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# smids_5x_deit_small_rms_0001_fold4

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: 1.4198
- Accuracy: 0.8783

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2762        | 1.0   | 375   | 0.4287          | 0.8433   |
| 0.1833        | 2.0   | 750   | 0.3850          | 0.88     |
| 0.0712        | 3.0   | 1125  | 0.5130          | 0.8533   |
| 0.1056        | 4.0   | 1500  | 0.5359          | 0.8617   |
| 0.0785        | 5.0   | 1875  | 0.5955          | 0.8767   |
| 0.0284        | 6.0   | 2250  | 0.6660          | 0.875    |
| 0.0398        | 7.0   | 2625  | 0.7191          | 0.8517   |
| 0.0358        | 8.0   | 3000  | 0.7890          | 0.855    |
| 0.0148        | 9.0   | 3375  | 0.8497          | 0.88     |
| 0.0842        | 10.0  | 3750  | 0.8652          | 0.8583   |
| 0.0545        | 11.0  | 4125  | 0.9852          | 0.8583   |
| 0.0347        | 12.0  | 4500  | 0.8898          | 0.8733   |
| 0.0481        | 13.0  | 4875  | 0.8728          | 0.8417   |
| 0.0109        | 14.0  | 5250  | 0.7860          | 0.8717   |
| 0.004         | 15.0  | 5625  | 0.9119          | 0.87     |
| 0.0001        | 16.0  | 6000  | 1.0067          | 0.8667   |
| 0.0208        | 17.0  | 6375  | 0.9891          | 0.8633   |
| 0.0022        | 18.0  | 6750  | 0.9452          | 0.8733   |
| 0.0258        | 19.0  | 7125  | 0.9437          | 0.865    |
| 0.0001        | 20.0  | 7500  | 0.9752          | 0.8867   |
| 0.014         | 21.0  | 7875  | 0.9198          | 0.8717   |
| 0.0001        | 22.0  | 8250  | 1.0263          | 0.8817   |
| 0.0007        | 23.0  | 8625  | 1.0253          | 0.875    |
| 0.0298        | 24.0  | 9000  | 0.9487          | 0.8717   |
| 0.0364        | 25.0  | 9375  | 1.0414          | 0.8567   |
| 0.0168        | 26.0  | 9750  | 0.9402          | 0.8667   |
| 0.0267        | 27.0  | 10125 | 1.0294          | 0.87     |
| 0.0004        | 28.0  | 10500 | 1.1186          | 0.8617   |
| 0.0           | 29.0  | 10875 | 1.0351          | 0.8733   |
| 0.0136        | 30.0  | 11250 | 1.0077          | 0.8667   |
| 0.0403        | 31.0  | 11625 | 0.9771          | 0.885    |
| 0.0141        | 32.0  | 12000 | 0.9784          | 0.875    |
| 0.0079        | 33.0  | 12375 | 0.9784          | 0.875    |
| 0.0198        | 34.0  | 12750 | 1.1841          | 0.87     |
| 0.0247        | 35.0  | 13125 | 1.1305          | 0.8767   |
| 0.0348        | 36.0  | 13500 | 1.1191          | 0.8733   |
| 0.0086        | 37.0  | 13875 | 1.0822          | 0.8817   |
| 0.004         | 38.0  | 14250 | 1.1525          | 0.8733   |
| 0.0           | 39.0  | 14625 | 1.3113          | 0.87     |
| 0.0           | 40.0  | 15000 | 1.2648          | 0.8767   |
| 0.0           | 41.0  | 15375 | 1.3219          | 0.88     |
| 0.0           | 42.0  | 15750 | 1.3106          | 0.88     |
| 0.0           | 43.0  | 16125 | 1.3404          | 0.8783   |
| 0.0           | 44.0  | 16500 | 1.3645          | 0.8783   |
| 0.0           | 45.0  | 16875 | 1.3862          | 0.875    |
| 0.0           | 46.0  | 17250 | 1.4020          | 0.8767   |
| 0.0           | 47.0  | 17625 | 1.4061          | 0.8783   |
| 0.0           | 48.0  | 18000 | 1.4149          | 0.8783   |
| 0.0           | 49.0  | 18375 | 1.4183          | 0.8783   |
| 0.0           | 50.0  | 18750 | 1.4198          | 0.8783   |


### 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