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# smids_3x_beit_base_rms_0001_fold5

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: 1.9458
- Accuracy: 0.8133

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8091        | 1.0   | 225   | 0.7888          | 0.5967   |
| 0.6825        | 2.0   | 450   | 0.6663          | 0.7      |
| 0.6331        | 3.0   | 675   | 0.6179          | 0.7233   |
| 0.545         | 4.0   | 900   | 0.5461          | 0.7667   |
| 0.4511        | 5.0   | 1125  | 0.5082          | 0.785    |
| 0.5168        | 6.0   | 1350  | 0.4960          | 0.7867   |
| 0.498         | 7.0   | 1575  | 0.4691          | 0.8133   |
| 0.4445        | 8.0   | 1800  | 0.5042          | 0.7883   |
| 0.3476        | 9.0   | 2025  | 0.5424          | 0.7883   |
| 0.3599        | 10.0  | 2250  | 0.4586          | 0.8183   |
| 0.2707        | 11.0  | 2475  | 0.5539          | 0.8133   |
| 0.3169        | 12.0  | 2700  | 0.5413          | 0.805    |
| 0.2566        | 13.0  | 2925  | 0.6318          | 0.7933   |
| 0.1731        | 14.0  | 3150  | 0.6551          | 0.805    |
| 0.1033        | 15.0  | 3375  | 0.7315          | 0.7967   |
| 0.1418        | 16.0  | 3600  | 0.6489          | 0.8217   |
| 0.1165        | 17.0  | 3825  | 0.8690          | 0.815    |
| 0.1219        | 18.0  | 4050  | 0.8038          | 0.7883   |
| 0.1084        | 19.0  | 4275  | 0.9593          | 0.795    |
| 0.0893        | 20.0  | 4500  | 0.9469          | 0.8      |
| 0.0691        | 21.0  | 4725  | 1.0155          | 0.8183   |
| 0.155         | 22.0  | 4950  | 1.0808          | 0.8033   |
| 0.068         | 23.0  | 5175  | 1.2932          | 0.8067   |
| 0.0871        | 24.0  | 5400  | 1.0549          | 0.7933   |
| 0.0614        | 25.0  | 5625  | 1.2073          | 0.8183   |
| 0.0426        | 26.0  | 5850  | 1.1147          | 0.8167   |
| 0.0501        | 27.0  | 6075  | 1.1794          | 0.8067   |
| 0.0496        | 28.0  | 6300  | 1.2384          | 0.8033   |
| 0.0951        | 29.0  | 6525  | 1.1319          | 0.795    |
| 0.0433        | 30.0  | 6750  | 1.1451          | 0.8217   |
| 0.0293        | 31.0  | 6975  | 1.3635          | 0.815    |
| 0.0095        | 32.0  | 7200  | 1.4313          | 0.8067   |
| 0.0383        | 33.0  | 7425  | 1.2822          | 0.8217   |
| 0.0173        | 34.0  | 7650  | 1.4012          | 0.8217   |
| 0.0213        | 35.0  | 7875  | 1.5178          | 0.8117   |
| 0.0022        | 36.0  | 8100  | 1.6408          | 0.8283   |
| 0.0311        | 37.0  | 8325  | 1.7017          | 0.8117   |
| 0.0285        | 38.0  | 8550  | 1.5903          | 0.8183   |
| 0.0027        | 39.0  | 8775  | 1.6460          | 0.815    |
| 0.0036        | 40.0  | 9000  | 1.6806          | 0.7983   |
| 0.0136        | 41.0  | 9225  | 1.5975          | 0.8067   |
| 0.0063        | 42.0  | 9450  | 1.8013          | 0.8217   |
| 0.0155        | 43.0  | 9675  | 1.6944          | 0.8083   |
| 0.0003        | 44.0  | 9900  | 1.5360          | 0.8283   |
| 0.0001        | 45.0  | 10125 | 1.7589          | 0.8283   |
| 0.0203        | 46.0  | 10350 | 1.7247          | 0.8267   |
| 0.0004        | 47.0  | 10575 | 1.7343          | 0.8217   |
| 0.0069        | 48.0  | 10800 | 1.8685          | 0.8183   |
| 0.0001        | 49.0  | 11025 | 1.9172          | 0.8133   |
| 0.0           | 50.0  | 11250 | 1.9458          | 0.8133   |


### 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