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# smids_5x_deit_base_sgd_0001_fold5

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: 0.5006
- Accuracy: 0.8133

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0891        | 1.0   | 375   | 1.0922          | 0.36     |
| 1.0632        | 2.0   | 750   | 1.0728          | 0.4133   |
| 1.0234        | 3.0   | 1125  | 1.0519          | 0.4667   |
| 1.0095        | 4.0   | 1500  | 1.0279          | 0.505    |
| 0.9691        | 5.0   | 1875  | 1.0014          | 0.54     |
| 0.9521        | 6.0   | 2250  | 0.9722          | 0.5683   |
| 0.9099        | 7.0   | 2625  | 0.9405          | 0.6033   |
| 0.8832        | 8.0   | 3000  | 0.9085          | 0.6267   |
| 0.8563        | 9.0   | 3375  | 0.8771          | 0.6533   |
| 0.8097        | 10.0  | 3750  | 0.8470          | 0.685    |
| 0.7629        | 11.0  | 4125  | 0.8186          | 0.705    |
| 0.7531        | 12.0  | 4500  | 0.7923          | 0.715    |
| 0.7082        | 13.0  | 4875  | 0.7677          | 0.7333   |
| 0.7318        | 14.0  | 5250  | 0.7449          | 0.7433   |
| 0.7243        | 15.0  | 5625  | 0.7237          | 0.7533   |
| 0.6668        | 16.0  | 6000  | 0.7041          | 0.7567   |
| 0.6939        | 17.0  | 6375  | 0.6860          | 0.76     |
| 0.6736        | 18.0  | 6750  | 0.6692          | 0.77     |
| 0.6795        | 19.0  | 7125  | 0.6538          | 0.78     |
| 0.6094        | 20.0  | 7500  | 0.6398          | 0.7833   |
| 0.5982        | 21.0  | 7875  | 0.6269          | 0.7817   |
| 0.5784        | 22.0  | 8250  | 0.6150          | 0.7867   |
| 0.6034        | 23.0  | 8625  | 0.6042          | 0.7933   |
| 0.6235        | 24.0  | 9000  | 0.5942          | 0.7967   |
| 0.5888        | 25.0  | 9375  | 0.5851          | 0.7933   |
| 0.5892        | 26.0  | 9750  | 0.5766          | 0.7933   |
| 0.5908        | 27.0  | 10125 | 0.5688          | 0.7983   |
| 0.5781        | 28.0  | 10500 | 0.5616          | 0.7983   |
| 0.5631        | 29.0  | 10875 | 0.5551          | 0.8      |
| 0.5055        | 30.0  | 11250 | 0.5492          | 0.8017   |
| 0.5168        | 31.0  | 11625 | 0.5436          | 0.805    |
| 0.5659        | 32.0  | 12000 | 0.5386          | 0.81     |
| 0.568         | 33.0  | 12375 | 0.5339          | 0.8083   |
| 0.5472        | 34.0  | 12750 | 0.5295          | 0.8117   |
| 0.5227        | 35.0  | 13125 | 0.5256          | 0.81     |
| 0.4679        | 36.0  | 13500 | 0.5220          | 0.81     |
| 0.5236        | 37.0  | 13875 | 0.5188          | 0.8117   |
| 0.5206        | 38.0  | 14250 | 0.5158          | 0.8117   |
| 0.5047        | 39.0  | 14625 | 0.5132          | 0.8133   |
| 0.5461        | 40.0  | 15000 | 0.5108          | 0.8133   |
| 0.495         | 41.0  | 15375 | 0.5087          | 0.8133   |
| 0.508         | 42.0  | 15750 | 0.5069          | 0.8133   |
| 0.5153        | 43.0  | 16125 | 0.5053          | 0.8133   |
| 0.4846        | 44.0  | 16500 | 0.5040          | 0.8133   |
| 0.5055        | 45.0  | 16875 | 0.5029          | 0.8133   |
| 0.5156        | 46.0  | 17250 | 0.5020          | 0.8133   |
| 0.525         | 47.0  | 17625 | 0.5013          | 0.8133   |
| 0.4795        | 48.0  | 18000 | 0.5009          | 0.8133   |
| 0.4888        | 49.0  | 18375 | 0.5006          | 0.8133   |
| 0.4989        | 50.0  | 18750 | 0.5006          | 0.8133   |


### 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