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# smids_3x_deit_small_rms_00001_fold4

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2799
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2819        | 1.0   | 225   | 0.3374          | 0.875    |
| 0.0966        | 2.0   | 450   | 0.3633          | 0.8733   |
| 0.0536        | 3.0   | 675   | 0.4166          | 0.8633   |
| 0.0743        | 4.0   | 900   | 0.4615          | 0.88     |
| 0.0231        | 5.0   | 1125  | 0.6512          | 0.865    |
| 0.0353        | 6.0   | 1350  | 0.6888          | 0.8783   |
| 0.0422        | 7.0   | 1575  | 0.8522          | 0.8717   |
| 0.0395        | 8.0   | 1800  | 0.9258          | 0.865    |
| 0.0246        | 9.0   | 2025  | 0.8665          | 0.8717   |
| 0.0065        | 10.0  | 2250  | 0.9925          | 0.8683   |
| 0.0002        | 11.0  | 2475  | 0.9127          | 0.875    |
| 0.0053        | 12.0  | 2700  | 0.9170          | 0.88     |
| 0.0078        | 13.0  | 2925  | 1.0774          | 0.8683   |
| 0.0094        | 14.0  | 3150  | 1.0384          | 0.8717   |
| 0.0815        | 15.0  | 3375  | 0.9472          | 0.8767   |
| 0.0           | 16.0  | 3600  | 1.0185          | 0.87     |
| 0.0           | 17.0  | 3825  | 1.1297          | 0.8733   |
| 0.0           | 18.0  | 4050  | 1.0917          | 0.865    |
| 0.0           | 19.0  | 4275  | 1.2078          | 0.865    |
| 0.0           | 20.0  | 4500  | 1.1817          | 0.8717   |
| 0.0328        | 21.0  | 4725  | 1.2494          | 0.8667   |
| 0.0221        | 22.0  | 4950  | 1.1544          | 0.87     |
| 0.0           | 23.0  | 5175  | 1.1616          | 0.87     |
| 0.0           | 24.0  | 5400  | 1.0994          | 0.875    |
| 0.0001        | 25.0  | 5625  | 1.1448          | 0.8767   |
| 0.0           | 26.0  | 5850  | 1.1095          | 0.88     |
| 0.0           | 27.0  | 6075  | 1.1096          | 0.88     |
| 0.0           | 28.0  | 6300  | 1.1094          | 0.8783   |
| 0.0           | 29.0  | 6525  | 1.1571          | 0.875    |
| 0.0           | 30.0  | 6750  | 1.1384          | 0.8833   |
| 0.0           | 31.0  | 6975  | 1.1678          | 0.875    |
| 0.0           | 32.0  | 7200  | 1.1864          | 0.8733   |
| 0.0006        | 33.0  | 7425  | 1.1549          | 0.8817   |
| 0.0           | 34.0  | 7650  | 1.1715          | 0.8833   |
| 0.0           | 35.0  | 7875  | 1.2200          | 0.8817   |
| 0.0           | 36.0  | 8100  | 1.1957          | 0.8817   |
| 0.0           | 37.0  | 8325  | 1.1943          | 0.8833   |
| 0.0           | 38.0  | 8550  | 1.1984          | 0.88     |
| 0.0           | 39.0  | 8775  | 1.2416          | 0.8817   |
| 0.0           | 40.0  | 9000  | 1.2319          | 0.8783   |
| 0.0           | 41.0  | 9225  | 1.2497          | 0.8783   |
| 0.0028        | 42.0  | 9450  | 1.2526          | 0.8783   |
| 0.0           | 43.0  | 9675  | 1.2621          | 0.8767   |
| 0.0026        | 44.0  | 9900  | 1.2618          | 0.875    |
| 0.0           | 45.0  | 10125 | 1.2702          | 0.8767   |
| 0.0           | 46.0  | 10350 | 1.2698          | 0.88     |
| 0.0           | 47.0  | 10575 | 1.2756          | 0.88     |
| 0.0           | 48.0  | 10800 | 1.2780          | 0.8783   |
| 0.0           | 49.0  | 11025 | 1.2789          | 0.8783   |
| 0.0           | 50.0  | 11250 | 1.2799          | 0.8783   |


### 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