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# smids_5x_deit_small_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: 0.8104
- Accuracy: 0.925

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1649        | 1.0   | 750   | 0.2981          | 0.8767   |
| 0.1219        | 2.0   | 1500  | 0.3351          | 0.9117   |
| 0.088         | 3.0   | 2250  | 0.4113          | 0.9117   |
| 0.0191        | 4.0   | 3000  | 0.5041          | 0.91     |
| 0.0316        | 5.0   | 3750  | 0.6431          | 0.905    |
| 0.0519        | 6.0   | 4500  | 0.6485          | 0.9017   |
| 0.0145        | 7.0   | 5250  | 0.6712          | 0.91     |
| 0.001         | 8.0   | 6000  | 0.7180          | 0.91     |
| 0.0005        | 9.0   | 6750  | 0.6201          | 0.9067   |
| 0.0001        | 10.0  | 7500  | 0.7375          | 0.92     |
| 0.009         | 11.0  | 8250  | 0.7397          | 0.9133   |
| 0.0           | 12.0  | 9000  | 0.7531          | 0.9233   |
| 0.0005        | 13.0  | 9750  | 0.7094          | 0.9167   |
| 0.0           | 14.0  | 10500 | 0.6906          | 0.9217   |
| 0.0           | 15.0  | 11250 | 0.7622          | 0.92     |
| 0.0           | 16.0  | 12000 | 0.7690          | 0.9167   |
| 0.0099        | 17.0  | 12750 | 0.7093          | 0.925    |
| 0.0049        | 18.0  | 13500 | 0.7817          | 0.9167   |
| 0.0           | 19.0  | 14250 | 0.7714          | 0.9183   |
| 0.0           | 20.0  | 15000 | 0.7423          | 0.92     |
| 0.0           | 21.0  | 15750 | 0.7472          | 0.9283   |
| 0.0           | 22.0  | 16500 | 0.8201          | 0.9217   |
| 0.0003        | 23.0  | 17250 | 0.7230          | 0.925    |
| 0.0           | 24.0  | 18000 | 0.7873          | 0.9233   |
| 0.0           | 25.0  | 18750 | 0.7903          | 0.9233   |
| 0.0           | 26.0  | 19500 | 0.7611          | 0.9233   |
| 0.0           | 27.0  | 20250 | 0.7662          | 0.9267   |
| 0.0           | 28.0  | 21000 | 0.7601          | 0.9267   |
| 0.0           | 29.0  | 21750 | 0.7659          | 0.925    |
| 0.0054        | 30.0  | 22500 | 0.7697          | 0.9217   |
| 0.0           | 31.0  | 23250 | 0.7755          | 0.9217   |
| 0.0           | 32.0  | 24000 | 0.7712          | 0.9217   |
| 0.0           | 33.0  | 24750 | 0.7599          | 0.9267   |
| 0.0           | 34.0  | 25500 | 0.7735          | 0.9267   |
| 0.0           | 35.0  | 26250 | 0.7806          | 0.925    |
| 0.0           | 36.0  | 27000 | 0.7835          | 0.9217   |
| 0.0039        | 37.0  | 27750 | 0.7879          | 0.925    |
| 0.0           | 38.0  | 28500 | 0.7885          | 0.9267   |
| 0.0           | 39.0  | 29250 | 0.7918          | 0.925    |
| 0.0           | 40.0  | 30000 | 0.7945          | 0.9267   |
| 0.0           | 41.0  | 30750 | 0.7955          | 0.9267   |
| 0.0           | 42.0  | 31500 | 0.7991          | 0.9233   |
| 0.0           | 43.0  | 32250 | 0.8003          | 0.925    |
| 0.0           | 44.0  | 33000 | 0.8023          | 0.925    |
| 0.0           | 45.0  | 33750 | 0.8041          | 0.925    |
| 0.0           | 46.0  | 34500 | 0.8060          | 0.925    |
| 0.0           | 47.0  | 35250 | 0.8084          | 0.925    |
| 0.0           | 48.0  | 36000 | 0.8088          | 0.925    |
| 0.0           | 49.0  | 36750 | 0.8102          | 0.9267   |
| 0.0           | 50.0  | 37500 | 0.8104          | 0.925    |


### 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