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# smids_3x_deit_base_adamax_001_fold4

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: 1.4430
- Accuracy: 0.8683

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4213        | 1.0   | 225   | 0.6102          | 0.7717   |
| 0.3393        | 2.0   | 450   | 0.3772          | 0.8633   |
| 0.1726        | 3.0   | 675   | 0.5092          | 0.8383   |
| 0.193         | 4.0   | 900   | 0.4686          | 0.8483   |
| 0.1372        | 5.0   | 1125  | 0.5042          | 0.8433   |
| 0.1034        | 6.0   | 1350  | 0.5804          | 0.865    |
| 0.0823        | 7.0   | 1575  | 0.6233          | 0.8533   |
| 0.1236        | 8.0   | 1800  | 0.5801          | 0.865    |
| 0.0441        | 9.0   | 2025  | 0.6304          | 0.8417   |
| 0.0803        | 10.0  | 2250  | 0.5593          | 0.8717   |
| 0.0376        | 11.0  | 2475  | 0.6594          | 0.8717   |
| 0.0163        | 12.0  | 2700  | 0.8241          | 0.85     |
| 0.0216        | 13.0  | 2925  | 0.8976          | 0.8433   |
| 0.008         | 14.0  | 3150  | 0.9835          | 0.8633   |
| 0.0166        | 15.0  | 3375  | 0.7511          | 0.8617   |
| 0.0347        | 16.0  | 3600  | 0.9629          | 0.84     |
| 0.0074        | 17.0  | 3825  | 0.7860          | 0.87     |
| 0.038         | 18.0  | 4050  | 0.8908          | 0.865    |
| 0.0062        | 19.0  | 4275  | 0.9746          | 0.8583   |
| 0.0007        | 20.0  | 4500  | 0.8618          | 0.8583   |
| 0.009         | 21.0  | 4725  | 0.9722          | 0.8533   |
| 0.0392        | 22.0  | 4950  | 0.9537          | 0.8733   |
| 0.0001        | 23.0  | 5175  | 1.0033          | 0.8667   |
| 0.0           | 24.0  | 5400  | 1.1106          | 0.8683   |
| 0.0           | 25.0  | 5625  | 1.1232          | 0.87     |
| 0.0           | 26.0  | 5850  | 1.1543          | 0.8683   |
| 0.0           | 27.0  | 6075  | 1.1839          | 0.8683   |
| 0.0           | 28.0  | 6300  | 1.1861          | 0.8683   |
| 0.0           | 29.0  | 6525  | 1.2026          | 0.8683   |
| 0.0           | 30.0  | 6750  | 1.2180          | 0.8683   |
| 0.0           | 31.0  | 6975  | 1.2377          | 0.8683   |
| 0.0           | 32.0  | 7200  | 1.2451          | 0.8683   |
| 0.0           | 33.0  | 7425  | 1.2700          | 0.8667   |
| 0.0           | 34.0  | 7650  | 1.2684          | 0.8683   |
| 0.0           | 35.0  | 7875  | 1.2850          | 0.8667   |
| 0.0           | 36.0  | 8100  | 1.3057          | 0.865    |
| 0.0           | 37.0  | 8325  | 1.3124          | 0.865    |
| 0.0           | 38.0  | 8550  | 1.3285          | 0.865    |
| 0.0           | 39.0  | 8775  | 1.3576          | 0.8667   |
| 0.0           | 40.0  | 9000  | 1.3675          | 0.8667   |
| 0.0           | 41.0  | 9225  | 1.3756          | 0.8667   |
| 0.0032        | 42.0  | 9450  | 1.3799          | 0.8667   |
| 0.0           | 43.0  | 9675  | 1.4079          | 0.8683   |
| 0.0028        | 44.0  | 9900  | 1.4001          | 0.8667   |
| 0.0           | 45.0  | 10125 | 1.4214          | 0.8683   |
| 0.0           | 46.0  | 10350 | 1.4209          | 0.8683   |
| 0.0           | 47.0  | 10575 | 1.4455          | 0.8667   |
| 0.0           | 48.0  | 10800 | 1.4424          | 0.8683   |
| 0.0           | 49.0  | 11025 | 1.4398          | 0.8667   |
| 0.0           | 50.0  | 11250 | 1.4430          | 0.8683   |


### 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