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# smids_3x_deit_base_sgd_00001_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: 1.0659
- Accuracy: 0.4491

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1289        | 1.0   | 226   | 1.0936          | 0.3873   |
| 1.088         | 2.0   | 452   | 1.0923          | 0.3907   |
| 1.1393        | 3.0   | 678   | 1.0911          | 0.3940   |
| 1.1082        | 4.0   | 904   | 1.0899          | 0.3957   |
| 1.1039        | 5.0   | 1130  | 1.0887          | 0.3973   |
| 1.1198        | 6.0   | 1356  | 1.0876          | 0.3957   |
| 1.1055        | 7.0   | 1582  | 1.0865          | 0.3957   |
| 1.1209        | 8.0   | 1808  | 1.0854          | 0.3973   |
| 1.0984        | 9.0   | 2034  | 1.0844          | 0.3990   |
| 1.0834        | 10.0  | 2260  | 1.0834          | 0.4040   |
| 1.1107        | 11.0  | 2486  | 1.0825          | 0.4057   |
| 1.1106        | 12.0  | 2712  | 1.0815          | 0.4107   |
| 1.0951        | 13.0  | 2938  | 1.0807          | 0.4107   |
| 1.084         | 14.0  | 3164  | 1.0798          | 0.4140   |
| 1.0913        | 15.0  | 3390  | 1.0790          | 0.4224   |
| 1.0879        | 16.0  | 3616  | 1.0781          | 0.4274   |
| 1.0942        | 17.0  | 3842  | 1.0774          | 0.4290   |
| 1.1034        | 18.0  | 4068  | 1.0766          | 0.4290   |
| 1.0749        | 19.0  | 4294  | 1.0759          | 0.4290   |
| 1.0856        | 20.0  | 4520  | 1.0752          | 0.4341   |
| 1.0907        | 21.0  | 4746  | 1.0745          | 0.4407   |
| 1.0776        | 22.0  | 4972  | 1.0739          | 0.4424   |
| 1.0863        | 23.0  | 5198  | 1.0733          | 0.4407   |
| 1.0887        | 24.0  | 5424  | 1.0727          | 0.4424   |
| 1.0775        | 25.0  | 5650  | 1.0722          | 0.4474   |
| 1.092         | 26.0  | 5876  | 1.0716          | 0.4457   |
| 1.09          | 27.0  | 6102  | 1.0711          | 0.4424   |
| 1.0932        | 28.0  | 6328  | 1.0707          | 0.4391   |
| 1.0761        | 29.0  | 6554  | 1.0702          | 0.4407   |
| 1.0937        | 30.0  | 6780  | 1.0698          | 0.4407   |
| 1.0661        | 31.0  | 7006  | 1.0694          | 0.4424   |
| 1.0826        | 32.0  | 7232  | 1.0690          | 0.4424   |
| 1.0898        | 33.0  | 7458  | 1.0686          | 0.4407   |
| 1.0784        | 34.0  | 7684  | 1.0683          | 0.4457   |
| 1.0944        | 35.0  | 7910  | 1.0680          | 0.4457   |
| 1.08          | 36.0  | 8136  | 1.0677          | 0.4474   |
| 1.0796        | 37.0  | 8362  | 1.0674          | 0.4474   |
| 1.08          | 38.0  | 8588  | 1.0672          | 0.4491   |
| 1.0835        | 39.0  | 8814  | 1.0670          | 0.4491   |
| 1.0952        | 40.0  | 9040  | 1.0668          | 0.4491   |
| 1.0844        | 41.0  | 9266  | 1.0666          | 0.4474   |
| 1.0907        | 42.0  | 9492  | 1.0664          | 0.4474   |
| 1.087         | 43.0  | 9718  | 1.0663          | 0.4474   |
| 1.0798        | 44.0  | 9944  | 1.0662          | 0.4474   |
| 1.0672        | 45.0  | 10170 | 1.0661          | 0.4457   |
| 1.0874        | 46.0  | 10396 | 1.0660          | 0.4457   |
| 1.0866        | 47.0  | 10622 | 1.0660          | 0.4457   |
| 1.0871        | 48.0  | 10848 | 1.0660          | 0.4474   |
| 1.0775        | 49.0  | 11074 | 1.0659          | 0.4491   |
| 1.0886        | 50.0  | 11300 | 1.0659          | 0.4491   |


### 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