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# smids_3x_deit_base_sgd_0001_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: 0.6484
- Accuracy: 0.7833

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0943        | 1.0   | 225   | 1.0944          | 0.385    |
| 1.0886        | 2.0   | 450   | 1.0814          | 0.4183   |
| 1.0651        | 3.0   | 675   | 1.0687          | 0.4433   |
| 1.0331        | 4.0   | 900   | 1.0558          | 0.47     |
| 1.0336        | 5.0   | 1125  | 1.0425          | 0.495    |
| 1.0015        | 6.0   | 1350  | 1.0281          | 0.5283   |
| 0.9943        | 7.0   | 1575  | 1.0128          | 0.5417   |
| 1.0159        | 8.0   | 1800  | 0.9962          | 0.5633   |
| 0.9416        | 9.0   | 2025  | 0.9786          | 0.58     |
| 0.9487        | 10.0  | 2250  | 0.9602          | 0.605    |
| 0.9205        | 11.0  | 2475  | 0.9418          | 0.6283   |
| 0.9048        | 12.0  | 2700  | 0.9232          | 0.6467   |
| 0.8976        | 13.0  | 2925  | 0.9053          | 0.6583   |
| 0.8732        | 14.0  | 3150  | 0.8879          | 0.6683   |
| 0.8572        | 15.0  | 3375  | 0.8714          | 0.6867   |
| 0.8436        | 16.0  | 3600  | 0.8558          | 0.6917   |
| 0.8538        | 17.0  | 3825  | 0.8409          | 0.705    |
| 0.8276        | 18.0  | 4050  | 0.8268          | 0.7117   |
| 0.7944        | 19.0  | 4275  | 0.8134          | 0.72     |
| 0.7901        | 20.0  | 4500  | 0.8009          | 0.7283   |
| 0.7811        | 21.0  | 4725  | 0.7889          | 0.735    |
| 0.7752        | 22.0  | 4950  | 0.7777          | 0.735    |
| 0.7747        | 23.0  | 5175  | 0.7670          | 0.7367   |
| 0.7558        | 24.0  | 5400  | 0.7570          | 0.735    |
| 0.7382        | 25.0  | 5625  | 0.7476          | 0.7383   |
| 0.7437        | 26.0  | 5850  | 0.7388          | 0.7433   |
| 0.713         | 27.0  | 6075  | 0.7303          | 0.7467   |
| 0.7178        | 28.0  | 6300  | 0.7225          | 0.75     |
| 0.7125        | 29.0  | 6525  | 0.7152          | 0.7583   |
| 0.6668        | 30.0  | 6750  | 0.7082          | 0.7633   |
| 0.681         | 31.0  | 6975  | 0.7018          | 0.765    |
| 0.7053        | 32.0  | 7200  | 0.6957          | 0.7633   |
| 0.7058        | 33.0  | 7425  | 0.6901          | 0.765    |
| 0.6687        | 34.0  | 7650  | 0.6848          | 0.77     |
| 0.6808        | 35.0  | 7875  | 0.6800          | 0.7717   |
| 0.7094        | 36.0  | 8100  | 0.6756          | 0.7717   |
| 0.6409        | 37.0  | 8325  | 0.6716          | 0.775    |
| 0.6467        | 38.0  | 8550  | 0.6679          | 0.775    |
| 0.6641        | 39.0  | 8775  | 0.6646          | 0.7783   |
| 0.6586        | 40.0  | 9000  | 0.6616          | 0.7783   |
| 0.6383        | 41.0  | 9225  | 0.6590          | 0.7817   |
| 0.6999        | 42.0  | 9450  | 0.6566          | 0.7817   |
| 0.615         | 43.0  | 9675  | 0.6546          | 0.7833   |
| 0.6607        | 44.0  | 9900  | 0.6528          | 0.7833   |
| 0.6399        | 45.0  | 10125 | 0.6514          | 0.7817   |
| 0.6503        | 46.0  | 10350 | 0.6503          | 0.7833   |
| 0.6327        | 47.0  | 10575 | 0.6494          | 0.7833   |
| 0.6452        | 48.0  | 10800 | 0.6488          | 0.7833   |
| 0.6566        | 49.0  | 11025 | 0.6485          | 0.7833   |
| 0.6339        | 50.0  | 11250 | 0.6484          | 0.7833   |


### 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