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# smids_5x_deit_tiny_rms_001_fold3

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: 0.8753
- Accuracy: 0.7767

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7906        | 1.0   | 375   | 0.9128          | 0.4983   |
| 0.7765        | 2.0   | 750   | 0.9232          | 0.4617   |
| 0.7977        | 3.0   | 1125  | 0.8743          | 0.5267   |
| 0.8093        | 4.0   | 1500  | 0.7926          | 0.5767   |
| 0.8508        | 5.0   | 1875  | 0.7894          | 0.5733   |
| 0.7532        | 6.0   | 2250  | 0.7991          | 0.6117   |
| 0.7584        | 7.0   | 2625  | 0.7566          | 0.625    |
| 0.7398        | 8.0   | 3000  | 0.7364          | 0.6083   |
| 0.7009        | 9.0   | 3375  | 0.7452          | 0.64     |
| 0.7014        | 10.0  | 3750  | 0.7192          | 0.6433   |
| 0.7226        | 11.0  | 4125  | 0.7119          | 0.6383   |
| 0.7293        | 12.0  | 4500  | 0.7180          | 0.6467   |
| 0.6344        | 13.0  | 4875  | 0.7612          | 0.6117   |
| 0.6251        | 14.0  | 5250  | 0.7810          | 0.66     |
| 0.6301        | 15.0  | 5625  | 0.6950          | 0.6733   |
| 0.6252        | 16.0  | 6000  | 0.7106          | 0.6767   |
| 0.688         | 17.0  | 6375  | 0.7082          | 0.6883   |
| 0.7261        | 18.0  | 6750  | 0.6859          | 0.6883   |
| 0.5633        | 19.0  | 7125  | 0.6734          | 0.7033   |
| 0.6092        | 20.0  | 7500  | 0.6580          | 0.7283   |
| 0.4728        | 21.0  | 7875  | 0.6793          | 0.7033   |
| 0.5681        | 22.0  | 8250  | 0.6598          | 0.7217   |
| 0.5951        | 23.0  | 8625  | 0.6134          | 0.7533   |
| 0.6592        | 24.0  | 9000  | 0.5954          | 0.7467   |
| 0.5215        | 25.0  | 9375  | 0.5847          | 0.74     |
| 0.5272        | 26.0  | 9750  | 0.6243          | 0.7017   |
| 0.5866        | 27.0  | 10125 | 0.6339          | 0.7233   |
| 0.5766        | 28.0  | 10500 | 0.5466          | 0.765    |
| 0.463         | 29.0  | 10875 | 0.5734          | 0.7583   |
| 0.5041        | 30.0  | 11250 | 0.5320          | 0.775    |
| 0.5133        | 31.0  | 11625 | 0.5507          | 0.7683   |
| 0.5402        | 32.0  | 12000 | 0.5711          | 0.7517   |
| 0.4526        | 33.0  | 12375 | 0.5736          | 0.7483   |
| 0.4724        | 34.0  | 12750 | 0.5009          | 0.79     |
| 0.3951        | 35.0  | 13125 | 0.5483          | 0.77     |
| 0.3876        | 36.0  | 13500 | 0.5689          | 0.755    |
| 0.3627        | 37.0  | 13875 | 0.5639          | 0.7733   |
| 0.4378        | 38.0  | 14250 | 0.5663          | 0.765    |
| 0.3725        | 39.0  | 14625 | 0.5574          | 0.7867   |
| 0.3444        | 40.0  | 15000 | 0.5740          | 0.7733   |
| 0.3158        | 41.0  | 15375 | 0.5671          | 0.7717   |
| 0.29          | 42.0  | 15750 | 0.6455          | 0.78     |
| 0.3784        | 43.0  | 16125 | 0.6093          | 0.785    |
| 0.318         | 44.0  | 16500 | 0.6835          | 0.7683   |
| 0.2949        | 45.0  | 16875 | 0.7092          | 0.7733   |
| 0.2996        | 46.0  | 17250 | 0.6699          | 0.7767   |
| 0.2938        | 47.0  | 17625 | 0.7545          | 0.7917   |
| 0.2248        | 48.0  | 18000 | 0.8050          | 0.775    |
| 0.2309        | 49.0  | 18375 | 0.8518          | 0.7767   |
| 0.1878        | 50.0  | 18750 | 0.8753          | 0.7767   |


### 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