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# smids_10x_deit_small_rms_0001_fold4

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: 1.4626
- Accuracy: 0.885

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2601        | 1.0   | 750   | 0.3716          | 0.8583   |
| 0.1216        | 2.0   | 1500  | 0.6123          | 0.855    |
| 0.1115        | 3.0   | 2250  | 0.5264          | 0.8667   |
| 0.066         | 4.0   | 3000  | 0.5528          | 0.8733   |
| 0.0447        | 5.0   | 3750  | 0.7645          | 0.8633   |
| 0.0487        | 6.0   | 4500  | 0.8232          | 0.8767   |
| 0.0554        | 7.0   | 5250  | 0.7277          | 0.865    |
| 0.0304        | 8.0   | 6000  | 0.8549          | 0.8667   |
| 0.0413        | 9.0   | 6750  | 0.8526          | 0.865    |
| 0.0142        | 10.0  | 7500  | 1.0441          | 0.8567   |
| 0.0561        | 11.0  | 8250  | 1.0164          | 0.8633   |
| 0.0352        | 12.0  | 9000  | 0.8537          | 0.875    |
| 0.0141        | 13.0  | 9750  | 0.9173          | 0.8617   |
| 0.0343        | 14.0  | 10500 | 1.0250          | 0.86     |
| 0.0048        | 15.0  | 11250 | 0.9231          | 0.8617   |
| 0.0038        | 16.0  | 12000 | 1.1476          | 0.8617   |
| 0.0308        | 17.0  | 12750 | 0.9914          | 0.885    |
| 0.0168        | 18.0  | 13500 | 1.0050          | 0.8783   |
| 0.0001        | 19.0  | 14250 | 1.0610          | 0.8667   |
| 0.0           | 20.0  | 15000 | 1.0251          | 0.86     |
| 0.0232        | 21.0  | 15750 | 1.1692          | 0.855    |
| 0.0026        | 22.0  | 16500 | 0.9562          | 0.8833   |
| 0.0001        | 23.0  | 17250 | 1.0914          | 0.8733   |
| 0.0042        | 24.0  | 18000 | 1.0684          | 0.8767   |
| 0.0           | 25.0  | 18750 | 0.9724          | 0.8833   |
| 0.0001        | 26.0  | 19500 | 1.0636          | 0.86     |
| 0.0001        | 27.0  | 20250 | 1.1239          | 0.86     |
| 0.0015        | 28.0  | 21000 | 1.1692          | 0.8683   |
| 0.0308        | 29.0  | 21750 | 1.1241          | 0.875    |
| 0.0263        | 30.0  | 22500 | 1.0816          | 0.8867   |
| 0.0           | 31.0  | 23250 | 0.9644          | 0.8867   |
| 0.0           | 32.0  | 24000 | 1.1653          | 0.875    |
| 0.0           | 33.0  | 24750 | 1.2370          | 0.8833   |
| 0.0117        | 34.0  | 25500 | 1.3585          | 0.8767   |
| 0.0014        | 35.0  | 26250 | 1.1826          | 0.885    |
| 0.0           | 36.0  | 27000 | 1.2030          | 0.8867   |
| 0.0           | 37.0  | 27750 | 1.4012          | 0.8717   |
| 0.0           | 38.0  | 28500 | 1.3242          | 0.8717   |
| 0.0           | 39.0  | 29250 | 1.2640          | 0.8833   |
| 0.0           | 40.0  | 30000 | 1.3613          | 0.8817   |
| 0.0           | 41.0  | 30750 | 1.3467          | 0.8883   |
| 0.0           | 42.0  | 31500 | 1.3505          | 0.8917   |
| 0.0           | 43.0  | 32250 | 1.3990          | 0.8867   |
| 0.0           | 44.0  | 33000 | 1.4081          | 0.8917   |
| 0.0           | 45.0  | 33750 | 1.4275          | 0.8883   |
| 0.0           | 46.0  | 34500 | 1.4373          | 0.8867   |
| 0.0           | 47.0  | 35250 | 1.4471          | 0.8867   |
| 0.0           | 48.0  | 36000 | 1.4562          | 0.885    |
| 0.0           | 49.0  | 36750 | 1.4611          | 0.885    |
| 0.0           | 50.0  | 37500 | 1.4626          | 0.885    |


### 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