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# smids_5x_deit_base_adamax_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: 0.9417
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2147        | 1.0   | 375   | 0.4092          | 0.8317   |
| 0.1408        | 2.0   | 750   | 0.3269          | 0.915    |
| 0.0228        | 3.0   | 1125  | 0.4567          | 0.9067   |
| 0.0111        | 4.0   | 1500  | 0.5788          | 0.9033   |
| 0.0156        | 5.0   | 1875  | 0.6062          | 0.9      |
| 0.0288        | 6.0   | 2250  | 0.6656          | 0.8917   |
| 0.0007        | 7.0   | 2625  | 0.6456          | 0.9017   |
| 0.0002        | 8.0   | 3000  | 0.6407          | 0.8917   |
| 0.0002        | 9.0   | 3375  | 0.6824          | 0.9083   |
| 0.0084        | 10.0  | 3750  | 0.6593          | 0.905    |
| 0.0203        | 11.0  | 4125  | 0.7617          | 0.9017   |
| 0.0033        | 12.0  | 4500  | 0.7022          | 0.9167   |
| 0.0           | 13.0  | 4875  | 0.8023          | 0.9033   |
| 0.0           | 14.0  | 5250  | 0.8062          | 0.9083   |
| 0.0           | 15.0  | 5625  | 0.8735          | 0.905    |
| 0.0293        | 16.0  | 6000  | 0.8124          | 0.9133   |
| 0.0           | 17.0  | 6375  | 0.8110          | 0.915    |
| 0.0           | 18.0  | 6750  | 0.7934          | 0.9167   |
| 0.0           | 19.0  | 7125  | 0.8257          | 0.9117   |
| 0.0           | 20.0  | 7500  | 0.8169          | 0.905    |
| 0.0           | 21.0  | 7875  | 0.7971          | 0.9167   |
| 0.0           | 22.0  | 8250  | 0.8206          | 0.905    |
| 0.0031        | 23.0  | 8625  | 0.8887          | 0.9067   |
| 0.0           | 24.0  | 9000  | 0.8570          | 0.91     |
| 0.0           | 25.0  | 9375  | 0.9027          | 0.9017   |
| 0.0           | 26.0  | 9750  | 0.8809          | 0.9067   |
| 0.0           | 27.0  | 10125 | 0.8772          | 0.9083   |
| 0.0           | 28.0  | 10500 | 0.8815          | 0.9083   |
| 0.0           | 29.0  | 10875 | 0.8462          | 0.91     |
| 0.0028        | 30.0  | 11250 | 0.8854          | 0.9083   |
| 0.0           | 31.0  | 11625 | 0.8584          | 0.9083   |
| 0.0           | 32.0  | 12000 | 0.8933          | 0.905    |
| 0.0           | 33.0  | 12375 | 0.8718          | 0.9083   |
| 0.0           | 34.0  | 12750 | 0.8798          | 0.9067   |
| 0.0           | 35.0  | 13125 | 0.8653          | 0.9083   |
| 0.0           | 36.0  | 13500 | 0.8742          | 0.9133   |
| 0.0           | 37.0  | 13875 | 0.8914          | 0.9083   |
| 0.0           | 38.0  | 14250 | 0.8921          | 0.91     |
| 0.0           | 39.0  | 14625 | 0.9001          | 0.9083   |
| 0.0025        | 40.0  | 15000 | 0.9101          | 0.9083   |
| 0.0           | 41.0  | 15375 | 0.9161          | 0.9067   |
| 0.0           | 42.0  | 15750 | 0.9182          | 0.9083   |
| 0.0           | 43.0  | 16125 | 0.9246          | 0.905    |
| 0.0           | 44.0  | 16500 | 0.9291          | 0.9083   |
| 0.0           | 45.0  | 16875 | 0.9302          | 0.9067   |
| 0.0           | 46.0  | 17250 | 0.9341          | 0.9067   |
| 0.0           | 47.0  | 17625 | 0.9378          | 0.9067   |
| 0.0           | 48.0  | 18000 | 0.9402          | 0.9067   |
| 0.0           | 49.0  | 18375 | 0.9417          | 0.9067   |
| 0.0           | 50.0  | 18750 | 0.9417          | 0.9067   |


### 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