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# smids_5x_deit_base_rms_00001_fold1

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7394
- Accuracy: 0.9098

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1851        | 1.0   | 376   | 0.2437          | 0.8998   |
| 0.1172        | 2.0   | 752   | 0.2452          | 0.9065   |
| 0.078         | 3.0   | 1128  | 0.3051          | 0.9032   |
| 0.0038        | 4.0   | 1504  | 0.3755          | 0.9149   |
| 0.0013        | 5.0   | 1880  | 0.4803          | 0.9048   |
| 0.0105        | 6.0   | 2256  | 0.5332          | 0.8948   |
| 0.0008        | 7.0   | 2632  | 0.5088          | 0.9015   |
| 0.0133        | 8.0   | 3008  | 0.5291          | 0.9149   |
| 0.0349        | 9.0   | 3384  | 0.6409          | 0.9048   |
| 0.0001        | 10.0  | 3760  | 0.6103          | 0.8998   |
| 0.0039        | 11.0  | 4136  | 0.6150          | 0.9065   |
| 0.0005        | 12.0  | 4512  | 0.7088          | 0.8948   |
| 0.0           | 13.0  | 4888  | 0.6260          | 0.8965   |
| 0.0213        | 14.0  | 5264  | 0.6512          | 0.9065   |
| 0.0001        | 15.0  | 5640  | 0.6705          | 0.8965   |
| 0.0           | 16.0  | 6016  | 0.6402          | 0.9098   |
| 0.0           | 17.0  | 6392  | 0.7356          | 0.9015   |
| 0.0           | 18.0  | 6768  | 0.6866          | 0.8932   |
| 0.0035        | 19.0  | 7144  | 0.7211          | 0.8982   |
| 0.0098        | 20.0  | 7520  | 0.7353          | 0.8982   |
| 0.0           | 21.0  | 7896  | 0.7497          | 0.9032   |
| 0.0001        | 22.0  | 8272  | 0.7881          | 0.9015   |
| 0.0           | 23.0  | 8648  | 0.7075          | 0.9065   |
| 0.0           | 24.0  | 9024  | 0.8340          | 0.8948   |
| 0.0           | 25.0  | 9400  | 0.8050          | 0.9032   |
| 0.0028        | 26.0  | 9776  | 0.7114          | 0.8982   |
| 0.0           | 27.0  | 10152 | 0.6978          | 0.9048   |
| 0.0           | 28.0  | 10528 | 0.7140          | 0.9032   |
| 0.0032        | 29.0  | 10904 | 0.6871          | 0.9098   |
| 0.0032        | 30.0  | 11280 | 0.7619          | 0.9032   |
| 0.0           | 31.0  | 11656 | 0.7031          | 0.9082   |
| 0.0           | 32.0  | 12032 | 0.7126          | 0.9082   |
| 0.0           | 33.0  | 12408 | 0.7501          | 0.9082   |
| 0.0           | 34.0  | 12784 | 0.7212          | 0.9149   |
| 0.0           | 35.0  | 13160 | 0.7433          | 0.9098   |
| 0.0           | 36.0  | 13536 | 0.7330          | 0.9132   |
| 0.0           | 37.0  | 13912 | 0.7531          | 0.9065   |
| 0.0           | 38.0  | 14288 | 0.7193          | 0.9098   |
| 0.0           | 39.0  | 14664 | 0.7113          | 0.9132   |
| 0.0           | 40.0  | 15040 | 0.7484          | 0.9149   |
| 0.0           | 41.0  | 15416 | 0.7482          | 0.9132   |
| 0.0           | 42.0  | 15792 | 0.7262          | 0.9132   |
| 0.0           | 43.0  | 16168 | 0.7432          | 0.9149   |
| 0.0           | 44.0  | 16544 | 0.7418          | 0.9149   |
| 0.0           | 45.0  | 16920 | 0.7350          | 0.9115   |
| 0.0025        | 46.0  | 17296 | 0.7363          | 0.9115   |
| 0.0           | 47.0  | 17672 | 0.7386          | 0.9098   |
| 0.0           | 48.0  | 18048 | 0.7382          | 0.9098   |
| 0.0           | 49.0  | 18424 | 0.7382          | 0.9098   |
| 0.0023        | 50.0  | 18800 | 0.7394          | 0.9098   |


### 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