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# smids_10x_deit_tiny_sgd_00001_fold2

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9487
- Accuracy: 0.5075

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3101        | 1.0   | 750   | 1.2862          | 0.3394   |
| 1.2651        | 2.0   | 1500  | 1.2316          | 0.3494   |
| 1.2534        | 3.0   | 2250  | 1.1915          | 0.3461   |
| 1.2051        | 4.0   | 3000  | 1.1627          | 0.3627   |
| 1.1704        | 5.0   | 3750  | 1.1422          | 0.3644   |
| 1.1559        | 6.0   | 4500  | 1.1266          | 0.3727   |
| 1.1314        | 7.0   | 5250  | 1.1140          | 0.3827   |
| 1.0917        | 8.0   | 6000  | 1.1032          | 0.3844   |
| 1.0985        | 9.0   | 6750  | 1.0936          | 0.4193   |
| 1.0734        | 10.0  | 7500  | 1.0847          | 0.4243   |
| 1.0397        | 11.0  | 8250  | 1.0764          | 0.4293   |
| 1.0584        | 12.0  | 9000  | 1.0685          | 0.4409   |
| 1.0692        | 13.0  | 9750  | 1.0611          | 0.4426   |
| 1.0127        | 14.0  | 10500 | 1.0540          | 0.4542   |
| 1.0605        | 15.0  | 11250 | 1.0471          | 0.4542   |
| 1.0197        | 16.0  | 12000 | 1.0406          | 0.4626   |
| 1.0472        | 17.0  | 12750 | 1.0344          | 0.4659   |
| 0.9868        | 18.0  | 13500 | 1.0285          | 0.4709   |
| 1.0498        | 19.0  | 14250 | 1.0228          | 0.4725   |
| 0.9916        | 20.0  | 15000 | 1.0174          | 0.4742   |
| 1.0032        | 21.0  | 15750 | 1.0122          | 0.4792   |
| 1.0262        | 22.0  | 16500 | 1.0073          | 0.4792   |
| 0.9732        | 23.0  | 17250 | 1.0026          | 0.4792   |
| 0.9627        | 24.0  | 18000 | 0.9981          | 0.4875   |
| 0.9933        | 25.0  | 18750 | 0.9939          | 0.4892   |
| 0.9645        | 26.0  | 19500 | 0.9898          | 0.4942   |
| 0.9413        | 27.0  | 20250 | 0.9860          | 0.4942   |
| 0.9502        | 28.0  | 21000 | 0.9824          | 0.4925   |
| 0.9622        | 29.0  | 21750 | 0.9790          | 0.4958   |
| 0.9399        | 30.0  | 22500 | 0.9758          | 0.4975   |
| 0.9259        | 31.0  | 23250 | 0.9728          | 0.4992   |
| 0.9425        | 32.0  | 24000 | 0.9700          | 0.5008   |
| 0.9657        | 33.0  | 24750 | 0.9673          | 0.5042   |
| 0.9537        | 34.0  | 25500 | 0.9649          | 0.5058   |
| 0.9361        | 35.0  | 26250 | 0.9627          | 0.5075   |
| 0.934         | 36.0  | 27000 | 0.9606          | 0.5092   |
| 0.927         | 37.0  | 27750 | 0.9587          | 0.5092   |
| 0.9435        | 38.0  | 28500 | 0.9570          | 0.5092   |
| 0.9139        | 39.0  | 29250 | 0.9555          | 0.5092   |
| 0.9394        | 40.0  | 30000 | 0.9541          | 0.5075   |
| 0.9635        | 41.0  | 30750 | 0.9529          | 0.5075   |
| 0.9447        | 42.0  | 31500 | 0.9519          | 0.5075   |
| 0.9124        | 43.0  | 32250 | 0.9510          | 0.5075   |
| 0.9404        | 44.0  | 33000 | 0.9503          | 0.5075   |
| 0.9374        | 45.0  | 33750 | 0.9497          | 0.5075   |
| 0.9103        | 46.0  | 34500 | 0.9493          | 0.5075   |
| 0.9609        | 47.0  | 35250 | 0.9490          | 0.5075   |
| 0.9309        | 48.0  | 36000 | 0.9488          | 0.5075   |
| 0.9307        | 49.0  | 36750 | 0.9487          | 0.5075   |
| 0.9119        | 50.0  | 37500 | 0.9487          | 0.5075   |


### 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