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# smids_3x_beit_base_rms_001_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: 0.7807
- Accuracy: 0.7579

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1042        | 1.0   | 226   | 1.0981          | 0.3456   |
| 0.942         | 2.0   | 452   | 0.9022          | 0.5275   |
| 0.8328        | 3.0   | 678   | 0.9600          | 0.4691   |
| 0.8702        | 4.0   | 904   | 0.9083          | 0.5543   |
| 0.8313        | 5.0   | 1130  | 0.8171          | 0.5760   |
| 0.8558        | 6.0   | 1356  | 0.8467          | 0.5342   |
| 0.7514        | 7.0   | 1582  | 0.7612          | 0.6277   |
| 0.7839        | 8.0   | 1808  | 0.7968          | 0.5659   |
| 0.7602        | 9.0   | 2034  | 0.7655          | 0.6210   |
| 0.7694        | 10.0  | 2260  | 0.7429          | 0.6060   |
| 0.7166        | 11.0  | 2486  | 0.7968          | 0.5626   |
| 0.7048        | 12.0  | 2712  | 0.8272          | 0.6077   |
| 0.6745        | 13.0  | 2938  | 0.8054          | 0.5993   |
| 0.7185        | 14.0  | 3164  | 0.7867          | 0.6194   |
| 0.7264        | 15.0  | 3390  | 0.7701          | 0.6377   |
| 0.6767        | 16.0  | 3616  | 0.7383          | 0.6144   |
| 0.6006        | 17.0  | 3842  | 0.8677          | 0.6077   |
| 0.6721        | 18.0  | 4068  | 0.7460          | 0.6361   |
| 0.6352        | 19.0  | 4294  | 0.7492          | 0.6127   |
| 0.642         | 20.0  | 4520  | 0.7712          | 0.6160   |
| 0.6647        | 21.0  | 4746  | 0.7257          | 0.6544   |
| 0.6408        | 22.0  | 4972  | 0.7629          | 0.6611   |
| 0.7655        | 23.0  | 5198  | 0.7723          | 0.6127   |
| 0.7074        | 24.0  | 5424  | 0.6879          | 0.6928   |
| 0.6919        | 25.0  | 5650  | 0.6962          | 0.6828   |
| 0.698         | 26.0  | 5876  | 0.7479          | 0.6361   |
| 0.641         | 27.0  | 6102  | 0.7653          | 0.6644   |
| 0.6417        | 28.0  | 6328  | 0.7791          | 0.6594   |
| 0.6123        | 29.0  | 6554  | 0.7195          | 0.6761   |
| 0.5918        | 30.0  | 6780  | 0.6991          | 0.6995   |
| 0.5562        | 31.0  | 7006  | 0.6938          | 0.6978   |
| 0.6293        | 32.0  | 7232  | 0.6564          | 0.7145   |
| 0.5615        | 33.0  | 7458  | 0.7421          | 0.6878   |
| 0.5411        | 34.0  | 7684  | 0.6688          | 0.7145   |
| 0.4483        | 35.0  | 7910  | 0.7701          | 0.6962   |
| 0.4776        | 36.0  | 8136  | 0.6349          | 0.7412   |
| 0.4775        | 37.0  | 8362  | 0.6430          | 0.7262   |
| 0.4854        | 38.0  | 8588  | 0.7095          | 0.7078   |
| 0.4208        | 39.0  | 8814  | 0.6254          | 0.7412   |
| 0.3846        | 40.0  | 9040  | 0.6645          | 0.7396   |
| 0.3663        | 41.0  | 9266  | 0.6430          | 0.7563   |
| 0.3616        | 42.0  | 9492  | 0.6767          | 0.7462   |
| 0.3863        | 43.0  | 9718  | 0.6432          | 0.7596   |
| 0.2608        | 44.0  | 9944  | 0.6472          | 0.7563   |
| 0.4159        | 45.0  | 10170 | 0.6400          | 0.7663   |
| 0.333         | 46.0  | 10396 | 0.6911          | 0.7613   |
| 0.2718        | 47.0  | 10622 | 0.7119          | 0.7696   |
| 0.2639        | 48.0  | 10848 | 0.7421          | 0.7596   |
| 0.236         | 49.0  | 11074 | 0.7543          | 0.7613   |
| 0.2672        | 50.0  | 11300 | 0.7807          | 0.7579   |


### 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