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# smids_5x_deit_small_rms_0001_fold1

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9277
- Accuracy: 0.9048

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2576        | 1.0   | 376   | 0.2886          | 0.8915   |
| 0.132         | 2.0   | 752   | 0.3675          | 0.8881   |
| 0.153         | 3.0   | 1128  | 0.3722          | 0.8815   |
| 0.1012        | 4.0   | 1504  | 0.5411          | 0.8715   |
| 0.0774        | 5.0   | 1880  | 0.4743          | 0.8881   |
| 0.0966        | 6.0   | 2256  | 0.5187          | 0.8831   |
| 0.02          | 7.0   | 2632  | 0.7637          | 0.8548   |
| 0.0255        | 8.0   | 3008  | 0.5858          | 0.8982   |
| 0.0535        | 9.0   | 3384  | 0.7179          | 0.8798   |
| 0.0051        | 10.0  | 3760  | 0.4830          | 0.9132   |
| 0.0623        | 11.0  | 4136  | 0.6803          | 0.8898   |
| 0.032         | 12.0  | 4512  | 0.6393          | 0.8831   |
| 0.0049        | 13.0  | 4888  | 0.6430          | 0.8965   |
| 0.0375        | 14.0  | 5264  | 0.6697          | 0.8948   |
| 0.0305        | 15.0  | 5640  | 0.4958          | 0.9165   |
| 0.0041        | 16.0  | 6016  | 0.6462          | 0.8965   |
| 0.0225        | 17.0  | 6392  | 0.6064          | 0.9065   |
| 0.0015        | 18.0  | 6768  | 0.7328          | 0.8865   |
| 0.0129        | 19.0  | 7144  | 0.6712          | 0.8848   |
| 0.0072        | 20.0  | 7520  | 0.7644          | 0.8881   |
| 0.002         | 21.0  | 7896  | 0.6536          | 0.9065   |
| 0.0135        | 22.0  | 8272  | 0.7707          | 0.8881   |
| 0.0245        | 23.0  | 8648  | 0.6111          | 0.8948   |
| 0.0006        | 24.0  | 9024  | 0.7622          | 0.8881   |
| 0.0001        | 25.0  | 9400  | 0.7257          | 0.9015   |
| 0.0065        | 26.0  | 9776  | 0.7266          | 0.8948   |
| 0.0001        | 27.0  | 10152 | 0.7834          | 0.9082   |
| 0.0001        | 28.0  | 10528 | 0.7481          | 0.9032   |
| 0.0047        | 29.0  | 10904 | 0.8083          | 0.8915   |
| 0.0032        | 30.0  | 11280 | 0.7670          | 0.8948   |
| 0.0008        | 31.0  | 11656 | 0.8608          | 0.8881   |
| 0.0001        | 32.0  | 12032 | 0.7792          | 0.8948   |
| 0.0001        | 33.0  | 12408 | 0.8789          | 0.8932   |
| 0.0           | 34.0  | 12784 | 0.7571          | 0.9015   |
| 0.0           | 35.0  | 13160 | 0.7309          | 0.9115   |
| 0.0002        | 36.0  | 13536 | 0.7237          | 0.9082   |
| 0.0           | 37.0  | 13912 | 0.8459          | 0.9015   |
| 0.0           | 38.0  | 14288 | 0.8205          | 0.9082   |
| 0.0           | 39.0  | 14664 | 0.8617          | 0.9048   |
| 0.0           | 40.0  | 15040 | 0.8709          | 0.8932   |
| 0.0           | 41.0  | 15416 | 0.8732          | 0.8915   |
| 0.0           | 42.0  | 15792 | 0.8524          | 0.8982   |
| 0.0           | 43.0  | 16168 | 0.8924          | 0.9048   |
| 0.0           | 44.0  | 16544 | 0.8692          | 0.8898   |
| 0.0           | 45.0  | 16920 | 0.8944          | 0.9015   |
| 0.0031        | 46.0  | 17296 | 0.8984          | 0.9032   |
| 0.0           | 47.0  | 17672 | 0.9119          | 0.9032   |
| 0.0           | 48.0  | 18048 | 0.9192          | 0.9048   |
| 0.0           | 49.0  | 18424 | 0.9260          | 0.9032   |
| 0.0023        | 50.0  | 18800 | 0.9277          | 0.9048   |


### 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