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# smids_5x_deit_tiny_adamax_0001_fold5

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: 0.9117
- Accuracy: 0.9067

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1964        | 1.0   | 375   | 0.3187          | 0.86     |
| 0.1451        | 2.0   | 750   | 0.2705          | 0.905    |
| 0.0998        | 3.0   | 1125  | 0.3251          | 0.905    |
| 0.0219        | 4.0   | 1500  | 0.4834          | 0.89     |
| 0.034         | 5.0   | 1875  | 0.5396          | 0.8967   |
| 0.0222        | 6.0   | 2250  | 0.4814          | 0.9133   |
| 0.0006        | 7.0   | 2625  | 0.6569          | 0.9133   |
| 0.013         | 8.0   | 3000  | 0.5398          | 0.91     |
| 0.001         | 9.0   | 3375  | 0.6798          | 0.9117   |
| 0.0003        | 10.0  | 3750  | 0.7290          | 0.9083   |
| 0.0012        | 11.0  | 4125  | 0.7598          | 0.9067   |
| 0.0           | 12.0  | 4500  | 0.6815          | 0.9167   |
| 0.0001        | 13.0  | 4875  | 0.7765          | 0.895    |
| 0.0004        | 14.0  | 5250  | 0.6391          | 0.9117   |
| 0.0001        | 15.0  | 5625  | 0.8172          | 0.9067   |
| 0.003         | 16.0  | 6000  | 0.6833          | 0.9083   |
| 0.0           | 17.0  | 6375  | 0.7002          | 0.9133   |
| 0.0103        | 18.0  | 6750  | 0.7679          | 0.91     |
| 0.0           | 19.0  | 7125  | 0.8144          | 0.905    |
| 0.0           | 20.0  | 7500  | 0.8111          | 0.905    |
| 0.0           | 21.0  | 7875  | 0.8619          | 0.9      |
| 0.0063        | 22.0  | 8250  | 0.7672          | 0.9117   |
| 0.0001        | 23.0  | 8625  | 0.8118          | 0.905    |
| 0.0           | 24.0  | 9000  | 0.7951          | 0.9133   |
| 0.0           | 25.0  | 9375  | 0.8220          | 0.9033   |
| 0.0           | 26.0  | 9750  | 0.7685          | 0.915    |
| 0.0           | 27.0  | 10125 | 0.8895          | 0.9      |
| 0.0           | 28.0  | 10500 | 0.7796          | 0.9167   |
| 0.0           | 29.0  | 10875 | 0.8774          | 0.9083   |
| 0.0031        | 30.0  | 11250 | 0.8526          | 0.905    |
| 0.0           | 31.0  | 11625 | 0.9000          | 0.9      |
| 0.0           | 32.0  | 12000 | 0.8541          | 0.9067   |
| 0.0           | 33.0  | 12375 | 0.8916          | 0.905    |
| 0.0           | 34.0  | 12750 | 0.8844          | 0.905    |
| 0.0           | 35.0  | 13125 | 0.8624          | 0.9067   |
| 0.0           | 36.0  | 13500 | 0.8971          | 0.9017   |
| 0.0           | 37.0  | 13875 | 0.8894          | 0.9083   |
| 0.003         | 38.0  | 14250 | 0.8778          | 0.905    |
| 0.0           | 39.0  | 14625 | 0.8771          | 0.9083   |
| 0.0           | 40.0  | 15000 | 0.8911          | 0.905    |
| 0.0           | 41.0  | 15375 | 0.8755          | 0.91     |
| 0.0           | 42.0  | 15750 | 0.8923          | 0.905    |
| 0.0           | 43.0  | 16125 | 0.8874          | 0.9067   |
| 0.0           | 44.0  | 16500 | 0.8938          | 0.9067   |
| 0.0           | 45.0  | 16875 | 0.8983          | 0.9067   |
| 0.0           | 46.0  | 17250 | 0.9065          | 0.9067   |
| 0.0027        | 47.0  | 17625 | 0.9068          | 0.9067   |
| 0.0           | 48.0  | 18000 | 0.9127          | 0.905    |
| 0.0           | 49.0  | 18375 | 0.9142          | 0.905    |
| 0.0023        | 50.0  | 18750 | 0.9117          | 0.9067   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm