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# smids_3x_beit_base_sgd_0001_fold1

This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6006
- Accuracy: 0.7513

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2428        | 1.0   | 226   | 1.2900          | 0.3139   |
| 1.1165        | 2.0   | 452   | 1.2280          | 0.3389   |
| 1.085         | 3.0   | 678   | 1.1717          | 0.3606   |
| 1.0873        | 4.0   | 904   | 1.1174          | 0.3973   |
| 1.0209        | 5.0   | 1130  | 1.0648          | 0.4207   |
| 0.9387        | 6.0   | 1356  | 1.0163          | 0.4741   |
| 0.9347        | 7.0   | 1582  | 0.9719          | 0.5175   |
| 0.8727        | 8.0   | 1808  | 0.9312          | 0.5626   |
| 0.8169        | 9.0   | 2034  | 0.8951          | 0.5993   |
| 0.861         | 10.0  | 2260  | 0.8623          | 0.6160   |
| 0.8138        | 11.0  | 2486  | 0.8344          | 0.6327   |
| 0.7635        | 12.0  | 2712  | 0.8096          | 0.6444   |
| 0.7469        | 13.0  | 2938  | 0.7879          | 0.6477   |
| 0.7457        | 14.0  | 3164  | 0.7697          | 0.6561   |
| 0.6958        | 15.0  | 3390  | 0.7527          | 0.6728   |
| 0.6961        | 16.0  | 3616  | 0.7374          | 0.6795   |
| 0.6436        | 17.0  | 3842  | 0.7245          | 0.6878   |
| 0.6513        | 18.0  | 4068  | 0.7127          | 0.6912   |
| 0.6672        | 19.0  | 4294  | 0.7016          | 0.6962   |
| 0.6558        | 20.0  | 4520  | 0.6918          | 0.7012   |
| 0.6466        | 21.0  | 4746  | 0.6834          | 0.7028   |
| 0.6561        | 22.0  | 4972  | 0.6751          | 0.7045   |
| 0.6208        | 23.0  | 5198  | 0.6670          | 0.7145   |
| 0.6499        | 24.0  | 5424  | 0.6602          | 0.7162   |
| 0.6316        | 25.0  | 5650  | 0.6537          | 0.7179   |
| 0.6488        | 26.0  | 5876  | 0.6486          | 0.7245   |
| 0.6013        | 27.0  | 6102  | 0.6431          | 0.7229   |
| 0.6349        | 28.0  | 6328  | 0.6385          | 0.7295   |
| 0.5571        | 29.0  | 6554  | 0.6343          | 0.7312   |
| 0.6883        | 30.0  | 6780  | 0.6303          | 0.7329   |
| 0.5874        | 31.0  | 7006  | 0.6269          | 0.7362   |
| 0.5957        | 32.0  | 7232  | 0.6236          | 0.7412   |
| 0.5454        | 33.0  | 7458  | 0.6209          | 0.7446   |
| 0.5392        | 34.0  | 7684  | 0.6182          | 0.7446   |
| 0.6014        | 35.0  | 7910  | 0.6160          | 0.7462   |
| 0.5394        | 36.0  | 8136  | 0.6140          | 0.7462   |
| 0.5557        | 37.0  | 8362  | 0.6119          | 0.7479   |
| 0.5868        | 38.0  | 8588  | 0.6101          | 0.7479   |
| 0.5673        | 39.0  | 8814  | 0.6084          | 0.7479   |
| 0.5576        | 40.0  | 9040  | 0.6071          | 0.7479   |
| 0.5598        | 41.0  | 9266  | 0.6057          | 0.7479   |
| 0.5493        | 42.0  | 9492  | 0.6045          | 0.7496   |
| 0.573         | 43.0  | 9718  | 0.6035          | 0.7513   |
| 0.5428        | 44.0  | 9944  | 0.6027          | 0.7513   |
| 0.6174        | 45.0  | 10170 | 0.6020          | 0.7513   |
| 0.5654        | 46.0  | 10396 | 0.6015          | 0.7513   |
| 0.5911        | 47.0  | 10622 | 0.6010          | 0.7513   |
| 0.5644        | 48.0  | 10848 | 0.6008          | 0.7513   |
| 0.5284        | 49.0  | 11074 | 0.6007          | 0.7513   |
| 0.5888        | 50.0  | 11300 | 0.6006          | 0.7513   |


### 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