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# smids_5x_beit_base_rms_00001_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: 1.3109
- Accuracy: 0.8933

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1968        | 1.0   | 375   | 0.3151          | 0.8917   |
| 0.1474        | 2.0   | 750   | 0.4474          | 0.8667   |
| 0.0741        | 3.0   | 1125  | 0.4618          | 0.89     |
| 0.0638        | 4.0   | 1500  | 0.5553          | 0.9083   |
| 0.0357        | 5.0   | 1875  | 0.7199          | 0.8767   |
| 0.027         | 6.0   | 2250  | 0.8598          | 0.8783   |
| 0.0037        | 7.0   | 2625  | 1.0235          | 0.8817   |
| 0.0229        | 8.0   | 3000  | 1.0021          | 0.8817   |
| 0.0002        | 9.0   | 3375  | 1.0533          | 0.88     |
| 0.0003        | 10.0  | 3750  | 1.0170          | 0.8917   |
| 0.0047        | 11.0  | 4125  | 1.0274          | 0.885    |
| 0.0161        | 12.0  | 4500  | 0.9972          | 0.8883   |
| 0.0395        | 13.0  | 4875  | 1.1208          | 0.8817   |
| 0.0195        | 14.0  | 5250  | 1.1819          | 0.8833   |
| 0.0231        | 15.0  | 5625  | 1.2063          | 0.8867   |
| 0.002         | 16.0  | 6000  | 1.1906          | 0.8817   |
| 0.0189        | 17.0  | 6375  | 1.3367          | 0.8683   |
| 0.006         | 18.0  | 6750  | 1.3216          | 0.8767   |
| 0.0201        | 19.0  | 7125  | 1.2482          | 0.8883   |
| 0.0004        | 20.0  | 7500  | 1.3064          | 0.88     |
| 0.0           | 21.0  | 7875  | 1.2624          | 0.8833   |
| 0.0332        | 22.0  | 8250  | 1.2916          | 0.8783   |
| 0.0001        | 23.0  | 8625  | 1.2718          | 0.875    |
| 0.0134        | 24.0  | 9000  | 1.2861          | 0.8767   |
| 0.0091        | 25.0  | 9375  | 1.2558          | 0.8867   |
| 0.0           | 26.0  | 9750  | 1.1412          | 0.875    |
| 0.0003        | 27.0  | 10125 | 1.1757          | 0.8883   |
| 0.0           | 28.0  | 10500 | 1.1969          | 0.885    |
| 0.0001        | 29.0  | 10875 | 1.2159          | 0.8833   |
| 0.0439        | 30.0  | 11250 | 1.2112          | 0.885    |
| 0.0           | 31.0  | 11625 | 1.1996          | 0.8867   |
| 0.0011        | 32.0  | 12000 | 1.2726          | 0.8917   |
| 0.0           | 33.0  | 12375 | 1.2290          | 0.895    |
| 0.003         | 34.0  | 12750 | 1.2689          | 0.885    |
| 0.0001        | 35.0  | 13125 | 1.2685          | 0.8833   |
| 0.0           | 36.0  | 13500 | 1.2338          | 0.89     |
| 0.0           | 37.0  | 13875 | 1.2931          | 0.8817   |
| 0.0037        | 38.0  | 14250 | 1.2980          | 0.8833   |
| 0.0           | 39.0  | 14625 | 1.3078          | 0.895    |
| 0.0           | 40.0  | 15000 | 1.3295          | 0.8867   |
| 0.0           | 41.0  | 15375 | 1.2988          | 0.8917   |
| 0.0           | 42.0  | 15750 | 1.3679          | 0.8817   |
| 0.0           | 43.0  | 16125 | 1.3182          | 0.8833   |
| 0.0           | 44.0  | 16500 | 1.3785          | 0.885    |
| 0.0           | 45.0  | 16875 | 1.3130          | 0.8833   |
| 0.0008        | 46.0  | 17250 | 1.3226          | 0.8917   |
| 0.028         | 47.0  | 17625 | 1.3211          | 0.89     |
| 0.0214        | 48.0  | 18000 | 1.3040          | 0.8933   |
| 0.0           | 49.0  | 18375 | 1.3082          | 0.8917   |
| 0.0           | 50.0  | 18750 | 1.3109          | 0.8933   |


### 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