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# smids_5x_deit_base_rms_0001_fold5

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9290
- Accuracy: 0.915

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2223        | 1.0   | 375   | 0.3109          | 0.8933   |
| 0.1256        | 2.0   | 750   | 0.3635          | 0.895    |
| 0.0772        | 3.0   | 1125  | 0.3333          | 0.905    |
| 0.0469        | 4.0   | 1500  | 0.4780          | 0.8867   |
| 0.0893        | 5.0   | 1875  | 0.6491          | 0.8683   |
| 0.0141        | 6.0   | 2250  | 0.5373          | 0.8983   |
| 0.0431        | 7.0   | 2625  | 0.4446          | 0.8933   |
| 0.0189        | 8.0   | 3000  | 0.5065          | 0.9067   |
| 0.0211        | 9.0   | 3375  | 0.4802          | 0.9133   |
| 0.0346        | 10.0  | 3750  | 0.5071          | 0.9033   |
| 0.0253        | 11.0  | 4125  | 0.6744          | 0.8983   |
| 0.0144        | 12.0  | 4500  | 0.6389          | 0.89     |
| 0.0096        | 13.0  | 4875  | 0.6130          | 0.905    |
| 0.0187        | 14.0  | 5250  | 0.6069          | 0.9083   |
| 0.103         | 15.0  | 5625  | 0.7299          | 0.8867   |
| 0.0003        | 16.0  | 6000  | 0.6047          | 0.9033   |
| 0.0078        | 17.0  | 6375  | 0.6867          | 0.8917   |
| 0.0106        | 18.0  | 6750  | 0.6759          | 0.8917   |
| 0.0326        | 19.0  | 7125  | 0.9578          | 0.8633   |
| 0.0231        | 20.0  | 7500  | 0.7260          | 0.8967   |
| 0.0327        | 21.0  | 7875  | 0.6167          | 0.9      |
| 0.0188        | 22.0  | 8250  | 0.6583          | 0.905    |
| 0.0002        | 23.0  | 8625  | 0.6038          | 0.9083   |
| 0.0276        | 24.0  | 9000  | 0.8452          | 0.8983   |
| 0.0002        | 25.0  | 9375  | 0.6542          | 0.9067   |
| 0.0045        | 26.0  | 9750  | 0.7924          | 0.905    |
| 0.0011        | 27.0  | 10125 | 0.9081          | 0.9017   |
| 0.0001        | 28.0  | 10500 | 0.6753          | 0.91     |
| 0.0001        | 29.0  | 10875 | 0.8443          | 0.8933   |
| 0.0026        | 30.0  | 11250 | 0.8554          | 0.9      |
| 0.0029        | 31.0  | 11625 | 1.0060          | 0.8983   |
| 0.0001        | 32.0  | 12000 | 0.7158          | 0.905    |
| 0.0113        | 33.0  | 12375 | 0.8641          | 0.9017   |
| 0.0           | 34.0  | 12750 | 0.7747          | 0.9067   |
| 0.0           | 35.0  | 13125 | 0.7089          | 0.925    |
| 0.0           | 36.0  | 13500 | 0.7594          | 0.9133   |
| 0.0184        | 37.0  | 13875 | 0.7578          | 0.915    |
| 0.0038        | 38.0  | 14250 | 0.7826          | 0.915    |
| 0.0           | 39.0  | 14625 | 0.7931          | 0.9183   |
| 0.0           | 40.0  | 15000 | 0.8433          | 0.91     |
| 0.0           | 41.0  | 15375 | 0.8317          | 0.9133   |
| 0.0           | 42.0  | 15750 | 0.8659          | 0.9133   |
| 0.0           | 43.0  | 16125 | 0.8765          | 0.9117   |
| 0.0           | 44.0  | 16500 | 0.9009          | 0.91     |
| 0.0           | 45.0  | 16875 | 0.9256          | 0.9133   |
| 0.0           | 46.0  | 17250 | 0.9323          | 0.915    |
| 0.003         | 47.0  | 17625 | 0.9213          | 0.915    |
| 0.0           | 48.0  | 18000 | 0.9276          | 0.915    |
| 0.0           | 49.0  | 18375 | 0.9299          | 0.915    |
| 0.0021        | 50.0  | 18750 | 0.9290          | 0.915    |


### 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