |
| 0.0965        | 42.0  | 9450  | 0.9317          | 0.8217   |
| 0.0987        | 43.0  | 9675  | 0.9015          | 0.825    |
| 0.1035        | 44.0  | 9900  | 1.1023          | 0.8083   |
| 0.0347        | 45.0  | 10125 | 1.2315          | 0.82     |
| 0.054         | 46.0  | 10350 | 1.2317          | 0.8083   |
| 0.014         | 47.0  | 10575 | 1.4229          | 0.82     |
| 0.0044        | 48.0  | 10800 | 1.5732          | 0.8217   |
| 0.0012        | 49.0  | 11025 | 1.6140          | 0.8183   |
| 0.0003        | 50.0  | 11250 | 1.6588          | 0.8167   |


### 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