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# smids_1x_beit_base_adamax_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: 0.9690
- Accuracy: 0.8733

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3842        | 1.0   | 75   | 0.3836          | 0.8567   |
| 0.2698        | 2.0   | 150  | 0.3565          | 0.8717   |
| 0.2112        | 3.0   | 225  | 0.3725          | 0.8667   |
| 0.1563        | 4.0   | 300  | 0.3983          | 0.8683   |
| 0.0925        | 5.0   | 375  | 0.3901          | 0.875    |
| 0.1014        | 6.0   | 450  | 0.4180          | 0.8817   |
| 0.0818        | 7.0   | 525  | 0.4236          | 0.8733   |
| 0.0472        | 8.0   | 600  | 0.4670          | 0.87     |
| 0.0417        | 9.0   | 675  | 0.5177          | 0.8767   |
| 0.0198        | 10.0  | 750  | 0.5528          | 0.8683   |
| 0.0232        | 11.0  | 825  | 0.5777          | 0.875    |
| 0.0159        | 12.0  | 900  | 0.6214          | 0.8683   |
| 0.0174        | 13.0  | 975  | 0.6477          | 0.87     |
| 0.0205        | 14.0  | 1050 | 0.7117          | 0.8633   |
| 0.0429        | 15.0  | 1125 | 0.7038          | 0.875    |
| 0.0098        | 16.0  | 1200 | 0.7398          | 0.8733   |
| 0.0056        | 17.0  | 1275 | 0.7568          | 0.8717   |
| 0.016         | 18.0  | 1350 | 0.7774          | 0.8733   |
| 0.0366        | 19.0  | 1425 | 0.7871          | 0.8783   |
| 0.0462        | 20.0  | 1500 | 0.7545          | 0.8867   |
| 0.0036        | 21.0  | 1575 | 0.8298          | 0.8767   |
| 0.013         | 22.0  | 1650 | 0.8793          | 0.875    |
| 0.0139        | 23.0  | 1725 | 0.8645          | 0.88     |
| 0.0044        | 24.0  | 1800 | 0.8813          | 0.8717   |
| 0.0148        | 25.0  | 1875 | 0.8534          | 0.8767   |
| 0.0146        | 26.0  | 1950 | 0.8817          | 0.8767   |
| 0.0054        | 27.0  | 2025 | 0.9081          | 0.87     |
| 0.0007        | 28.0  | 2100 | 0.8989          | 0.8767   |
| 0.0046        | 29.0  | 2175 | 0.8951          | 0.88     |
| 0.0234        | 30.0  | 2250 | 0.9014          | 0.8717   |
| 0.0106        | 31.0  | 2325 | 0.9119          | 0.8667   |
| 0.0085        | 32.0  | 2400 | 0.9313          | 0.8717   |
| 0.0036        | 33.0  | 2475 | 0.9195          | 0.8733   |
| 0.001         | 34.0  | 2550 | 0.9166          | 0.8717   |
| 0.0098        | 35.0  | 2625 | 0.9378          | 0.87     |
| 0.0089        | 36.0  | 2700 | 0.9278          | 0.8717   |
| 0.0099        | 37.0  | 2775 | 0.9534          | 0.8717   |
| 0.0248        | 38.0  | 2850 | 0.9419          | 0.8783   |
| 0.0327        | 39.0  | 2925 | 0.9391          | 0.8733   |
| 0.0223        | 40.0  | 3000 | 0.9364          | 0.875    |
| 0.0147        | 41.0  | 3075 | 0.9305          | 0.8767   |
| 0.0288        | 42.0  | 3150 | 0.9572          | 0.8783   |
| 0.0191        | 43.0  | 3225 | 0.9619          | 0.875    |
| 0.0008        | 44.0  | 3300 | 0.9576          | 0.875    |
| 0.0019        | 45.0  | 3375 | 0.9660          | 0.8733   |
| 0.0022        | 46.0  | 3450 | 0.9692          | 0.875    |
| 0.0015        | 47.0  | 3525 | 0.9668          | 0.875    |
| 0.0054        | 48.0  | 3600 | 0.9744          | 0.8733   |
| 0.0016        | 49.0  | 3675 | 0.9694          | 0.8733   |
| 0.0003        | 50.0  | 3750 | 0.9690          | 0.8733   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

abnormal_sperm non-sperm normal_sperm