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# smids_5x_deit_small_sgd_00001_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: 0.9877
- Accuracy: 0.5167

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0889        | 1.0   | 375   | 1.0662          | 0.4217   |
| 1.0663        | 2.0   | 750   | 1.0632          | 0.4233   |
| 1.047         | 3.0   | 1125  | 1.0602          | 0.43     |
| 1.0589        | 4.0   | 1500  | 1.0573          | 0.43     |
| 1.0463        | 5.0   | 1875  | 1.0544          | 0.4317   |
| 1.0388        | 6.0   | 2250  | 1.0516          | 0.4333   |
| 1.0109        | 7.0   | 2625  | 1.0487          | 0.435    |
| 1.0394        | 8.0   | 3000  | 1.0459          | 0.4383   |
| 1.0347        | 9.0   | 3375  | 1.0431          | 0.4417   |
| 1.0355        | 10.0  | 3750  | 1.0404          | 0.4467   |
| 1.059         | 11.0  | 4125  | 1.0377          | 0.4467   |
| 1.0235        | 12.0  | 4500  | 1.0352          | 0.445    |
| 1.0136        | 13.0  | 4875  | 1.0326          | 0.4467   |
| 1.0313        | 14.0  | 5250  | 1.0301          | 0.45     |
| 1.0046        | 15.0  | 5625  | 1.0277          | 0.4567   |
| 1.0138        | 16.0  | 6000  | 1.0253          | 0.4633   |
| 1.0055        | 17.0  | 6375  | 1.0230          | 0.465    |
| 0.998         | 18.0  | 6750  | 1.0207          | 0.4667   |
| 1.0178        | 19.0  | 7125  | 1.0186          | 0.4667   |
| 1.019         | 20.0  | 7500  | 1.0165          | 0.4717   |
| 0.9884        | 21.0  | 7875  | 1.0145          | 0.4783   |
| 1.0226        | 22.0  | 8250  | 1.0125          | 0.48     |
| 1.0239        | 23.0  | 8625  | 1.0106          | 0.4833   |
| 1.0151        | 24.0  | 9000  | 1.0088          | 0.49     |
| 0.997         | 25.0  | 9375  | 1.0071          | 0.49     |
| 0.9698        | 26.0  | 9750  | 1.0054          | 0.4917   |
| 0.958         | 27.0  | 10125 | 1.0038          | 0.495    |
| 1.0132        | 28.0  | 10500 | 1.0023          | 0.4933   |
| 0.9673        | 29.0  | 10875 | 1.0008          | 0.4983   |
| 0.9986        | 30.0  | 11250 | 0.9995          | 0.5      |
| 0.9881        | 31.0  | 11625 | 0.9982          | 0.505    |
| 1.0083        | 32.0  | 12000 | 0.9970          | 0.505    |
| 0.9851        | 33.0  | 12375 | 0.9959          | 0.5067   |
| 0.9949        | 34.0  | 12750 | 0.9949          | 0.5067   |
| 0.988         | 35.0  | 13125 | 0.9939          | 0.5083   |
| 1.0062        | 36.0  | 13500 | 0.9930          | 0.51     |
| 0.9899        | 37.0  | 13875 | 0.9922          | 0.5083   |
| 0.9951        | 38.0  | 14250 | 0.9914          | 0.51     |
| 1.0002        | 39.0  | 14625 | 0.9908          | 0.5133   |
| 0.9573        | 40.0  | 15000 | 0.9902          | 0.5133   |
| 0.9723        | 41.0  | 15375 | 0.9896          | 0.515    |
| 0.977         | 42.0  | 15750 | 0.9892          | 0.515    |
| 0.9762        | 43.0  | 16125 | 0.9888          | 0.515    |
| 0.9976        | 44.0  | 16500 | 0.9885          | 0.5167   |
| 0.965         | 45.0  | 16875 | 0.9882          | 0.5167   |
| 0.9904        | 46.0  | 17250 | 0.9880          | 0.5167   |
| 0.9962        | 47.0  | 17625 | 0.9879          | 0.5167   |
| 0.982         | 48.0  | 18000 | 0.9878          | 0.5167   |
| 0.9851        | 49.0  | 18375 | 0.9877          | 0.5167   |
| 0.9675        | 50.0  | 18750 | 0.9877          | 0.5167   |


### 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