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# smids_3x_deit_tiny_rms_00001_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: 1.3270
- Accuracy: 0.8783

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3199        | 1.0   | 225   | 0.3628          | 0.8533   |
| 0.1542        | 2.0   | 450   | 0.3942          | 0.8633   |
| 0.1186        | 3.0   | 675   | 0.4019          | 0.8783   |
| 0.1068        | 4.0   | 900   | 0.4521          | 0.8667   |
| 0.0629        | 5.0   | 1125  | 0.5068          | 0.8733   |
| 0.0347        | 6.0   | 1350  | 0.6175          | 0.8733   |
| 0.0294        | 7.0   | 1575  | 0.7043          | 0.8683   |
| 0.0692        | 8.0   | 1800  | 0.7322          | 0.8683   |
| 0.0709        | 9.0   | 2025  | 0.8957          | 0.8683   |
| 0.0668        | 10.0  | 2250  | 0.9618          | 0.8633   |
| 0.0043        | 11.0  | 2475  | 1.0931          | 0.855    |
| 0.0053        | 12.0  | 2700  | 1.0669          | 0.8617   |
| 0.0043        | 13.0  | 2925  | 1.2308          | 0.8533   |
| 0.0002        | 14.0  | 3150  | 1.1863          | 0.86     |
| 0.0021        | 15.0  | 3375  | 1.1504          | 0.8633   |
| 0.0           | 16.0  | 3600  | 1.1831          | 0.8683   |
| 0.0542        | 17.0  | 3825  | 1.2418          | 0.8633   |
| 0.0002        | 18.0  | 4050  | 1.2334          | 0.8567   |
| 0.0           | 19.0  | 4275  | 1.2760          | 0.8717   |
| 0.0018        | 20.0  | 4500  | 1.3796          | 0.8567   |
| 0.0           | 21.0  | 4725  | 1.2762          | 0.8683   |
| 0.0           | 22.0  | 4950  | 1.3847          | 0.8583   |
| 0.0001        | 23.0  | 5175  | 1.2718          | 0.8733   |
| 0.0           | 24.0  | 5400  | 1.2359          | 0.8717   |
| 0.0           | 25.0  | 5625  | 1.1935          | 0.8783   |
| 0.0           | 26.0  | 5850  | 1.2054          | 0.8783   |
| 0.0           | 27.0  | 6075  | 1.3706          | 0.86     |
| 0.0           | 28.0  | 6300  | 1.3639          | 0.8683   |
| 0.0           | 29.0  | 6525  | 1.2958          | 0.865    |
| 0.0           | 30.0  | 6750  | 1.3600          | 0.8617   |
| 0.0001        | 31.0  | 6975  | 1.4078          | 0.865    |
| 0.0           | 32.0  | 7200  | 1.3542          | 0.8667   |
| 0.0           | 33.0  | 7425  | 1.3120          | 0.865    |
| 0.0           | 34.0  | 7650  | 1.2757          | 0.8717   |
| 0.0           | 35.0  | 7875  | 1.2928          | 0.8667   |
| 0.0           | 36.0  | 8100  | 1.3460          | 0.8667   |
| 0.0002        | 37.0  | 8325  | 1.3775          | 0.8633   |
| 0.0           | 38.0  | 8550  | 1.3704          | 0.8617   |
| 0.0           | 39.0  | 8775  | 1.3338          | 0.87     |
| 0.0           | 40.0  | 9000  | 1.3261          | 0.8717   |
| 0.0           | 41.0  | 9225  | 1.3217          | 0.8717   |
| 0.0068        | 42.0  | 9450  | 1.3066          | 0.8733   |
| 0.0           | 43.0  | 9675  | 1.3141          | 0.875    |
| 0.0059        | 44.0  | 9900  | 1.3176          | 0.8767   |
| 0.0           | 45.0  | 10125 | 1.3464          | 0.865    |
| 0.0           | 46.0  | 10350 | 1.3263          | 0.8733   |
| 0.0           | 47.0  | 10575 | 1.3244          | 0.875    |
| 0.0           | 48.0  | 10800 | 1.3245          | 0.8767   |
| 0.0           | 49.0  | 11025 | 1.3277          | 0.8783   |
| 0.0           | 50.0  | 11250 | 1.3270          | 0.8783   |


### 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