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# smids_3x_beit_base_sgd_001_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: 0.3105
- Accuracy: 0.8783

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8516        | 1.0   | 225   | 0.8297          | 0.6267   |
| 0.6679        | 2.0   | 450   | 0.6103          | 0.7567   |
| 0.57          | 3.0   | 675   | 0.5223          | 0.7883   |
| 0.4959        | 4.0   | 900   | 0.4753          | 0.8083   |
| 0.4424        | 5.0   | 1125  | 0.4319          | 0.8233   |
| 0.4261        | 6.0   | 1350  | 0.4129          | 0.8283   |
| 0.4396        | 7.0   | 1575  | 0.4075          | 0.8167   |
| 0.4595        | 8.0   | 1800  | 0.3942          | 0.8267   |
| 0.4172        | 9.0   | 2025  | 0.3692          | 0.8367   |
| 0.3688        | 10.0  | 2250  | 0.3605          | 0.8583   |
| 0.4132        | 11.0  | 2475  | 0.3610          | 0.8417   |
| 0.369         | 12.0  | 2700  | 0.3465          | 0.8567   |
| 0.3672        | 13.0  | 2925  | 0.3443          | 0.8517   |
| 0.3409        | 14.0  | 3150  | 0.3437          | 0.855    |
| 0.2695        | 15.0  | 3375  | 0.3370          | 0.8567   |
| 0.311         | 16.0  | 3600  | 0.3373          | 0.8533   |
| 0.3177        | 17.0  | 3825  | 0.3325          | 0.8567   |
| 0.3059        | 18.0  | 4050  | 0.3310          | 0.8567   |
| 0.3295        | 19.0  | 4275  | 0.3271          | 0.8583   |
| 0.3201        | 20.0  | 4500  | 0.3301          | 0.8667   |
| 0.2645        | 21.0  | 4725  | 0.3242          | 0.8683   |
| 0.2497        | 22.0  | 4950  | 0.3240          | 0.8633   |
| 0.2626        | 23.0  | 5175  | 0.3196          | 0.8617   |
| 0.267         | 24.0  | 5400  | 0.3185          | 0.8733   |
| 0.2637        | 25.0  | 5625  | 0.3155          | 0.8733   |
| 0.3416        | 26.0  | 5850  | 0.3155          | 0.8783   |
| 0.3255        | 27.0  | 6075  | 0.3159          | 0.8767   |
| 0.3021        | 28.0  | 6300  | 0.3189          | 0.875    |
| 0.2292        | 29.0  | 6525  | 0.3137          | 0.8783   |
| 0.2207        | 30.0  | 6750  | 0.3185          | 0.8733   |
| 0.2158        | 31.0  | 6975  | 0.3173          | 0.8683   |
| 0.2149        | 32.0  | 7200  | 0.3154          | 0.87     |
| 0.248         | 33.0  | 7425  | 0.3134          | 0.8767   |
| 0.2339        | 34.0  | 7650  | 0.3133          | 0.875    |
| 0.2585        | 35.0  | 7875  | 0.3147          | 0.8767   |
| 0.2565        | 36.0  | 8100  | 0.3120          | 0.875    |
| 0.269         | 37.0  | 8325  | 0.3111          | 0.8783   |
| 0.2546        | 38.0  | 8550  | 0.3139          | 0.8733   |
| 0.2114        | 39.0  | 8775  | 0.3110          | 0.8767   |
| 0.2032        | 40.0  | 9000  | 0.3108          | 0.8767   |
| 0.2376        | 41.0  | 9225  | 0.3108          | 0.8783   |
| 0.2558        | 42.0  | 9450  | 0.3092          | 0.8767   |
| 0.2753        | 43.0  | 9675  | 0.3113          | 0.875    |
| 0.2795        | 44.0  | 9900  | 0.3109          | 0.8767   |
| 0.2412        | 45.0  | 10125 | 0.3113          | 0.8783   |
| 0.2003        | 46.0  | 10350 | 0.3105          | 0.88     |
| 0.2528        | 47.0  | 10575 | 0.3109          | 0.88     |
| 0.2265        | 48.0  | 10800 | 0.3109          | 0.8783   |
| 0.2494        | 49.0  | 11025 | 0.3106          | 0.8783   |
| 0.2763        | 50.0  | 11250 | 0.3105          | 0.8783   |


### 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