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# smids_3x_deit_base_rms_0001_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: 0.8578
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3767        | 1.0   | 226   | 0.4015          | 0.8047   |
| 0.1856        | 2.0   | 452   | 0.2907          | 0.8915   |
| 0.122         | 3.0   | 678   | 0.3819          | 0.8397   |
| 0.0716        | 4.0   | 904   | 0.6439          | 0.8598   |
| 0.0597        | 5.0   | 1130  | 0.4947          | 0.8831   |
| 0.0636        | 6.0   | 1356  | 0.4627          | 0.8965   |
| 0.0123        | 7.0   | 1582  | 0.5193          | 0.8798   |
| 0.0384        | 8.0   | 1808  | 0.5328          | 0.8965   |
| 0.0347        | 9.0   | 2034  | 0.5230          | 0.8865   |
| 0.0555        | 10.0  | 2260  | 0.4625          | 0.8915   |
| 0.0105        | 11.0  | 2486  | 0.4967          | 0.9032   |
| 0.0151        | 12.0  | 2712  | 0.5936          | 0.8798   |
| 0.0374        | 13.0  | 2938  | 0.5700          | 0.8881   |
| 0.0272        | 14.0  | 3164  | 0.5683          | 0.8915   |
| 0.0029        | 15.0  | 3390  | 0.8104          | 0.8815   |
| 0.0276        | 16.0  | 3616  | 0.6803          | 0.8932   |
| 0.006         | 17.0  | 3842  | 0.6793          | 0.8781   |
| 0.0461        | 18.0  | 4068  | 0.6650          | 0.8815   |
| 0.006         | 19.0  | 4294  | 0.8601          | 0.8831   |
| 0.0039        | 20.0  | 4520  | 0.5720          | 0.8948   |
| 0.0002        | 21.0  | 4746  | 0.6983          | 0.8948   |
| 0.0089        | 22.0  | 4972  | 0.6968          | 0.8865   |
| 0.0025        | 23.0  | 5198  | 0.7765          | 0.9032   |
| 0.0037        | 24.0  | 5424  | 0.7330          | 0.8965   |
| 0.0105        | 25.0  | 5650  | 0.5590          | 0.8932   |
| 0.0002        | 26.0  | 5876  | 0.6884          | 0.9048   |
| 0.0001        | 27.0  | 6102  | 0.6695          | 0.9015   |
| 0.0048        | 28.0  | 6328  | 0.7561          | 0.8848   |
| 0.0001        | 29.0  | 6554  | 0.8455          | 0.8831   |
| 0.0168        | 30.0  | 6780  | 0.6624          | 0.8932   |
| 0.013         | 31.0  | 7006  | 0.7840          | 0.8932   |
| 0.0           | 32.0  | 7232  | 0.6961          | 0.8982   |
| 0.0033        | 33.0  | 7458  | 0.8341          | 0.8915   |
| 0.0           | 34.0  | 7684  | 0.7715          | 0.9048   |
| 0.0           | 35.0  | 7910  | 0.8192          | 0.9015   |
| 0.0           | 36.0  | 8136  | 0.7732          | 0.9048   |
| 0.0           | 37.0  | 8362  | 0.7832          | 0.9098   |
| 0.0           | 38.0  | 8588  | 0.7728          | 0.9065   |
| 0.0           | 39.0  | 8814  | 0.8176          | 0.9065   |
| 0.0           | 40.0  | 9040  | 0.8093          | 0.9048   |
| 0.0026        | 41.0  | 9266  | 0.7762          | 0.9132   |
| 0.0021        | 42.0  | 9492  | 0.7747          | 0.9065   |
| 0.0           | 43.0  | 9718  | 0.7876          | 0.9048   |
| 0.0           | 44.0  | 9944  | 0.7913          | 0.9015   |
| 0.0           | 45.0  | 10170 | 0.8068          | 0.9015   |
| 0.0           | 46.0  | 10396 | 0.8218          | 0.9015   |
| 0.0           | 47.0  | 10622 | 0.8475          | 0.9048   |
| 0.0           | 48.0  | 10848 | 0.8522          | 0.9048   |
| 0.0           | 49.0  | 11074 | 0.8566          | 0.9048   |
| 0.0           | 50.0  | 11300 | 0.8578          | 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