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# smids_5x_deit_base_adamax_00001_fold4

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: 1.1380
- Accuracy: 0.8667

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2793        | 1.0   | 375   | 0.3375          | 0.8733   |
| 0.2086        | 2.0   | 750   | 0.3220          | 0.8767   |
| 0.0773        | 3.0   | 1125  | 0.3501          | 0.87     |
| 0.1352        | 4.0   | 1500  | 0.3537          | 0.8883   |
| 0.0451        | 5.0   | 1875  | 0.4252          | 0.8733   |
| 0.0092        | 6.0   | 2250  | 0.5239          | 0.8683   |
| 0.0099        | 7.0   | 2625  | 0.6333          | 0.8617   |
| 0.0011        | 8.0   | 3000  | 0.6345          | 0.8783   |
| 0.0007        | 9.0   | 3375  | 0.6729          | 0.87     |
| 0.0003        | 10.0  | 3750  | 0.7305          | 0.87     |
| 0.0078        | 11.0  | 4125  | 0.8099          | 0.8533   |
| 0.0001        | 12.0  | 4500  | 0.8279          | 0.8633   |
| 0.0001        | 13.0  | 4875  | 0.8432          | 0.865    |
| 0.0241        | 14.0  | 5250  | 0.8671          | 0.8617   |
| 0.0001        | 15.0  | 5625  | 0.9017          | 0.8733   |
| 0.0           | 16.0  | 6000  | 0.9098          | 0.8683   |
| 0.0001        | 17.0  | 6375  | 0.9302          | 0.8683   |
| 0.021         | 18.0  | 6750  | 0.9362          | 0.865    |
| 0.0           | 19.0  | 7125  | 0.9767          | 0.8767   |
| 0.0           | 20.0  | 7500  | 0.9642          | 0.87     |
| 0.0           | 21.0  | 7875  | 0.9726          | 0.8683   |
| 0.0           | 22.0  | 8250  | 0.9924          | 0.87     |
| 0.0           | 23.0  | 8625  | 1.0407          | 0.8683   |
| 0.0           | 24.0  | 9000  | 0.9983          | 0.865    |
| 0.0           | 25.0  | 9375  | 1.0050          | 0.8767   |
| 0.0           | 26.0  | 9750  | 1.1029          | 0.8683   |
| 0.0           | 27.0  | 10125 | 1.0432          | 0.8633   |
| 0.0           | 28.0  | 10500 | 1.0623          | 0.87     |
| 0.0           | 29.0  | 10875 | 1.0507          | 0.8667   |
| 0.0           | 30.0  | 11250 | 1.0651          | 0.8717   |
| 0.0           | 31.0  | 11625 | 1.0597          | 0.8667   |
| 0.0           | 32.0  | 12000 | 1.0584          | 0.8633   |
| 0.0           | 33.0  | 12375 | 1.0660          | 0.8633   |
| 0.0           | 34.0  | 12750 | 1.0722          | 0.865    |
| 0.0           | 35.0  | 13125 | 1.0821          | 0.865    |
| 0.0           | 36.0  | 13500 | 1.0843          | 0.8617   |
| 0.0           | 37.0  | 13875 | 1.0957          | 0.8683   |
| 0.0089        | 38.0  | 14250 | 1.1135          | 0.8717   |
| 0.0           | 39.0  | 14625 | 1.1105          | 0.8683   |
| 0.0           | 40.0  | 15000 | 1.1158          | 0.8683   |
| 0.0           | 41.0  | 15375 | 1.1145          | 0.8667   |
| 0.0           | 42.0  | 15750 | 1.1172          | 0.8683   |
| 0.0           | 43.0  | 16125 | 1.1213          | 0.8683   |
| 0.0           | 44.0  | 16500 | 1.1274          | 0.8683   |
| 0.0           | 45.0  | 16875 | 1.1305          | 0.8683   |
| 0.0           | 46.0  | 17250 | 1.1329          | 0.8683   |
| 0.0           | 47.0  | 17625 | 1.1347          | 0.8683   |
| 0.0           | 48.0  | 18000 | 1.1363          | 0.8667   |
| 0.0           | 49.0  | 18375 | 1.1373          | 0.8667   |
| 0.0           | 50.0  | 18750 | 1.1380          | 0.8667   |


### 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