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# smids_3x_deit_tiny_adamax_00001_fold5

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8922
- Accuracy: 0.8867

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4176        | 1.0   | 225   | 0.4035          | 0.8483   |
| 0.2988        | 2.0   | 450   | 0.3067          | 0.8767   |
| 0.2655        | 3.0   | 675   | 0.2905          | 0.875    |
| 0.1842        | 4.0   | 900   | 0.2684          | 0.8983   |
| 0.1435        | 5.0   | 1125  | 0.2769          | 0.8933   |
| 0.1106        | 6.0   | 1350  | 0.2720          | 0.895    |
| 0.1509        | 7.0   | 1575  | 0.2967          | 0.8917   |
| 0.1529        | 8.0   | 1800  | 0.3180          | 0.8767   |
| 0.1311        | 9.0   | 2025  | 0.3248          | 0.89     |
| 0.0801        | 10.0  | 2250  | 0.3813          | 0.885    |
| 0.0435        | 11.0  | 2475  | 0.4094          | 0.8833   |
| 0.0973        | 12.0  | 2700  | 0.4656          | 0.88     |
| 0.0775        | 13.0  | 2925  | 0.4789          | 0.8917   |
| 0.0342        | 14.0  | 3150  | 0.5459          | 0.88     |
| 0.0207        | 15.0  | 3375  | 0.5599          | 0.8833   |
| 0.0139        | 16.0  | 3600  | 0.5932          | 0.8917   |
| 0.0015        | 17.0  | 3825  | 0.6480          | 0.88     |
| 0.0008        | 18.0  | 4050  | 0.6641          | 0.88     |
| 0.0269        | 19.0  | 4275  | 0.6876          | 0.885    |
| 0.0066        | 20.0  | 4500  | 0.7051          | 0.8883   |
| 0.0002        | 21.0  | 4725  | 0.7338          | 0.8883   |
| 0.0003        | 22.0  | 4950  | 0.7295          | 0.88     |
| 0.0053        | 23.0  | 5175  | 0.7640          | 0.8833   |
| 0.0118        | 24.0  | 5400  | 0.8006          | 0.8833   |
| 0.0002        | 25.0  | 5625  | 0.7995          | 0.885    |
| 0.0002        | 26.0  | 5850  | 0.8061          | 0.8833   |
| 0.0002        | 27.0  | 6075  | 0.8090          | 0.8817   |
| 0.0003        | 28.0  | 6300  | 0.8501          | 0.875    |
| 0.0137        | 29.0  | 6525  | 0.8643          | 0.8767   |
| 0.0001        | 30.0  | 6750  | 0.8347          | 0.885    |
| 0.0001        | 31.0  | 6975  | 0.8412          | 0.8867   |
| 0.0001        | 32.0  | 7200  | 0.8482          | 0.8867   |
| 0.0139        | 33.0  | 7425  | 0.8560          | 0.8833   |
| 0.0           | 34.0  | 7650  | 0.8490          | 0.8817   |
| 0.0026        | 35.0  | 7875  | 0.8633          | 0.8867   |
| 0.0           | 36.0  | 8100  | 0.8671          | 0.8883   |
| 0.0222        | 37.0  | 8325  | 0.8736          | 0.885    |
| 0.0           | 38.0  | 8550  | 0.8850          | 0.8783   |
| 0.0           | 39.0  | 8775  | 0.8799          | 0.8833   |
| 0.0           | 40.0  | 9000  | 0.8936          | 0.88     |
| 0.0           | 41.0  | 9225  | 0.8899          | 0.8817   |
| 0.0           | 42.0  | 9450  | 0.8900          | 0.885    |
| 0.0114        | 43.0  | 9675  | 0.8889          | 0.8833   |
| 0.0           | 44.0  | 9900  | 0.8840          | 0.8867   |
| 0.0           | 45.0  | 10125 | 0.8851          | 0.8867   |
| 0.0           | 46.0  | 10350 | 0.8906          | 0.885    |
| 0.0           | 47.0  | 10575 | 0.8900          | 0.885    |
| 0.0           | 48.0  | 10800 | 0.8911          | 0.8867   |
| 0.0           | 49.0  | 11025 | 0.8913          | 0.8867   |
| 0.0           | 50.0  | 11250 | 0.8922          | 0.8867   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm