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# smids_5x_beit_base_rms_00001_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: 0.9550
- Accuracy: 0.91

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1871        | 1.0   | 375   | 0.3400          | 0.865    |
| 0.1042        | 2.0   | 750   | 0.2834          | 0.9017   |
| 0.1083        | 3.0   | 1125  | 0.3546          | 0.9033   |
| 0.0201        | 4.0   | 1500  | 0.3961          | 0.9133   |
| 0.0166        | 5.0   | 1875  | 0.6199          | 0.9083   |
| 0.0246        | 6.0   | 2250  | 0.6057          | 0.8967   |
| 0.0224        | 7.0   | 2625  | 0.7547          | 0.9117   |
| 0.0003        | 8.0   | 3000  | 0.7052          | 0.9133   |
| 0.0111        | 9.0   | 3375  | 0.7830          | 0.8983   |
| 0.0518        | 10.0  | 3750  | 0.8521          | 0.8967   |
| 0.02          | 11.0  | 4125  | 0.9299          | 0.8933   |
| 0.0138        | 12.0  | 4500  | 0.9525          | 0.8983   |
| 0.0001        | 13.0  | 4875  | 0.8824          | 0.9067   |
| 0.013         | 14.0  | 5250  | 0.9828          | 0.8833   |
| 0.0349        | 15.0  | 5625  | 0.8057          | 0.9033   |
| 0.0008        | 16.0  | 6000  | 0.9444          | 0.8983   |
| 0.0016        | 17.0  | 6375  | 0.8486          | 0.905    |
| 0.0093        | 18.0  | 6750  | 0.8485          | 0.9083   |
| 0.0057        | 19.0  | 7125  | 0.8351          | 0.895    |
| 0.0146        | 20.0  | 7500  | 0.8068          | 0.915    |
| 0.0088        | 21.0  | 7875  | 0.8372          | 0.9117   |
| 0.0074        | 22.0  | 8250  | 0.8780          | 0.905    |
| 0.0068        | 23.0  | 8625  | 0.9227          | 0.9067   |
| 0.0           | 24.0  | 9000  | 0.8408          | 0.9067   |
| 0.0           | 25.0  | 9375  | 0.8878          | 0.9067   |
| 0.0001        | 26.0  | 9750  | 0.6996          | 0.9217   |
| 0.0043        | 27.0  | 10125 | 0.7960          | 0.915    |
| 0.0021        | 28.0  | 10500 | 0.8288          | 0.91     |
| 0.002         | 29.0  | 10875 | 0.8059          | 0.9133   |
| 0.0055        | 30.0  | 11250 | 0.8992          | 0.9117   |
| 0.0001        | 31.0  | 11625 | 0.9502          | 0.9083   |
| 0.0001        | 32.0  | 12000 | 1.0009          | 0.9067   |
| 0.0047        | 33.0  | 12375 | 0.9429          | 0.91     |
| 0.0           | 34.0  | 12750 | 0.9564          | 0.905    |
| 0.0           | 35.0  | 13125 | 0.9119          | 0.9217   |
| 0.0           | 36.0  | 13500 | 1.0028          | 0.9033   |
| 0.0           | 37.0  | 13875 | 0.9150          | 0.91     |
| 0.0054        | 38.0  | 14250 | 0.9393          | 0.91     |
| 0.0           | 39.0  | 14625 | 1.0004          | 0.9067   |
| 0.0           | 40.0  | 15000 | 0.9733          | 0.9083   |
| 0.0001        | 41.0  | 15375 | 0.9774          | 0.9067   |
| 0.0           | 42.0  | 15750 | 0.9404          | 0.9133   |
| 0.0           | 43.0  | 16125 | 0.9724          | 0.9117   |
| 0.0           | 44.0  | 16500 | 0.9389          | 0.915    |
| 0.0           | 45.0  | 16875 | 0.9342          | 0.9167   |
| 0.0           | 46.0  | 17250 | 0.9815          | 0.9117   |
| 0.0058        | 47.0  | 17625 | 0.9724          | 0.9067   |
| 0.0           | 48.0  | 18000 | 0.9650          | 0.9067   |
| 0.0           | 49.0  | 18375 | 0.9572          | 0.9083   |
| 0.0012        | 50.0  | 18750 | 0.9550          | 0.91     |


### 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