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# smids_10x_beit_large_sgd_0001_fold4

This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3577
- Accuracy: 0.8583

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.956         | 1.0   | 750   | 0.9742          | 0.4667   |
| 0.7783        | 2.0   | 1500  | 0.8200          | 0.63     |
| 0.7323        | 3.0   | 2250  | 0.7096          | 0.71     |
| 0.6337        | 4.0   | 3000  | 0.6341          | 0.7517   |
| 0.5065        | 5.0   | 3750  | 0.5795          | 0.775    |
| 0.4965        | 6.0   | 4500  | 0.5386          | 0.8      |
| 0.4578        | 7.0   | 5250  | 0.5091          | 0.8117   |
| 0.4692        | 8.0   | 6000  | 0.4857          | 0.825    |
| 0.4711        | 9.0   | 6750  | 0.4676          | 0.8333   |
| 0.3709        | 10.0  | 7500  | 0.4525          | 0.835    |
| 0.4051        | 11.0  | 8250  | 0.4402          | 0.8367   |
| 0.4533        | 12.0  | 9000  | 0.4305          | 0.8417   |
| 0.3537        | 13.0  | 9750  | 0.4215          | 0.8467   |
| 0.4025        | 14.0  | 10500 | 0.4147          | 0.8483   |
| 0.3254        | 15.0  | 11250 | 0.4082          | 0.8467   |
| 0.3312        | 16.0  | 12000 | 0.4031          | 0.8467   |
| 0.2854        | 17.0  | 12750 | 0.3983          | 0.8483   |
| 0.3355        | 18.0  | 13500 | 0.3942          | 0.8517   |
| 0.3881        | 19.0  | 14250 | 0.3905          | 0.8483   |
| 0.3257        | 20.0  | 15000 | 0.3873          | 0.8517   |
| 0.3303        | 21.0  | 15750 | 0.3846          | 0.8483   |
| 0.3308        | 22.0  | 16500 | 0.3815          | 0.8517   |
| 0.3025        | 23.0  | 17250 | 0.3791          | 0.85     |
| 0.3591        | 24.0  | 18000 | 0.3770          | 0.8517   |
| 0.3426        | 25.0  | 18750 | 0.3750          | 0.8567   |
| 0.2909        | 26.0  | 19500 | 0.3737          | 0.8567   |
| 0.3106        | 27.0  | 20250 | 0.3719          | 0.855    |
| 0.3129        | 28.0  | 21000 | 0.3704          | 0.855    |
| 0.2957        | 29.0  | 21750 | 0.3688          | 0.855    |
| 0.2639        | 30.0  | 22500 | 0.3673          | 0.855    |
| 0.2821        | 31.0  | 23250 | 0.3660          | 0.855    |
| 0.2912        | 32.0  | 24000 | 0.3649          | 0.8567   |
| 0.3006        | 33.0  | 24750 | 0.3640          | 0.8583   |
| 0.3129        | 34.0  | 25500 | 0.3632          | 0.8583   |
| 0.2463        | 35.0  | 26250 | 0.3625          | 0.86     |
| 0.3133        | 36.0  | 27000 | 0.3619          | 0.8583   |
| 0.3061        | 37.0  | 27750 | 0.3612          | 0.8583   |
| 0.3206        | 38.0  | 28500 | 0.3606          | 0.8583   |
| 0.3433        | 39.0  | 29250 | 0.3601          | 0.8583   |
| 0.3138        | 40.0  | 30000 | 0.3597          | 0.8583   |
| 0.2988        | 41.0  | 30750 | 0.3593          | 0.8583   |
| 0.3075        | 42.0  | 31500 | 0.3589          | 0.8583   |
| 0.3059        | 43.0  | 32250 | 0.3587          | 0.8583   |
| 0.3142        | 44.0  | 33000 | 0.3585          | 0.8583   |
| 0.3034        | 45.0  | 33750 | 0.3583          | 0.8583   |
| 0.2744        | 46.0  | 34500 | 0.3580          | 0.8583   |
| 0.2599        | 47.0  | 35250 | 0.3579          | 0.8583   |
| 0.2643        | 48.0  | 36000 | 0.3578          | 0.8583   |
| 0.2927        | 49.0  | 36750 | 0.3577          | 0.8583   |
| 0.2381        | 50.0  | 37500 | 0.3577          | 0.8583   |


### 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