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# smids_3x_deit_tiny_rms_00001_fold3

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: 0.9705
- Accuracy: 0.9033

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3413        | 1.0   | 225   | 0.2808          | 0.8933   |
| 0.2287        | 2.0   | 450   | 0.2742          | 0.9      |
| 0.1591        | 3.0   | 675   | 0.3006          | 0.89     |
| 0.0862        | 4.0   | 900   | 0.3381          | 0.8883   |
| 0.087         | 5.0   | 1125  | 0.4066          | 0.8783   |
| 0.0686        | 6.0   | 1350  | 0.4702          | 0.885    |
| 0.0747        | 7.0   | 1575  | 0.5496          | 0.8933   |
| 0.0256        | 8.0   | 1800  | 0.6539          | 0.885    |
| 0.0458        | 9.0   | 2025  | 0.6793          | 0.8883   |
| 0.0397        | 10.0  | 2250  | 0.7901          | 0.88     |
| 0.0252        | 11.0  | 2475  | 0.7599          | 0.8967   |
| 0.0212        | 12.0  | 2700  | 0.8640          | 0.89     |
| 0.033         | 13.0  | 2925  | 0.8965          | 0.88     |
| 0.0005        | 14.0  | 3150  | 0.8332          | 0.895    |
| 0.0068        | 15.0  | 3375  | 0.9483          | 0.8717   |
| 0.0008        | 16.0  | 3600  | 1.0157          | 0.8783   |
| 0.0016        | 17.0  | 3825  | 0.8948          | 0.8867   |
| 0.0002        | 18.0  | 4050  | 0.8418          | 0.895    |
| 0.0           | 19.0  | 4275  | 0.8994          | 0.8983   |
| 0.0044        | 20.0  | 4500  | 0.8798          | 0.9083   |
| 0.0           | 21.0  | 4725  | 1.0795          | 0.8717   |
| 0.0009        | 22.0  | 4950  | 1.0744          | 0.885    |
| 0.0066        | 23.0  | 5175  | 0.9462          | 0.8883   |
| 0.0           | 24.0  | 5400  | 0.8715          | 0.9017   |
| 0.0088        | 25.0  | 5625  | 0.9553          | 0.8983   |
| 0.0           | 26.0  | 5850  | 0.9300          | 0.9      |
| 0.0271        | 27.0  | 6075  | 0.9362          | 0.8967   |
| 0.0           | 28.0  | 6300  | 0.9453          | 0.8967   |
| 0.0056        | 29.0  | 6525  | 1.0241          | 0.8917   |
| 0.0134        | 30.0  | 6750  | 0.9852          | 0.9017   |
| 0.0062        | 31.0  | 6975  | 0.9946          | 0.9017   |
| 0.0           | 32.0  | 7200  | 1.0433          | 0.89     |
| 0.0           | 33.0  | 7425  | 0.9693          | 0.8983   |
| 0.0           | 34.0  | 7650  | 0.9977          | 0.8983   |
| 0.0           | 35.0  | 7875  | 0.9577          | 0.9033   |
| 0.0           | 36.0  | 8100  | 0.9692          | 0.8967   |
| 0.0           | 37.0  | 8325  | 0.9675          | 0.8967   |
| 0.0           | 38.0  | 8550  | 0.9775          | 0.9      |
| 0.0           | 39.0  | 8775  | 0.9600          | 0.9      |
| 0.0           | 40.0  | 9000  | 0.9503          | 0.8983   |
| 0.0           | 41.0  | 9225  | 0.9634          | 0.895    |
| 0.0           | 42.0  | 9450  | 0.9624          | 0.9      |
| 0.0           | 43.0  | 9675  | 0.9737          | 0.9      |
| 0.0           | 44.0  | 9900  | 0.9743          | 0.9017   |
| 0.0           | 45.0  | 10125 | 0.9706          | 0.9      |
| 0.0           | 46.0  | 10350 | 0.9706          | 0.9      |
| 0.0019        | 47.0  | 10575 | 0.9708          | 0.9017   |
| 0.0           | 48.0  | 10800 | 0.9707          | 0.9017   |
| 0.0           | 49.0  | 11025 | 0.9710          | 0.9017   |
| 0.0           | 50.0  | 11250 | 0.9705          | 0.9033   |


### 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