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# smids_5x_deit_base_adamax_001_fold3

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: 1.1005
- Accuracy: 0.89

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.307         | 1.0   | 375   | 0.3243          | 0.88     |
| 0.1951        | 2.0   | 750   | 0.2848          | 0.8967   |
| 0.1756        | 3.0   | 1125  | 0.3260          | 0.8767   |
| 0.1301        | 4.0   | 1500  | 0.3461          | 0.8933   |
| 0.1724        | 5.0   | 1875  | 0.3433          | 0.8783   |
| 0.1105        | 6.0   | 2250  | 0.5327          | 0.8517   |
| 0.105         | 7.0   | 2625  | 0.4495          | 0.89     |
| 0.1373        | 8.0   | 3000  | 0.3477          | 0.8933   |
| 0.0545        | 9.0   | 3375  | 0.5403          | 0.8767   |
| 0.026         | 10.0  | 3750  | 0.6392          | 0.8717   |
| 0.0547        | 11.0  | 4125  | 0.6160          | 0.875    |
| 0.0385        | 12.0  | 4500  | 0.5572          | 0.885    |
| 0.0376        | 13.0  | 4875  | 0.6146          | 0.8967   |
| 0.0031        | 14.0  | 5250  | 0.6509          | 0.8883   |
| 0.0185        | 15.0  | 5625  | 0.6515          | 0.885    |
| 0.0353        | 16.0  | 6000  | 0.7637          | 0.885    |
| 0.0052        | 17.0  | 6375  | 0.7211          | 0.8817   |
| 0.011         | 18.0  | 6750  | 0.5915          | 0.9067   |
| 0.0053        | 19.0  | 7125  | 0.6576          | 0.89     |
| 0.0044        | 20.0  | 7500  | 0.6728          | 0.8983   |
| 0.0003        | 21.0  | 7875  | 0.7362          | 0.8817   |
| 0.0001        | 22.0  | 8250  | 0.7370          | 0.8817   |
| 0.0265        | 23.0  | 8625  | 0.6954          | 0.895    |
| 0.0011        | 24.0  | 9000  | 0.7244          | 0.8883   |
| 0.0056        | 25.0  | 9375  | 0.7383          | 0.8917   |
| 0.0           | 26.0  | 9750  | 0.6944          | 0.9033   |
| 0.0001        | 27.0  | 10125 | 0.8581          | 0.8933   |
| 0.0002        | 28.0  | 10500 | 0.7732          | 0.8917   |
| 0.0001        | 29.0  | 10875 | 0.9540          | 0.8867   |
| 0.005         | 30.0  | 11250 | 0.8145          | 0.8933   |
| 0.0003        | 31.0  | 11625 | 0.8223          | 0.8967   |
| 0.0           | 32.0  | 12000 | 0.8225          | 0.89     |
| 0.0           | 33.0  | 12375 | 0.8479          | 0.8933   |
| 0.0           | 34.0  | 12750 | 0.8571          | 0.895    |
| 0.0           | 35.0  | 13125 | 0.9119          | 0.8917   |
| 0.0           | 36.0  | 13500 | 0.9029          | 0.8917   |
| 0.0           | 37.0  | 13875 | 0.9226          | 0.8967   |
| 0.0           | 38.0  | 14250 | 0.9083          | 0.895    |
| 0.0           | 39.0  | 14625 | 1.0048          | 0.8933   |
| 0.0026        | 40.0  | 15000 | 1.0018          | 0.8883   |
| 0.0           | 41.0  | 15375 | 1.0177          | 0.8917   |
| 0.0           | 42.0  | 15750 | 1.0273          | 0.8917   |
| 0.0           | 43.0  | 16125 | 1.0393          | 0.8933   |
| 0.0           | 44.0  | 16500 | 1.0649          | 0.895    |
| 0.0           | 45.0  | 16875 | 1.0825          | 0.8883   |
| 0.0           | 46.0  | 17250 | 1.0743          | 0.895    |
| 0.0           | 47.0  | 17625 | 1.0848          | 0.8917   |
| 0.0           | 48.0  | 18000 | 1.0902          | 0.8917   |
| 0.0           | 49.0  | 18375 | 1.0954          | 0.89     |
| 0.0           | 50.0  | 18750 | 1.1005          | 0.89     |


### 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