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# smids_5x_deit_base_sgd_0001_fold3

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: 0.5127
- Accuracy: 0.8183

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.086         | 1.0   | 375   | 1.0870          | 0.4083   |
| 1.0544        | 2.0   | 750   | 1.0666          | 0.4433   |
| 1.0309        | 3.0   | 1125  | 1.0448          | 0.48     |
| 0.9851        | 4.0   | 1500  | 1.0206          | 0.5333   |
| 0.9784        | 5.0   | 1875  | 0.9932          | 0.575    |
| 0.9454        | 6.0   | 2250  | 0.9632          | 0.6017   |
| 0.9054        | 7.0   | 2625  | 0.9323          | 0.6317   |
| 0.8859        | 8.0   | 3000  | 0.9017          | 0.6617   |
| 0.8212        | 9.0   | 3375  | 0.8722          | 0.675    |
| 0.846         | 10.0  | 3750  | 0.8441          | 0.69     |
| 0.7793        | 11.0  | 4125  | 0.8178          | 0.6967   |
| 0.7556        | 12.0  | 4500  | 0.7933          | 0.72     |
| 0.753         | 13.0  | 4875  | 0.7705          | 0.7333   |
| 0.7537        | 14.0  | 5250  | 0.7493          | 0.7433   |
| 0.7009        | 15.0  | 5625  | 0.7292          | 0.7517   |
| 0.6573        | 16.0  | 6000  | 0.7106          | 0.7517   |
| 0.6888        | 17.0  | 6375  | 0.6931          | 0.7583   |
| 0.6952        | 18.0  | 6750  | 0.6769          | 0.765    |
| 0.6154        | 19.0  | 7125  | 0.6622          | 0.7733   |
| 0.6418        | 20.0  | 7500  | 0.6487          | 0.7833   |
| 0.5745        | 21.0  | 7875  | 0.6362          | 0.7883   |
| 0.5966        | 22.0  | 8250  | 0.6248          | 0.7933   |
| 0.6053        | 23.0  | 8625  | 0.6144          | 0.7983   |
| 0.6111        | 24.0  | 9000  | 0.6047          | 0.8      |
| 0.5758        | 25.0  | 9375  | 0.5959          | 0.805    |
| 0.5916        | 26.0  | 9750  | 0.5876          | 0.8067   |
| 0.5804        | 27.0  | 10125 | 0.5800          | 0.81     |
| 0.5956        | 28.0  | 10500 | 0.5730          | 0.81     |
| 0.5266        | 29.0  | 10875 | 0.5666          | 0.81     |
| 0.5435        | 30.0  | 11250 | 0.5607          | 0.8117   |
| 0.566         | 31.0  | 11625 | 0.5553          | 0.8133   |
| 0.5691        | 32.0  | 12000 | 0.5503          | 0.8133   |
| 0.6036        | 33.0  | 12375 | 0.5456          | 0.815    |
| 0.5178        | 34.0  | 12750 | 0.5415          | 0.8133   |
| 0.4823        | 35.0  | 13125 | 0.5375          | 0.8167   |
| 0.4738        | 36.0  | 13500 | 0.5340          | 0.8183   |
| 0.5126        | 37.0  | 13875 | 0.5307          | 0.8183   |
| 0.4971        | 38.0  | 14250 | 0.5278          | 0.8183   |
| 0.5124        | 39.0  | 14625 | 0.5252          | 0.82     |
| 0.4766        | 40.0  | 15000 | 0.5229          | 0.82     |
| 0.4547        | 41.0  | 15375 | 0.5208          | 0.82     |
| 0.5009        | 42.0  | 15750 | 0.5190          | 0.82     |
| 0.5345        | 43.0  | 16125 | 0.5174          | 0.82     |
| 0.5032        | 44.0  | 16500 | 0.5161          | 0.8183   |
| 0.4932        | 45.0  | 16875 | 0.5150          | 0.8183   |
| 0.5003        | 46.0  | 17250 | 0.5141          | 0.8183   |
| 0.5538        | 47.0  | 17625 | 0.5134          | 0.8183   |
| 0.5053        | 48.0  | 18000 | 0.5130          | 0.8183   |
| 0.5053        | 49.0  | 18375 | 0.5128          | 0.8183   |
| 0.4871        | 50.0  | 18750 | 0.5127          | 0.8183   |


### 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