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# smids_5x_deit_small_rms_00001_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: 0.7417
- Accuracy: 0.9232

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2432        | 1.0   | 376   | 0.2964          | 0.8731   |
| 0.1614        | 2.0   | 752   | 0.2849          | 0.8932   |
| 0.1382        | 3.0   | 1128  | 0.2988          | 0.9048   |
| 0.0674        | 4.0   | 1504  | 0.3782          | 0.9082   |
| 0.0238        | 5.0   | 1880  | 0.4767          | 0.9132   |
| 0.0038        | 6.0   | 2256  | 0.4683          | 0.9215   |
| 0.0081        | 7.0   | 2632  | 0.5149          | 0.9015   |
| 0.0311        | 8.0   | 3008  | 0.6215          | 0.9082   |
| 0.0258        | 9.0   | 3384  | 0.6420          | 0.9015   |
| 0.0003        | 10.0  | 3760  | 0.7389          | 0.8982   |
| 0.0016        | 11.0  | 4136  | 0.7097          | 0.9032   |
| 0.0237        | 12.0  | 4512  | 0.7322          | 0.8965   |
| 0.0           | 13.0  | 4888  | 0.6330          | 0.9065   |
| 0.0121        | 14.0  | 5264  | 0.6713          | 0.8998   |
| 0.0128        | 15.0  | 5640  | 0.6959          | 0.9032   |
| 0.0           | 16.0  | 6016  | 0.5921          | 0.9165   |
| 0.0147        | 17.0  | 6392  | 0.7286          | 0.9032   |
| 0.0096        | 18.0  | 6768  | 0.6654          | 0.9115   |
| 0.0001        | 19.0  | 7144  | 0.7241          | 0.9065   |
| 0.026         | 20.0  | 7520  | 0.7595          | 0.9115   |
| 0.0004        | 21.0  | 7896  | 0.7089          | 0.9132   |
| 0.0001        | 22.0  | 8272  | 0.7020          | 0.9132   |
| 0.0189        | 23.0  | 8648  | 0.7064          | 0.9032   |
| 0.0           | 24.0  | 9024  | 0.6953          | 0.9182   |
| 0.0008        | 25.0  | 9400  | 0.6754          | 0.9048   |
| 0.0029        | 26.0  | 9776  | 0.6682          | 0.9149   |
| 0.0039        | 27.0  | 10152 | 0.7036          | 0.9115   |
| 0.0           | 28.0  | 10528 | 0.7901          | 0.9098   |
| 0.0047        | 29.0  | 10904 | 0.7958          | 0.9165   |
| 0.0042        | 30.0  | 11280 | 0.7246          | 0.9115   |
| 0.0           | 31.0  | 11656 | 0.7694          | 0.9132   |
| 0.0           | 32.0  | 12032 | 0.7581          | 0.9082   |
| 0.0           | 33.0  | 12408 | 0.7146          | 0.9149   |
| 0.0           | 34.0  | 12784 | 0.7034          | 0.9165   |
| 0.0           | 35.0  | 13160 | 0.7688          | 0.9115   |
| 0.0           | 36.0  | 13536 | 0.7638          | 0.9132   |
| 0.0           | 37.0  | 13912 | 0.8028          | 0.9115   |
| 0.0           | 38.0  | 14288 | 0.7323          | 0.9215   |
| 0.0           | 39.0  | 14664 | 0.7555          | 0.9199   |
| 0.0           | 40.0  | 15040 | 0.7506          | 0.9215   |
| 0.0           | 41.0  | 15416 | 0.7416          | 0.9215   |
| 0.0           | 42.0  | 15792 | 0.7376          | 0.9199   |
| 0.0           | 43.0  | 16168 | 0.7280          | 0.9215   |
| 0.0           | 44.0  | 16544 | 0.7390          | 0.9215   |
| 0.0           | 45.0  | 16920 | 0.7365          | 0.9232   |
| 0.0028        | 46.0  | 17296 | 0.7367          | 0.9232   |
| 0.0           | 47.0  | 17672 | 0.7395          | 0.9232   |
| 0.0           | 48.0  | 18048 | 0.7408          | 0.9232   |
| 0.0           | 49.0  | 18424 | 0.7418          | 0.9232   |
| 0.0024        | 50.0  | 18800 | 0.7417          | 0.9232   |


### 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