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# smids_5x_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: 0.9537
- Accuracy: 0.905

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2762        | 1.0   | 375   | 0.2896          | 0.895    |
| 0.1644        | 2.0   | 750   | 0.3438          | 0.8783   |
| 0.0979        | 3.0   | 1125  | 0.3168          | 0.89     |
| 0.0564        | 4.0   | 1500  | 0.3257          | 0.9033   |
| 0.0779        | 5.0   | 1875  | 0.3885          | 0.8983   |
| 0.0908        | 6.0   | 2250  | 0.5137          | 0.8917   |
| 0.0272        | 7.0   | 2625  | 0.6097          | 0.9      |
| 0.0302        | 8.0   | 3000  | 0.6164          | 0.905    |
| 0.0116        | 9.0   | 3375  | 0.6445          | 0.905    |
| 0.03          | 10.0  | 3750  | 0.8252          | 0.8983   |
| 0.0134        | 11.0  | 4125  | 0.8460          | 0.8933   |
| 0.0063        | 12.0  | 4500  | 0.8830          | 0.8933   |
| 0.0001        | 13.0  | 4875  | 0.7641          | 0.8967   |
| 0.0003        | 14.0  | 5250  | 0.6936          | 0.9067   |
| 0.0001        | 15.0  | 5625  | 0.8158          | 0.8933   |
| 0.0147        | 16.0  | 6000  | 0.8005          | 0.9      |
| 0.0           | 17.0  | 6375  | 0.8369          | 0.9083   |
| 0.0           | 18.0  | 6750  | 0.7801          | 0.9117   |
| 0.0003        | 19.0  | 7125  | 0.8314          | 0.9      |
| 0.0272        | 20.0  | 7500  | 0.7717          | 0.9067   |
| 0.0197        | 21.0  | 7875  | 0.7616          | 0.91     |
| 0.0022        | 22.0  | 8250  | 0.8297          | 0.9017   |
| 0.0082        | 23.0  | 8625  | 0.9830          | 0.8967   |
| 0.0           | 24.0  | 9000  | 0.8304          | 0.9033   |
| 0.0           | 25.0  | 9375  | 0.9362          | 0.8967   |
| 0.0           | 26.0  | 9750  | 1.0186          | 0.885    |
| 0.0           | 27.0  | 10125 | 0.9660          | 0.895    |
| 0.0           | 28.0  | 10500 | 0.8565          | 0.9067   |
| 0.0           | 29.0  | 10875 | 1.0314          | 0.9      |
| 0.004         | 30.0  | 11250 | 1.0325          | 0.8917   |
| 0.0           | 31.0  | 11625 | 0.9803          | 0.905    |
| 0.0           | 32.0  | 12000 | 0.9230          | 0.8967   |
| 0.0           | 33.0  | 12375 | 0.9577          | 0.8983   |
| 0.0055        | 34.0  | 12750 | 1.0448          | 0.89     |
| 0.0           | 35.0  | 13125 | 0.9488          | 0.9017   |
| 0.0           | 36.0  | 13500 | 0.9198          | 0.9083   |
| 0.0           | 37.0  | 13875 | 0.9232          | 0.905    |
| 0.0           | 38.0  | 14250 | 1.0528          | 0.89     |
| 0.0           | 39.0  | 14625 | 0.9516          | 0.8983   |
| 0.0039        | 40.0  | 15000 | 0.9539          | 0.8983   |
| 0.0           | 41.0  | 15375 | 0.9633          | 0.8983   |
| 0.0           | 42.0  | 15750 | 0.9250          | 0.9033   |
| 0.0           | 43.0  | 16125 | 0.9440          | 0.9017   |
| 0.0           | 44.0  | 16500 | 0.9475          | 0.905    |
| 0.0           | 45.0  | 16875 | 0.9408          | 0.905    |
| 0.0           | 46.0  | 17250 | 0.9488          | 0.905    |
| 0.0           | 47.0  | 17625 | 0.9474          | 0.905    |
| 0.0           | 48.0  | 18000 | 0.9524          | 0.905    |
| 0.0           | 49.0  | 18375 | 0.9540          | 0.9033   |
| 0.0           | 50.0  | 18750 | 0.9537          | 0.905    |


### 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