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# smids_3x_deit_base_adamax_00001_fold2

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: 0.7483
- Accuracy: 0.8819

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2908        | 1.0   | 225   | 0.3519          | 0.8469   |
| 0.2412        | 2.0   | 450   | 0.3425          | 0.8652   |
| 0.1561        | 3.0   | 675   | 0.3113          | 0.8752   |
| 0.1722        | 4.0   | 900   | 0.3333          | 0.8819   |
| 0.0793        | 5.0   | 1125  | 0.3397          | 0.8869   |
| 0.0512        | 6.0   | 1350  | 0.3703          | 0.8902   |
| 0.0408        | 7.0   | 1575  | 0.3948          | 0.8869   |
| 0.0153        | 8.0   | 1800  | 0.4474          | 0.8852   |
| 0.0036        | 9.0   | 2025  | 0.5055          | 0.8819   |
| 0.0009        | 10.0  | 2250  | 0.5138          | 0.8918   |
| 0.0011        | 11.0  | 2475  | 0.5776          | 0.8752   |
| 0.0004        | 12.0  | 2700  | 0.6002          | 0.8785   |
| 0.0004        | 13.0  | 2925  | 0.6053          | 0.8819   |
| 0.0002        | 14.0  | 3150  | 0.6097          | 0.8918   |
| 0.0002        | 15.0  | 3375  | 0.6366          | 0.8819   |
| 0.0001        | 16.0  | 3600  | 0.6507          | 0.8819   |
| 0.0042        | 17.0  | 3825  | 0.6732          | 0.8869   |
| 0.0001        | 18.0  | 4050  | 0.6626          | 0.8852   |
| 0.0001        | 19.0  | 4275  | 0.6800          | 0.8885   |
| 0.0001        | 20.0  | 4500  | 0.6886          | 0.8852   |
| 0.0001        | 21.0  | 4725  | 0.7001          | 0.8819   |
| 0.0001        | 22.0  | 4950  | 0.7256          | 0.8869   |
| 0.008         | 23.0  | 5175  | 0.7472          | 0.8918   |
| 0.0001        | 24.0  | 5400  | 0.7160          | 0.8835   |
| 0.0075        | 25.0  | 5625  | 0.7354          | 0.8852   |
| 0.0001        | 26.0  | 5850  | 0.7213          | 0.8819   |
| 0.0001        | 27.0  | 6075  | 0.7101          | 0.8835   |
| 0.0           | 28.0  | 6300  | 0.7245          | 0.8819   |
| 0.0001        | 29.0  | 6525  | 0.7475          | 0.8869   |
| 0.0058        | 30.0  | 6750  | 0.7235          | 0.8852   |
| 0.0           | 31.0  | 6975  | 0.7151          | 0.8852   |
| 0.0           | 32.0  | 7200  | 0.7303          | 0.8852   |
| 0.0           | 33.0  | 7425  | 0.7353          | 0.8835   |
| 0.0           | 34.0  | 7650  | 0.7337          | 0.8835   |
| 0.0           | 35.0  | 7875  | 0.7550          | 0.8835   |
| 0.0034        | 36.0  | 8100  | 0.7409          | 0.8885   |
| 0.0           | 37.0  | 8325  | 0.7323          | 0.8769   |
| 0.0           | 38.0  | 8550  | 0.7381          | 0.8835   |
| 0.0026        | 39.0  | 8775  | 0.7392          | 0.8819   |
| 0.0           | 40.0  | 9000  | 0.7428          | 0.8819   |
| 0.0           | 41.0  | 9225  | 0.7496          | 0.8835   |
| 0.0           | 42.0  | 9450  | 0.7433          | 0.8835   |
| 0.0           | 43.0  | 9675  | 0.7393          | 0.8852   |
| 0.0           | 44.0  | 9900  | 0.7435          | 0.8835   |
| 0.0           | 45.0  | 10125 | 0.7479          | 0.8835   |
| 0.0           | 46.0  | 10350 | 0.7454          | 0.8835   |
| 0.0           | 47.0  | 10575 | 0.7458          | 0.8802   |
| 0.0           | 48.0  | 10800 | 0.7464          | 0.8819   |
| 0.0037        | 49.0  | 11025 | 0.7477          | 0.8835   |
| 0.0037        | 50.0  | 11250 | 0.7483          | 0.8819   |


### 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