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# smids_3x_deit_small_adamax_0001_fold5

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8808
- Accuracy: 0.9

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3174        | 1.0   | 225   | 0.2998          | 0.8783   |
| 0.1192        | 2.0   | 450   | 0.2723          | 0.9083   |
| 0.0978        | 3.0   | 675   | 0.4594          | 0.8617   |
| 0.0329        | 4.0   | 900   | 0.4368          | 0.8833   |
| 0.0356        | 5.0   | 1125  | 0.4938          | 0.89     |
| 0.0115        | 6.0   | 1350  | 0.5783          | 0.895    |
| 0.0594        | 7.0   | 1575  | 0.5882          | 0.8933   |
| 0.0073        | 8.0   | 1800  | 0.6931          | 0.8983   |
| 0.0161        | 9.0   | 2025  | 0.7537          | 0.8917   |
| 0.0716        | 10.0  | 2250  | 0.8442          | 0.885    |
| 0.0028        | 11.0  | 2475  | 0.9224          | 0.875    |
| 0.0418        | 12.0  | 2700  | 0.8061          | 0.8883   |
| 0.0025        | 13.0  | 2925  | 0.9720          | 0.865    |
| 0.0001        | 14.0  | 3150  | 0.8224          | 0.8883   |
| 0.0003        | 15.0  | 3375  | 0.8506          | 0.8883   |
| 0.0           | 16.0  | 3600  | 0.8357          | 0.8883   |
| 0.0           | 17.0  | 3825  | 0.7488          | 0.895    |
| 0.0043        | 18.0  | 4050  | 0.8378          | 0.8783   |
| 0.008         | 19.0  | 4275  | 0.8339          | 0.8883   |
| 0.0009        | 20.0  | 4500  | 0.8302          | 0.8917   |
| 0.0           | 21.0  | 4725  | 0.8494          | 0.89     |
| 0.0           | 22.0  | 4950  | 0.8420          | 0.895    |
| 0.0           | 23.0  | 5175  | 0.8388          | 0.8933   |
| 0.0042        | 24.0  | 5400  | 0.8556          | 0.895    |
| 0.0           | 25.0  | 5625  | 0.8527          | 0.895    |
| 0.0           | 26.0  | 5850  | 0.8812          | 0.8917   |
| 0.0           | 27.0  | 6075  | 0.8957          | 0.8917   |
| 0.0           | 28.0  | 6300  | 0.8419          | 0.8933   |
| 0.0034        | 29.0  | 6525  | 0.8176          | 0.8967   |
| 0.0           | 30.0  | 6750  | 0.8204          | 0.9      |
| 0.0           | 31.0  | 6975  | 0.8207          | 0.9      |
| 0.0           | 32.0  | 7200  | 0.8298          | 0.8933   |
| 0.0034        | 33.0  | 7425  | 0.8331          | 0.8967   |
| 0.0           | 34.0  | 7650  | 0.8399          | 0.895    |
| 0.0029        | 35.0  | 7875  | 0.8427          | 0.895    |
| 0.0           | 36.0  | 8100  | 0.8464          | 0.895    |
| 0.004         | 37.0  | 8325  | 0.8513          | 0.8967   |
| 0.0           | 38.0  | 8550  | 0.8556          | 0.8983   |
| 0.0           | 39.0  | 8775  | 0.8574          | 0.8933   |
| 0.0           | 40.0  | 9000  | 0.8619          | 0.8967   |
| 0.0           | 41.0  | 9225  | 0.8650          | 0.8983   |
| 0.0           | 42.0  | 9450  | 0.8684          | 0.8983   |
| 0.0027        | 43.0  | 9675  | 0.8709          | 0.8983   |
| 0.0           | 44.0  | 9900  | 0.8740          | 0.8983   |
| 0.0           | 45.0  | 10125 | 0.8773          | 0.8967   |
| 0.0           | 46.0  | 10350 | 0.8777          | 0.9      |
| 0.0           | 47.0  | 10575 | 0.8792          | 0.9      |
| 0.0           | 48.0  | 10800 | 0.8800          | 0.9      |
| 0.0           | 49.0  | 11025 | 0.8807          | 0.9      |
| 0.0           | 50.0  | 11250 | 0.8808          | 0.9      |


### 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