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# smids_3x_beit_base_rms_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: 1.4390
- Accuracy: 0.8497

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8068        | 1.0   | 226   | 1.0605          | 0.3272   |
| 0.7543        | 2.0   | 452   | 0.8522          | 0.5943   |
| 0.6302        | 3.0   | 678   | 0.8188          | 0.6227   |
| 0.591         | 4.0   | 904   | 0.7284          | 0.6795   |
| 0.5365        | 5.0   | 1130  | 0.5240          | 0.7830   |
| 0.4875        | 6.0   | 1356  | 0.4715          | 0.8030   |
| 0.2956        | 7.0   | 1582  | 0.5230          | 0.8130   |
| 0.3385        | 8.0   | 1808  | 0.4637          | 0.8047   |
| 0.2498        | 9.0   | 2034  | 0.5733          | 0.8230   |
| 0.2375        | 10.0  | 2260  | 0.5001          | 0.8381   |
| 0.2383        | 11.0  | 2486  | 0.5213          | 0.8164   |
| 0.1638        | 12.0  | 2712  | 0.7500          | 0.8097   |
| 0.1669        | 13.0  | 2938  | 0.6347          | 0.8347   |
| 0.091         | 14.0  | 3164  | 0.8704          | 0.8164   |
| 0.0933        | 15.0  | 3390  | 0.6698          | 0.8280   |
| 0.1167        | 16.0  | 3616  | 0.7435          | 0.8481   |
| 0.0442        | 17.0  | 3842  | 0.8758          | 0.8164   |
| 0.0649        | 18.0  | 4068  | 0.8054          | 0.8247   |
| 0.0996        | 19.0  | 4294  | 0.8135          | 0.8164   |
| 0.0421        | 20.0  | 4520  | 0.8460          | 0.8464   |
| 0.0255        | 21.0  | 4746  | 1.2147          | 0.8097   |
| 0.0814        | 22.0  | 4972  | 0.8708          | 0.8331   |
| 0.07          | 23.0  | 5198  | 1.0564          | 0.8364   |
| 0.029         | 24.0  | 5424  | 1.0607          | 0.8364   |
| 0.0335        | 25.0  | 5650  | 1.0179          | 0.8464   |
| 0.0974        | 26.0  | 5876  | 0.8966          | 0.8364   |
| 0.0251        | 27.0  | 6102  | 1.0900          | 0.8297   |
| 0.0304        | 28.0  | 6328  | 0.9348          | 0.8347   |
| 0.0116        | 29.0  | 6554  | 1.0392          | 0.8447   |
| 0.036         | 30.0  | 6780  | 1.0080          | 0.8414   |
| 0.0176        | 31.0  | 7006  | 1.0131          | 0.8364   |
| 0.0187        | 32.0  | 7232  | 0.9626          | 0.8397   |
| 0.0495        | 33.0  | 7458  | 0.9911          | 0.8414   |
| 0.0106        | 34.0  | 7684  | 1.2195          | 0.8331   |
| 0.0005        | 35.0  | 7910  | 1.2232          | 0.8464   |
| 0.0148        | 36.0  | 8136  | 1.1060          | 0.8364   |
| 0.0093        | 37.0  | 8362  | 1.0552          | 0.8364   |
| 0.0212        | 38.0  | 8588  | 1.1910          | 0.8364   |
| 0.0009        | 39.0  | 8814  | 1.1001          | 0.8431   |
| 0.0083        | 40.0  | 9040  | 1.2874          | 0.8481   |
| 0.0296        | 41.0  | 9266  | 1.3495          | 0.8381   |
| 0.0225        | 42.0  | 9492  | 1.3683          | 0.8414   |
| 0.0158        | 43.0  | 9718  | 1.2852          | 0.8481   |
| 0.0056        | 44.0  | 9944  | 1.3620          | 0.8447   |
| 0.0126        | 45.0  | 10170 | 1.3137          | 0.8431   |
| 0.0           | 46.0  | 10396 | 1.4527          | 0.8497   |
| 0.013         | 47.0  | 10622 | 1.4028          | 0.8531   |
| 0.0375        | 48.0  | 10848 | 1.3979          | 0.8481   |
| 0.0006        | 49.0  | 11074 | 1.4369          | 0.8497   |
| 0.0135        | 50.0  | 11300 | 1.4390          | 0.8497   |


### 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