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# smids_3x_deit_small_sgd_00001_fold1

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: 1.0200
- Accuracy: 0.4925

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0919        | 1.0   | 226   | 1.0743          | 0.4407   |
| 1.0742        | 2.0   | 452   | 1.0717          | 0.4391   |
| 1.0524        | 3.0   | 678   | 1.0693          | 0.4357   |
| 1.0603        | 4.0   | 904   | 1.0670          | 0.4391   |
| 1.0627        | 5.0   | 1130  | 1.0647          | 0.4391   |
| 1.0778        | 6.0   | 1356  | 1.0625          | 0.4457   |
| 1.0591        | 7.0   | 1582  | 1.0603          | 0.4441   |
| 1.0519        | 8.0   | 1808  | 1.0582          | 0.4457   |
| 1.0538        | 9.0   | 2034  | 1.0562          | 0.4474   |
| 1.0527        | 10.0  | 2260  | 1.0542          | 0.4491   |
| 1.057         | 11.0  | 2486  | 1.0523          | 0.4491   |
| 1.0703        | 12.0  | 2712  | 1.0505          | 0.4491   |
| 1.0101        | 13.0  | 2938  | 1.0488          | 0.4474   |
| 1.0585        | 14.0  | 3164  | 1.0471          | 0.4491   |
| 1.0541        | 15.0  | 3390  | 1.0455          | 0.4541   |
| 1.0416        | 16.0  | 3616  | 1.0438          | 0.4591   |
| 1.03          | 17.0  | 3842  | 1.0423          | 0.4608   |
| 1.0278        | 18.0  | 4068  | 1.0408          | 0.4624   |
| 1.0509        | 19.0  | 4294  | 1.0394          | 0.4641   |
| 1.0188        | 20.0  | 4520  | 1.0381          | 0.4674   |
| 1.0344        | 21.0  | 4746  | 1.0367          | 0.4674   |
| 1.0343        | 22.0  | 4972  | 1.0355          | 0.4691   |
| 1.0324        | 23.0  | 5198  | 1.0343          | 0.4691   |
| 1.0367        | 24.0  | 5424  | 1.0331          | 0.4725   |
| 0.9995        | 25.0  | 5650  | 1.0320          | 0.4725   |
| 1.0409        | 26.0  | 5876  | 1.0310          | 0.4725   |
| 1.0145        | 27.0  | 6102  | 1.0300          | 0.4758   |
| 1.0333        | 28.0  | 6328  | 1.0290          | 0.4758   |
| 1.0283        | 29.0  | 6554  | 1.0281          | 0.4791   |
| 1.0286        | 30.0  | 6780  | 1.0273          | 0.4808   |
| 1.0197        | 31.0  | 7006  | 1.0265          | 0.4808   |
| 1.0259        | 32.0  | 7232  | 1.0257          | 0.4791   |
| 1.0188        | 33.0  | 7458  | 1.0250          | 0.4808   |
| 1.0201        | 34.0  | 7684  | 1.0244          | 0.4808   |
| 1.0339        | 35.0  | 7910  | 1.0238          | 0.4825   |
| 1.0181        | 36.0  | 8136  | 1.0232          | 0.4875   |
| 1.0181        | 37.0  | 8362  | 1.0227          | 0.4875   |
| 1.0115        | 38.0  | 8588  | 1.0223          | 0.4875   |
| 0.9878        | 39.0  | 8814  | 1.0218          | 0.4891   |
| 1.0096        | 40.0  | 9040  | 1.0215          | 0.4891   |
| 1.024         | 41.0  | 9266  | 1.0212          | 0.4891   |
| 1.0376        | 42.0  | 9492  | 1.0209          | 0.4908   |
| 1.0233        | 43.0  | 9718  | 1.0206          | 0.4925   |
| 1.0195        | 44.0  | 9944  | 1.0204          | 0.4925   |
| 1.0135        | 45.0  | 10170 | 1.0203          | 0.4925   |
| 1.039         | 46.0  | 10396 | 1.0202          | 0.4925   |
| 1.0136        | 47.0  | 10622 | 1.0201          | 0.4925   |
| 1.0247        | 48.0  | 10848 | 1.0200          | 0.4925   |
| 1.0178        | 49.0  | 11074 | 1.0200          | 0.4925   |
| 1.0351        | 50.0  | 11300 | 1.0200          | 0.4925   |


### 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