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# smids_5x_deit_base_sgd_00001_fold2

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: 1.0641
- Accuracy: 0.4459

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1035        | 1.0   | 375   | 1.1062          | 0.3344   |
| 1.1126        | 2.0   | 750   | 1.1043          | 0.3344   |
| 1.104         | 3.0   | 1125  | 1.1024          | 0.3344   |
| 1.1172        | 4.0   | 1500  | 1.1007          | 0.3428   |
| 1.1218        | 5.0   | 1875  | 1.0990          | 0.3494   |
| 1.103         | 6.0   | 2250  | 1.0973          | 0.3544   |
| 1.0899        | 7.0   | 2625  | 1.0957          | 0.3594   |
| 1.1072        | 8.0   | 3000  | 1.0942          | 0.3661   |
| 1.0922        | 9.0   | 3375  | 1.0926          | 0.3744   |
| 1.0843        | 10.0  | 3750  | 1.0912          | 0.3727   |
| 1.081         | 11.0  | 4125  | 1.0898          | 0.3710   |
| 1.0891        | 12.0  | 4500  | 1.0884          | 0.3760   |
| 1.0709        | 13.0  | 4875  | 1.0871          | 0.3777   |
| 1.0708        | 14.0  | 5250  | 1.0858          | 0.3827   |
| 1.0647        | 15.0  | 5625  | 1.0846          | 0.3827   |
| 1.0675        | 16.0  | 6000  | 1.0834          | 0.3877   |
| 1.0777        | 17.0  | 6375  | 1.0822          | 0.3927   |
| 1.1021        | 18.0  | 6750  | 1.0811          | 0.3943   |
| 1.075         | 19.0  | 7125  | 1.0800          | 0.3993   |
| 1.08          | 20.0  | 7500  | 1.0789          | 0.3977   |
| 1.0665        | 21.0  | 7875  | 1.0779          | 0.4010   |
| 1.0636        | 22.0  | 8250  | 1.0769          | 0.4010   |
| 1.0724        | 23.0  | 8625  | 1.0760          | 0.4043   |
| 1.075         | 24.0  | 9000  | 1.0751          | 0.4093   |
| 1.0668        | 25.0  | 9375  | 1.0742          | 0.4077   |
| 1.0648        | 26.0  | 9750  | 1.0734          | 0.4160   |
| 1.0792        | 27.0  | 10125 | 1.0726          | 0.4176   |
| 1.068         | 28.0  | 10500 | 1.0718          | 0.4160   |
| 1.0536        | 29.0  | 10875 | 1.0711          | 0.4160   |
| 1.0571        | 30.0  | 11250 | 1.0704          | 0.4193   |
| 1.055         | 31.0  | 11625 | 1.0698          | 0.4226   |
| 1.0604        | 32.0  | 12000 | 1.0691          | 0.4226   |
| 1.0502        | 33.0  | 12375 | 1.0686          | 0.4260   |
| 1.0518        | 34.0  | 12750 | 1.0680          | 0.4243   |
| 1.0472        | 35.0  | 13125 | 1.0675          | 0.4276   |
| 1.0642        | 36.0  | 13500 | 1.0670          | 0.4309   |
| 1.052         | 37.0  | 13875 | 1.0666          | 0.4309   |
| 1.0617        | 38.0  | 14250 | 1.0662          | 0.4309   |
| 1.0473        | 39.0  | 14625 | 1.0658          | 0.4359   |
| 1.0678        | 40.0  | 15000 | 1.0655          | 0.4393   |
| 1.0397        | 41.0  | 15375 | 1.0652          | 0.4393   |
| 1.0482        | 42.0  | 15750 | 1.0650          | 0.4393   |
| 1.0333        | 43.0  | 16125 | 1.0647          | 0.4393   |
| 1.0512        | 44.0  | 16500 | 1.0645          | 0.4409   |
| 1.0593        | 45.0  | 16875 | 1.0644          | 0.4409   |
| 1.0581        | 46.0  | 17250 | 1.0643          | 0.4409   |
| 1.043         | 47.0  | 17625 | 1.0642          | 0.4426   |
| 1.0454        | 48.0  | 18000 | 1.0641          | 0.4443   |
| 1.0474        | 49.0  | 18375 | 1.0641          | 0.4459   |
| 1.0427        | 50.0  | 18750 | 1.0641          | 0.4459   |


### 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