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# smids_10x_deit_base_sgd_0001_fold1

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4637
- Accuracy: 0.8197

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0699        | 1.0   | 751   | 1.0865          | 0.3372   |
| 1.0294        | 2.0   | 1502  | 1.0518          | 0.4207   |
| 0.9671        | 3.0   | 2253  | 1.0151          | 0.4992   |
| 0.8907        | 4.0   | 3004  | 0.9742          | 0.5526   |
| 0.8635        | 5.0   | 3755  | 0.9306          | 0.6043   |
| 0.8115        | 6.0   | 4506  | 0.8888          | 0.6461   |
| 0.7615        | 7.0   | 5257  | 0.8493          | 0.6694   |
| 0.7205        | 8.0   | 6008  | 0.8123          | 0.6861   |
| 0.6698        | 9.0   | 6759  | 0.7780          | 0.7078   |
| 0.6144        | 10.0  | 7510  | 0.7466          | 0.7195   |
| 0.6353        | 11.0  | 8261  | 0.7180          | 0.7295   |
| 0.5516        | 12.0  | 9012  | 0.6921          | 0.7396   |
| 0.5555        | 13.0  | 9763  | 0.6685          | 0.7446   |
| 0.5272        | 14.0  | 10514 | 0.6476          | 0.7546   |
| 0.5021        | 15.0  | 11265 | 0.6291          | 0.7596   |
| 0.4905        | 16.0  | 12016 | 0.6125          | 0.7713   |
| 0.4771        | 17.0  | 12767 | 0.5975          | 0.7696   |
| 0.4486        | 18.0  | 13518 | 0.5841          | 0.7730   |
| 0.4932        | 19.0  | 14269 | 0.5723          | 0.7713   |
| 0.4372        | 20.0  | 15020 | 0.5615          | 0.7746   |
| 0.3961        | 21.0  | 15771 | 0.5521          | 0.7780   |
| 0.455         | 22.0  | 16522 | 0.5434          | 0.7796   |
| 0.4028        | 23.0  | 17273 | 0.5355          | 0.7896   |
| 0.4361        | 24.0  | 18024 | 0.5285          | 0.7913   |
| 0.4568        | 25.0  | 18775 | 0.5220          | 0.7930   |
| 0.5129        | 26.0  | 19526 | 0.5160          | 0.7947   |
| 0.4225        | 27.0  | 20277 | 0.5106          | 0.7997   |
| 0.4409        | 28.0  | 21028 | 0.5056          | 0.7997   |
| 0.4065        | 29.0  | 21779 | 0.5010          | 0.7997   |
| 0.4201        | 30.0  | 22530 | 0.4969          | 0.8013   |
| 0.3612        | 31.0  | 23281 | 0.4930          | 0.8047   |
| 0.5003        | 32.0  | 24032 | 0.4895          | 0.8063   |
| 0.4064        | 33.0  | 24783 | 0.4864          | 0.8080   |
| 0.4           | 34.0  | 25534 | 0.4834          | 0.8097   |
| 0.4159        | 35.0  | 26285 | 0.4807          | 0.8097   |
| 0.4048        | 36.0  | 27036 | 0.4783          | 0.8130   |
| 0.4636        | 37.0  | 27787 | 0.4761          | 0.8147   |
| 0.3893        | 38.0  | 28538 | 0.4740          | 0.8147   |
| 0.3469        | 39.0  | 29289 | 0.4723          | 0.8147   |
| 0.4394        | 40.0  | 30040 | 0.4706          | 0.8164   |
| 0.4132        | 41.0  | 30791 | 0.4692          | 0.8180   |
| 0.3775        | 42.0  | 31542 | 0.4680          | 0.8197   |
| 0.3957        | 43.0  | 32293 | 0.4669          | 0.8197   |
| 0.4064        | 44.0  | 33044 | 0.4660          | 0.8197   |
| 0.4124        | 45.0  | 33795 | 0.4652          | 0.8197   |
| 0.3879        | 46.0  | 34546 | 0.4646          | 0.8197   |
| 0.3807        | 47.0  | 35297 | 0.4642          | 0.8197   |
| 0.4327        | 48.0  | 36048 | 0.4639          | 0.8197   |
| 0.3727        | 49.0  | 36799 | 0.4638          | 0.8197   |
| 0.3716        | 50.0  | 37550 | 0.4637          | 0.8197   |


### 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