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# smids_5x_deit_tiny_sgd_00001_fold1

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: 1.0635
- Accuracy: 0.4541

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.346         | 1.0   | 376   | 1.2991          | 0.3489   |
| 1.3817        | 2.0   | 752   | 1.2686          | 0.3589   |
| 1.3103        | 3.0   | 1128  | 1.2425          | 0.3656   |
| 1.3556        | 4.0   | 1504  | 1.2205          | 0.3656   |
| 1.2443        | 5.0   | 1880  | 1.2020          | 0.3723   |
| 1.1947        | 6.0   | 2256  | 1.1865          | 0.3806   |
| 1.184         | 7.0   | 2632  | 1.1737          | 0.3940   |
| 1.2121        | 8.0   | 3008  | 1.1630          | 0.3873   |
| 1.1793        | 9.0   | 3384  | 1.1540          | 0.3773   |
| 1.1564        | 10.0  | 3760  | 1.1464          | 0.3740   |
| 1.148         | 11.0  | 4136  | 1.1397          | 0.3756   |
| 1.1774        | 12.0  | 4512  | 1.1340          | 0.3756   |
| 1.1493        | 13.0  | 4888  | 1.1288          | 0.3790   |
| 1.1491        | 14.0  | 5264  | 1.1241          | 0.3790   |
| 1.1465        | 15.0  | 5640  | 1.1198          | 0.3856   |
| 1.1089        | 16.0  | 6016  | 1.1159          | 0.3990   |
| 1.1015        | 17.0  | 6392  | 1.1122          | 0.4057   |
| 1.1166        | 18.0  | 6768  | 1.1086          | 0.4073   |
| 1.1502        | 19.0  | 7144  | 1.1053          | 0.4124   |
| 1.124         | 20.0  | 7520  | 1.1022          | 0.4174   |
| 1.1102        | 21.0  | 7896  | 1.0992          | 0.4207   |
| 1.0904        | 22.0  | 8272  | 1.0964          | 0.4190   |
| 1.0897        | 23.0  | 8648  | 1.0937          | 0.4207   |
| 1.1449        | 24.0  | 9024  | 1.0912          | 0.4190   |
| 1.0609        | 25.0  | 9400  | 1.0888          | 0.4157   |
| 1.0747        | 26.0  | 9776  | 1.0865          | 0.4207   |
| 1.0631        | 27.0  | 10152 | 1.0844          | 0.4240   |
| 1.0872        | 28.0  | 10528 | 1.0823          | 0.4274   |
| 1.0811        | 29.0  | 10904 | 1.0804          | 0.4290   |
| 1.1082        | 30.0  | 11280 | 1.0786          | 0.4307   |
| 1.0863        | 31.0  | 11656 | 1.0769          | 0.4324   |
| 1.103         | 32.0  | 12032 | 1.0753          | 0.4290   |
| 1.0918        | 33.0  | 12408 | 1.0738          | 0.4324   |
| 1.06          | 34.0  | 12784 | 1.0725          | 0.4391   |
| 1.0723        | 35.0  | 13160 | 1.0712          | 0.4424   |
| 1.0366        | 36.0  | 13536 | 1.0701          | 0.4457   |
| 1.0655        | 37.0  | 13912 | 1.0690          | 0.4474   |
| 1.0787        | 38.0  | 14288 | 1.0681          | 0.4457   |
| 1.0751        | 39.0  | 14664 | 1.0672          | 0.4474   |
| 1.0508        | 40.0  | 15040 | 1.0665          | 0.4541   |
| 1.0565        | 41.0  | 15416 | 1.0658          | 0.4541   |
| 1.0404        | 42.0  | 15792 | 1.0652          | 0.4541   |
| 1.0767        | 43.0  | 16168 | 1.0648          | 0.4541   |
| 1.076         | 44.0  | 16544 | 1.0644          | 0.4541   |
| 1.0183        | 45.0  | 16920 | 1.0640          | 0.4541   |
| 1.0393        | 46.0  | 17296 | 1.0638          | 0.4541   |
| 1.065         | 47.0  | 17672 | 1.0636          | 0.4541   |
| 1.0432        | 48.0  | 18048 | 1.0635          | 0.4541   |
| 1.0432        | 49.0  | 18424 | 1.0635          | 0.4541   |
| 1.0255        | 50.0  | 18800 | 1.0635          | 0.4541   |


### 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