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# smids_5x_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: 0.5141
- Accuracy: 0.8063

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.073         | 1.0   | 376   | 1.0746          | 0.4391   |
| 1.0668        | 2.0   | 752   | 1.0543          | 0.4775   |
| 1.0508        | 3.0   | 1128  | 1.0329          | 0.5326   |
| 1.0204        | 4.0   | 1504  | 1.0085          | 0.5676   |
| 0.9684        | 5.0   | 1880  | 0.9803          | 0.6110   |
| 0.9344        | 6.0   | 2256  | 0.9504          | 0.6344   |
| 0.9026        | 7.0   | 2632  | 0.9203          | 0.6511   |
| 0.9041        | 8.0   | 3008  | 0.8901          | 0.6694   |
| 0.8974        | 9.0   | 3384  | 0.8608          | 0.6828   |
| 0.8237        | 10.0  | 3760  | 0.8327          | 0.6945   |
| 0.7953        | 11.0  | 4136  | 0.8062          | 0.6962   |
| 0.7905        | 12.0  | 4512  | 0.7820          | 0.7028   |
| 0.7556        | 13.0  | 4888  | 0.7593          | 0.7129   |
| 0.7345        | 14.0  | 5264  | 0.7385          | 0.7262   |
| 0.7177        | 15.0  | 5640  | 0.7192          | 0.7346   |
| 0.6509        | 16.0  | 6016  | 0.7013          | 0.7412   |
| 0.6656        | 17.0  | 6392  | 0.6845          | 0.7479   |
| 0.6821        | 18.0  | 6768  | 0.6693          | 0.7496   |
| 0.6446        | 19.0  | 7144  | 0.6551          | 0.7546   |
| 0.6223        | 20.0  | 7520  | 0.6422          | 0.7629   |
| 0.6135        | 21.0  | 7896  | 0.6303          | 0.7679   |
| 0.5755        | 22.0  | 8272  | 0.6195          | 0.7713   |
| 0.5973        | 23.0  | 8648  | 0.6095          | 0.7730   |
| 0.6156        | 24.0  | 9024  | 0.6004          | 0.7746   |
| 0.6003        | 25.0  | 9400  | 0.5920          | 0.7780   |
| 0.5642        | 26.0  | 9776  | 0.5842          | 0.7796   |
| 0.5302        | 27.0  | 10152 | 0.5770          | 0.7813   |
| 0.5137        | 28.0  | 10528 | 0.5704          | 0.7863   |
| 0.5415        | 29.0  | 10904 | 0.5643          | 0.7880   |
| 0.5224        | 30.0  | 11280 | 0.5587          | 0.7880   |
| 0.5509        | 31.0  | 11656 | 0.5535          | 0.7930   |
| 0.5721        | 32.0  | 12032 | 0.5488          | 0.7913   |
| 0.5247        | 33.0  | 12408 | 0.5445          | 0.7930   |
| 0.4916        | 34.0  | 12784 | 0.5406          | 0.7963   |
| 0.5191        | 35.0  | 13160 | 0.5369          | 0.7980   |
| 0.5171        | 36.0  | 13536 | 0.5337          | 0.8013   |
| 0.4685        | 37.0  | 13912 | 0.5307          | 0.8063   |
| 0.5439        | 38.0  | 14288 | 0.5280          | 0.8063   |
| 0.4686        | 39.0  | 14664 | 0.5255          | 0.8080   |
| 0.4898        | 40.0  | 15040 | 0.5234          | 0.8063   |
| 0.509         | 41.0  | 15416 | 0.5214          | 0.8063   |
| 0.4464        | 42.0  | 15792 | 0.5198          | 0.8063   |
| 0.4635        | 43.0  | 16168 | 0.5183          | 0.8063   |
| 0.5321        | 44.0  | 16544 | 0.5171          | 0.8063   |
| 0.4939        | 45.0  | 16920 | 0.5161          | 0.8063   |
| 0.4922        | 46.0  | 17296 | 0.5153          | 0.8063   |
| 0.4738        | 47.0  | 17672 | 0.5147          | 0.8063   |
| 0.5216        | 48.0  | 18048 | 0.5143          | 0.8063   |
| 0.4726        | 49.0  | 18424 | 0.5141          | 0.8063   |
| 0.4528        | 50.0  | 18800 | 0.5141          | 0.8063   |


### 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