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# smids_5x_beit_base_sgd_00001_fold3

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: 1.1248
- Accuracy: 0.3967

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2551        | 1.0   | 375   | 1.3211          | 0.3167   |
| 1.2561        | 2.0   | 750   | 1.3119          | 0.32     |
| 1.2134        | 3.0   | 1125  | 1.3028          | 0.325    |
| 1.226         | 4.0   | 1500  | 1.2942          | 0.325    |
| 1.1635        | 5.0   | 1875  | 1.2859          | 0.3267   |
| 1.2304        | 6.0   | 2250  | 1.2778          | 0.3333   |
| 1.1734        | 7.0   | 2625  | 1.2702          | 0.3383   |
| 1.1724        | 8.0   | 3000  | 1.2625          | 0.3417   |
| 1.1336        | 9.0   | 3375  | 1.2554          | 0.3467   |
| 1.1266        | 10.0  | 3750  | 1.2486          | 0.3517   |
| 1.1276        | 11.0  | 4125  | 1.2419          | 0.355    |
| 1.1538        | 12.0  | 4500  | 1.2355          | 0.355    |
| 1.1425        | 13.0  | 4875  | 1.2292          | 0.3567   |
| 1.1463        | 14.0  | 5250  | 1.2233          | 0.36     |
| 1.1661        | 15.0  | 5625  | 1.2174          | 0.3633   |
| 1.1118        | 16.0  | 6000  | 1.2118          | 0.365    |
| 1.123         | 17.0  | 6375  | 1.2063          | 0.3667   |
| 1.1065        | 18.0  | 6750  | 1.2010          | 0.3667   |
| 1.1074        | 19.0  | 7125  | 1.1959          | 0.365    |
| 1.0742        | 20.0  | 7500  | 1.1911          | 0.3717   |
| 1.0616        | 21.0  | 7875  | 1.1865          | 0.3717   |
| 1.0745        | 22.0  | 8250  | 1.1820          | 0.3717   |
| 1.0871        | 23.0  | 8625  | 1.1777          | 0.3717   |
| 1.031         | 24.0  | 9000  | 1.1737          | 0.3717   |
| 1.0843        | 25.0  | 9375  | 1.1697          | 0.375    |
| 1.0616        | 26.0  | 9750  | 1.1660          | 0.3767   |
| 1.0414        | 27.0  | 10125 | 1.1624          | 0.3783   |
| 1.0303        | 28.0  | 10500 | 1.1590          | 0.3783   |
| 0.9887        | 29.0  | 10875 | 1.1558          | 0.38     |
| 1.0267        | 30.0  | 11250 | 1.1528          | 0.38     |
| 1.0792        | 31.0  | 11625 | 1.1499          | 0.3833   |
| 1.0736        | 32.0  | 12000 | 1.1472          | 0.3883   |
| 1.0868        | 33.0  | 12375 | 1.1446          | 0.39     |
| 1.0257        | 34.0  | 12750 | 1.1422          | 0.3883   |
| 1.0237        | 35.0  | 13125 | 1.1400          | 0.39     |
| 1.0201        | 36.0  | 13500 | 1.1379          | 0.39     |
| 1.0769        | 37.0  | 13875 | 1.1360          | 0.3917   |
| 1.032         | 38.0  | 14250 | 1.1343          | 0.3933   |
| 1.0317        | 39.0  | 14625 | 1.1327          | 0.395    |
| 1.0402        | 40.0  | 15000 | 1.1312          | 0.395    |
| 0.957         | 41.0  | 15375 | 1.1300          | 0.395    |
| 1.0445        | 42.0  | 15750 | 1.1288          | 0.395    |
| 1.0399        | 43.0  | 16125 | 1.1278          | 0.395    |
| 1.0323        | 44.0  | 16500 | 1.1270          | 0.3967   |
| 1.0444        | 45.0  | 16875 | 1.1263          | 0.3967   |
| 0.9983        | 46.0  | 17250 | 1.1257          | 0.3967   |
| 1.042         | 47.0  | 17625 | 1.1253          | 0.3967   |
| 1.0685        | 48.0  | 18000 | 1.1250          | 0.3967   |
| 1.0486        | 49.0  | 18375 | 1.1249          | 0.3967   |
| 1.0457        | 50.0  | 18750 | 1.1248          | 0.3967   |


### 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