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# smids_5x_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: 0.2832
- Accuracy: 0.8867

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6263        | 1.0   | 375   | 0.6777          | 0.725    |
| 0.5866        | 2.0   | 750   | 0.4870          | 0.81     |
| 0.4483        | 3.0   | 1125  | 0.4247          | 0.825    |
| 0.4271        | 4.0   | 1500  | 0.3855          | 0.84     |
| 0.4066        | 5.0   | 1875  | 0.3633          | 0.8467   |
| 0.3828        | 6.0   | 2250  | 0.3474          | 0.8417   |
| 0.309         | 7.0   | 2625  | 0.3371          | 0.8583   |
| 0.3188        | 8.0   | 3000  | 0.3295          | 0.86     |
| 0.3147        | 9.0   | 3375  | 0.3210          | 0.8633   |
| 0.2842        | 10.0  | 3750  | 0.3163          | 0.8633   |
| 0.258         | 11.0  | 4125  | 0.3059          | 0.87     |
| 0.2796        | 12.0  | 4500  | 0.3036          | 0.8717   |
| 0.2552        | 13.0  | 4875  | 0.2994          | 0.87     |
| 0.2763        | 14.0  | 5250  | 0.2979          | 0.8633   |
| 0.2925        | 15.0  | 5625  | 0.3004          | 0.865    |
| 0.2222        | 16.0  | 6000  | 0.2915          | 0.8767   |
| 0.2839        | 17.0  | 6375  | 0.2879          | 0.8783   |
| 0.2546        | 18.0  | 6750  | 0.2876          | 0.88     |
| 0.2528        | 19.0  | 7125  | 0.2899          | 0.8817   |
| 0.1895        | 20.0  | 7500  | 0.2841          | 0.885    |
| 0.2366        | 21.0  | 7875  | 0.2901          | 0.8767   |
| 0.2149        | 22.0  | 8250  | 0.2831          | 0.8883   |
| 0.2987        | 23.0  | 8625  | 0.2845          | 0.8833   |
| 0.232         | 24.0  | 9000  | 0.2818          | 0.885    |
| 0.2416        | 25.0  | 9375  | 0.2809          | 0.8883   |
| 0.2147        | 26.0  | 9750  | 0.2789          | 0.8867   |
| 0.2824        | 27.0  | 10125 | 0.2796          | 0.8883   |
| 0.2229        | 28.0  | 10500 | 0.2814          | 0.8883   |
| 0.2625        | 29.0  | 10875 | 0.2884          | 0.8767   |
| 0.1908        | 30.0  | 11250 | 0.2826          | 0.885    |
| 0.2464        | 31.0  | 11625 | 0.2786          | 0.8867   |
| 0.2333        | 32.0  | 12000 | 0.2809          | 0.89     |
| 0.2568        | 33.0  | 12375 | 0.2768          | 0.8867   |
| 0.2444        | 34.0  | 12750 | 0.2777          | 0.8883   |
| 0.1971        | 35.0  | 13125 | 0.2787          | 0.8883   |
| 0.1586        | 36.0  | 13500 | 0.2808          | 0.8867   |
| 0.1628        | 37.0  | 13875 | 0.2838          | 0.8817   |
| 0.2206        | 38.0  | 14250 | 0.2772          | 0.8867   |
| 0.1707        | 39.0  | 14625 | 0.2818          | 0.8833   |
| 0.2328        | 40.0  | 15000 | 0.2820          | 0.8867   |
| 0.1705        | 41.0  | 15375 | 0.2828          | 0.89     |
| 0.1753        | 42.0  | 15750 | 0.2851          | 0.8867   |
| 0.2269        | 43.0  | 16125 | 0.2832          | 0.8933   |
| 0.1772        | 44.0  | 16500 | 0.2830          | 0.8883   |
| 0.235         | 45.0  | 16875 | 0.2841          | 0.8883   |
| 0.251         | 46.0  | 17250 | 0.2828          | 0.8867   |
| 0.2199        | 47.0  | 17625 | 0.2831          | 0.8883   |
| 0.1679        | 48.0  | 18000 | 0.2835          | 0.8867   |
| 0.2096        | 49.0  | 18375 | 0.2833          | 0.8867   |
| 0.22          | 50.0  | 18750 | 0.2832          | 0.8867   |


### 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