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# smids_3x_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: 0.6429
- Accuracy: 0.77

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0879        | 1.0   | 225   | 1.0991          | 0.3483   |
| 1.0983        | 2.0   | 450   | 1.0868          | 0.3783   |
| 1.0739        | 3.0   | 675   | 1.0747          | 0.4117   |
| 1.0339        | 4.0   | 900   | 1.0623          | 0.44     |
| 1.037         | 5.0   | 1125  | 1.0494          | 0.475    |
| 1.0134        | 6.0   | 1350  | 1.0355          | 0.4983   |
| 1.0062        | 7.0   | 1575  | 1.0204          | 0.5233   |
| 0.9728        | 8.0   | 1800  | 1.0042          | 0.5467   |
| 0.9628        | 9.0   | 2025  | 0.9873          | 0.56     |
| 0.9357        | 10.0  | 2250  | 0.9698          | 0.58     |
| 0.9397        | 11.0  | 2475  | 0.9521          | 0.6      |
| 0.916         | 12.0  | 2700  | 0.9337          | 0.6183   |
| 0.9116        | 13.0  | 2925  | 0.9154          | 0.6283   |
| 0.8855        | 14.0  | 3150  | 0.8973          | 0.6467   |
| 0.8322        | 15.0  | 3375  | 0.8797          | 0.67     |
| 0.8412        | 16.0  | 3600  | 0.8631          | 0.6883   |
| 0.8508        | 17.0  | 3825  | 0.8471          | 0.695    |
| 0.8144        | 18.0  | 4050  | 0.8319          | 0.7      |
| 0.8203        | 19.0  | 4275  | 0.8174          | 0.715    |
| 0.8033        | 20.0  | 4500  | 0.8038          | 0.7283   |
| 0.7838        | 21.0  | 4725  | 0.7909          | 0.7317   |
| 0.7999        | 22.0  | 4950  | 0.7790          | 0.73     |
| 0.7579        | 23.0  | 5175  | 0.7676          | 0.7383   |
| 0.7789        | 24.0  | 5400  | 0.7568          | 0.7433   |
| 0.7344        | 25.0  | 5625  | 0.7468          | 0.7467   |
| 0.7494        | 26.0  | 5850  | 0.7374          | 0.7517   |
| 0.7562        | 27.0  | 6075  | 0.7284          | 0.755    |
| 0.701         | 28.0  | 6300  | 0.7202          | 0.7583   |
| 0.7274        | 29.0  | 6525  | 0.7124          | 0.7617   |
| 0.666         | 30.0  | 6750  | 0.7052          | 0.7617   |
| 0.691         | 31.0  | 6975  | 0.6984          | 0.765    |
| 0.6782        | 32.0  | 7200  | 0.6921          | 0.765    |
| 0.7115        | 33.0  | 7425  | 0.6862          | 0.7667   |
| 0.6811        | 34.0  | 7650  | 0.6808          | 0.7717   |
| 0.6447        | 35.0  | 7875  | 0.6758          | 0.7717   |
| 0.6506        | 36.0  | 8100  | 0.6713          | 0.7717   |
| 0.6763        | 37.0  | 8325  | 0.6671          | 0.7717   |
| 0.6655        | 38.0  | 8550  | 0.6632          | 0.7733   |
| 0.6291        | 39.0  | 8775  | 0.6597          | 0.7767   |
| 0.6533        | 40.0  | 9000  | 0.6566          | 0.775    |
| 0.6426        | 41.0  | 9225  | 0.6538          | 0.7733   |
| 0.6744        | 42.0  | 9450  | 0.6514          | 0.7733   |
| 0.6449        | 43.0  | 9675  | 0.6493          | 0.775    |
| 0.6886        | 44.0  | 9900  | 0.6475          | 0.7733   |
| 0.6363        | 45.0  | 10125 | 0.6460          | 0.7733   |
| 0.6267        | 46.0  | 10350 | 0.6448          | 0.7717   |
| 0.6739        | 47.0  | 10575 | 0.6439          | 0.77     |
| 0.6555        | 48.0  | 10800 | 0.6433          | 0.7717   |
| 0.6557        | 49.0  | 11025 | 0.6430          | 0.77     |
| 0.6715        | 50.0  | 11250 | 0.6429          | 0.77     |


### 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