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# smids_5x_beit_base_sgd_001_fold4

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: 0.4156
- Accuracy: 0.8467

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6893        | 1.0   | 375   | 0.6764          | 0.7333   |
| 0.5522        | 2.0   | 750   | 0.5194          | 0.7917   |
| 0.409         | 3.0   | 1125  | 0.4634          | 0.805    |
| 0.4507        | 4.0   | 1500  | 0.4337          | 0.81     |
| 0.416         | 5.0   | 1875  | 0.4157          | 0.8233   |
| 0.3067        | 6.0   | 2250  | 0.4110          | 0.825    |
| 0.3644        | 7.0   | 2625  | 0.3987          | 0.8333   |
| 0.3243        | 8.0   | 3000  | 0.3991          | 0.835    |
| 0.3062        | 9.0   | 3375  | 0.3919          | 0.8383   |
| 0.308         | 10.0  | 3750  | 0.3990          | 0.8383   |
| 0.2735        | 11.0  | 4125  | 0.3981          | 0.835    |
| 0.2384        | 12.0  | 4500  | 0.3880          | 0.8417   |
| 0.2357        | 13.0  | 4875  | 0.3907          | 0.845    |
| 0.3175        | 14.0  | 5250  | 0.3900          | 0.8417   |
| 0.2423        | 15.0  | 5625  | 0.3853          | 0.8517   |
| 0.1987        | 16.0  | 6000  | 0.3848          | 0.8433   |
| 0.2594        | 17.0  | 6375  | 0.3874          | 0.845    |
| 0.2225        | 18.0  | 6750  | 0.3883          | 0.8533   |
| 0.247         | 19.0  | 7125  | 0.3920          | 0.8383   |
| 0.2235        | 20.0  | 7500  | 0.3894          | 0.8433   |
| 0.2203        | 21.0  | 7875  | 0.3971          | 0.8417   |
| 0.2258        | 22.0  | 8250  | 0.3954          | 0.8533   |
| 0.2363        | 23.0  | 8625  | 0.3968          | 0.845    |
| 0.2288        | 24.0  | 9000  | 0.3993          | 0.8467   |
| 0.2646        | 25.0  | 9375  | 0.4039          | 0.84     |
| 0.1839        | 26.0  | 9750  | 0.3987          | 0.8433   |
| 0.2779        | 27.0  | 10125 | 0.4000          | 0.845    |
| 0.1848        | 28.0  | 10500 | 0.4019          | 0.8367   |
| 0.2029        | 29.0  | 10875 | 0.4110          | 0.84     |
| 0.2593        | 30.0  | 11250 | 0.4030          | 0.845    |
| 0.2187        | 31.0  | 11625 | 0.4051          | 0.8417   |
| 0.1821        | 32.0  | 12000 | 0.4072          | 0.8467   |
| 0.2095        | 33.0  | 12375 | 0.4076          | 0.8433   |
| 0.2109        | 34.0  | 12750 | 0.4087          | 0.8433   |
| 0.1759        | 35.0  | 13125 | 0.4129          | 0.84     |
| 0.1595        | 36.0  | 13500 | 0.4130          | 0.8433   |
| 0.2131        | 37.0  | 13875 | 0.4150          | 0.84     |
| 0.2036        | 38.0  | 14250 | 0.4132          | 0.85     |
| 0.247         | 39.0  | 14625 | 0.4135          | 0.8433   |
| 0.2148        | 40.0  | 15000 | 0.4147          | 0.8433   |
| 0.2333        | 41.0  | 15375 | 0.4120          | 0.8433   |
| 0.213         | 42.0  | 15750 | 0.4128          | 0.8433   |
| 0.1929        | 43.0  | 16125 | 0.4163          | 0.84     |
| 0.1822        | 44.0  | 16500 | 0.4161          | 0.845    |
| 0.2316        | 45.0  | 16875 | 0.4158          | 0.845    |
| 0.1873        | 46.0  | 17250 | 0.4147          | 0.845    |
| 0.2645        | 47.0  | 17625 | 0.4157          | 0.845    |
| 0.1954        | 48.0  | 18000 | 0.4157          | 0.845    |
| 0.1804        | 49.0  | 18375 | 0.4155          | 0.8467   |
| 0.1952        | 50.0  | 18750 | 0.4156          | 0.8467   |


### 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