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# smids_3x_deit_base_adamax_001_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: 1.0127
- Accuracy: 0.8869

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4406        | 1.0   | 225   | 0.3562          | 0.8469   |
| 0.2499        | 2.0   | 450   | 0.3565          | 0.8486   |
| 0.2125        | 3.0   | 675   | 0.4018          | 0.8453   |
| 0.2418        | 4.0   | 900   | 0.3934          | 0.8569   |
| 0.1601        | 5.0   | 1125  | 0.3784          | 0.8586   |
| 0.1028        | 6.0   | 1350  | 0.4102          | 0.8669   |
| 0.1553        | 7.0   | 1575  | 0.4212          | 0.8602   |
| 0.0503        | 8.0   | 1800  | 0.4355          | 0.8835   |
| 0.1093        | 9.0   | 2025  | 0.4633          | 0.8752   |
| 0.0466        | 10.0  | 2250  | 0.4823          | 0.8769   |
| 0.0657        | 11.0  | 2475  | 0.5786          | 0.8686   |
| 0.0239        | 12.0  | 2700  | 0.4970          | 0.8835   |
| 0.0307        | 13.0  | 2925  | 0.5265          | 0.8686   |
| 0.0264        | 14.0  | 3150  | 0.5798          | 0.8935   |
| 0.0353        | 15.0  | 3375  | 0.6161          | 0.8835   |
| 0.0235        | 16.0  | 3600  | 0.6574          | 0.8852   |
| 0.0193        | 17.0  | 3825  | 0.6464          | 0.8869   |
| 0.0083        | 18.0  | 4050  | 0.5114          | 0.8935   |
| 0.0031        | 19.0  | 4275  | 0.6573          | 0.8869   |
| 0.0004        | 20.0  | 4500  | 0.6971          | 0.8918   |
| 0.023         | 21.0  | 4725  | 0.8443          | 0.8619   |
| 0.0243        | 22.0  | 4950  | 0.6663          | 0.8719   |
| 0.0379        | 23.0  | 5175  | 0.7440          | 0.8819   |
| 0.0041        | 24.0  | 5400  | 0.6714          | 0.8935   |
| 0.012         | 25.0  | 5625  | 0.8149          | 0.8802   |
| 0.0           | 26.0  | 5850  | 0.7898          | 0.8935   |
| 0.0001        | 27.0  | 6075  | 0.8193          | 0.8918   |
| 0.0           | 28.0  | 6300  | 0.7983          | 0.8852   |
| 0.0           | 29.0  | 6525  | 0.8430          | 0.8885   |
| 0.0044        | 30.0  | 6750  | 0.8519          | 0.8902   |
| 0.0027        | 31.0  | 6975  | 0.8733          | 0.8885   |
| 0.0           | 32.0  | 7200  | 0.8655          | 0.8935   |
| 0.0033        | 33.0  | 7425  | 0.8624          | 0.8852   |
| 0.0           | 34.0  | 7650  | 0.9256          | 0.8885   |
| 0.0           | 35.0  | 7875  | 0.9075          | 0.8852   |
| 0.0032        | 36.0  | 8100  | 0.9257          | 0.8869   |
| 0.0           | 37.0  | 8325  | 0.9450          | 0.8835   |
| 0.0           | 38.0  | 8550  | 0.9586          | 0.8819   |
| 0.0036        | 39.0  | 8775  | 0.9521          | 0.8852   |
| 0.0           | 40.0  | 9000  | 0.9863          | 0.8852   |
| 0.0044        | 41.0  | 9225  | 0.9719          | 0.8819   |
| 0.0           | 42.0  | 9450  | 0.9708          | 0.8819   |
| 0.0           | 43.0  | 9675  | 0.9922          | 0.8885   |
| 0.0           | 44.0  | 9900  | 0.9951          | 0.8869   |
| 0.0           | 45.0  | 10125 | 1.0055          | 0.8835   |
| 0.0           | 46.0  | 10350 | 1.0046          | 0.8885   |
| 0.0           | 47.0  | 10575 | 1.0098          | 0.8852   |
| 0.0           | 48.0  | 10800 | 1.0105          | 0.8885   |
| 0.0024        | 49.0  | 11025 | 1.0125          | 0.8869   |
| 0.0024        | 50.0  | 11250 | 1.0127          | 0.8869   |


### 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