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# smids_3x_beit_base_adamax_0001_fold1

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: 0.7494
- Accuracy: 0.9115

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3678        | 1.0   | 226   | 0.3292          | 0.8614   |
| 0.2124        | 2.0   | 452   | 0.3720          | 0.8815   |
| 0.1134        | 3.0   | 678   | 0.4692          | 0.8631   |
| 0.0789        | 4.0   | 904   | 0.3549          | 0.9032   |
| 0.0454        | 5.0   | 1130  | 0.4305          | 0.9048   |
| 0.0205        | 6.0   | 1356  | 0.5024          | 0.9149   |
| 0.001         | 7.0   | 1582  | 0.5548          | 0.9065   |
| 0.0104        | 8.0   | 1808  | 0.5394          | 0.8998   |
| 0.0382        | 9.0   | 2034  | 0.5732          | 0.9149   |
| 0.0007        | 10.0  | 2260  | 0.6012          | 0.9098   |
| 0.0391        | 11.0  | 2486  | 0.5763          | 0.9082   |
| 0.0059        | 12.0  | 2712  | 0.6108          | 0.9065   |
| 0.0173        | 13.0  | 2938  | 0.5672          | 0.9115   |
| 0.017         | 14.0  | 3164  | 0.7490          | 0.8982   |
| 0.011         | 15.0  | 3390  | 0.6808          | 0.9065   |
| 0.0001        | 16.0  | 3616  | 0.6376          | 0.9115   |
| 0.01          | 17.0  | 3842  | 0.6232          | 0.9065   |
| 0.001         | 18.0  | 4068  | 0.6761          | 0.8982   |
| 0.0042        | 19.0  | 4294  | 0.7354          | 0.9115   |
| 0.0001        | 20.0  | 4520  | 0.6861          | 0.9098   |
| 0.0007        | 21.0  | 4746  | 0.7202          | 0.9065   |
| 0.0044        | 22.0  | 4972  | 0.6969          | 0.9082   |
| 0.0048        | 23.0  | 5198  | 0.6620          | 0.9199   |
| 0.0           | 24.0  | 5424  | 0.7820          | 0.8998   |
| 0.0           | 25.0  | 5650  | 0.6630          | 0.9149   |
| 0.0           | 26.0  | 5876  | 0.6962          | 0.9165   |
| 0.0           | 27.0  | 6102  | 0.7046          | 0.9149   |
| 0.0119        | 28.0  | 6328  | 0.8033          | 0.9032   |
| 0.0           | 29.0  | 6554  | 0.6906          | 0.9115   |
| 0.0002        | 30.0  | 6780  | 0.6827          | 0.9098   |
| 0.0002        | 31.0  | 7006  | 0.7730          | 0.9065   |
| 0.0           | 32.0  | 7232  | 0.8017          | 0.9015   |
| 0.004         | 33.0  | 7458  | 0.7703          | 0.9098   |
| 0.0001        | 34.0  | 7684  | 0.7283          | 0.9098   |
| 0.0           | 35.0  | 7910  | 0.7503          | 0.9065   |
| 0.0           | 36.0  | 8136  | 0.7083          | 0.9149   |
| 0.0           | 37.0  | 8362  | 0.7770          | 0.9048   |
| 0.0           | 38.0  | 8588  | 0.7053          | 0.9165   |
| 0.0           | 39.0  | 8814  | 0.7150          | 0.9165   |
| 0.0           | 40.0  | 9040  | 0.7204          | 0.9182   |
| 0.0022        | 41.0  | 9266  | 0.7127          | 0.9165   |
| 0.0033        | 42.0  | 9492  | 0.7275          | 0.9149   |
| 0.0           | 43.0  | 9718  | 0.7350          | 0.9165   |
| 0.0           | 44.0  | 9944  | 0.7337          | 0.9149   |
| 0.0           | 45.0  | 10170 | 0.7372          | 0.9115   |
| 0.0002        | 46.0  | 10396 | 0.7514          | 0.9165   |
| 0.0           | 47.0  | 10622 | 0.7501          | 0.9115   |
| 0.0           | 48.0  | 10848 | 0.7502          | 0.9149   |
| 0.0           | 49.0  | 11074 | 0.7494          | 0.9098   |
| 0.0           | 50.0  | 11300 | 0.7494          | 0.9115   |


### 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