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# smids_3x_deit_base_rms_0001_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: 1.2420
- 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3339        | 1.0   | 225   | 0.3477          | 0.8767   |
| 0.1864        | 2.0   | 450   | 0.4551          | 0.875    |
| 0.1083        | 3.0   | 675   | 0.7108          | 0.8417   |
| 0.1229        | 4.0   | 900   | 0.4781          | 0.8883   |
| 0.0535        | 5.0   | 1125  | 0.6232          | 0.8733   |
| 0.0735        | 6.0   | 1350  | 0.5812          | 0.88     |
| 0.0369        | 7.0   | 1575  | 0.6859          | 0.8667   |
| 0.055         | 8.0   | 1800  | 0.7382          | 0.8417   |
| 0.0533        | 9.0   | 2025  | 0.7001          | 0.8833   |
| 0.0337        | 10.0  | 2250  | 0.7953          | 0.855    |
| 0.0039        | 11.0  | 2475  | 0.6932          | 0.8783   |
| 0.0367        | 12.0  | 2700  | 0.7006          | 0.8733   |
| 0.0008        | 13.0  | 2925  | 0.6228          | 0.895    |
| 0.0352        | 14.0  | 3150  | 0.6483          | 0.88     |
| 0.0536        | 15.0  | 3375  | 0.7338          | 0.8733   |
| 0.0282        | 16.0  | 3600  | 0.7455          | 0.8667   |
| 0.0418        | 17.0  | 3825  | 0.6314          | 0.8833   |
| 0.0045        | 18.0  | 4050  | 0.8581          | 0.8833   |
| 0.0013        | 19.0  | 4275  | 0.8791          | 0.8717   |
| 0.0236        | 20.0  | 4500  | 0.8293          | 0.8733   |
| 0.0003        | 21.0  | 4725  | 0.9177          | 0.87     |
| 0.0214        | 22.0  | 4950  | 0.9227          | 0.8633   |
| 0.0034        | 23.0  | 5175  | 0.7968          | 0.8867   |
| 0.0319        | 24.0  | 5400  | 0.8643          | 0.885    |
| 0.0068        | 25.0  | 5625  | 0.8481          | 0.88     |
| 0.0008        | 26.0  | 5850  | 1.0685          | 0.8733   |
| 0.0           | 27.0  | 6075  | 0.9795          | 0.88     |
| 0.016         | 28.0  | 6300  | 0.9377          | 0.87     |
| 0.0001        | 29.0  | 6525  | 1.0537          | 0.855    |
| 0.0           | 30.0  | 6750  | 1.0268          | 0.865    |
| 0.0184        | 31.0  | 6975  | 0.8036          | 0.8733   |
| 0.0           | 32.0  | 7200  | 0.9837          | 0.88     |
| 0.0           | 33.0  | 7425  | 0.9530          | 0.8917   |
| 0.0           | 34.0  | 7650  | 1.0294          | 0.8883   |
| 0.0           | 35.0  | 7875  | 1.0787          | 0.8817   |
| 0.0           | 36.0  | 8100  | 1.0515          | 0.885    |
| 0.0           | 37.0  | 8325  | 1.0565          | 0.8867   |
| 0.0           | 38.0  | 8550  | 1.0716          | 0.89     |
| 0.0           | 39.0  | 8775  | 1.0979          | 0.885    |
| 0.0           | 40.0  | 9000  | 1.1315          | 0.8867   |
| 0.0           | 41.0  | 9225  | 1.1955          | 0.8867   |
| 0.0034        | 42.0  | 9450  | 1.1923          | 0.8933   |
| 0.0           | 43.0  | 9675  | 1.1858          | 0.8917   |
| 0.0029        | 44.0  | 9900  | 1.2299          | 0.89     |
| 0.0           | 45.0  | 10125 | 1.2340          | 0.89     |
| 0.0           | 46.0  | 10350 | 1.2528          | 0.885    |
| 0.0           | 47.0  | 10575 | 1.2511          | 0.8867   |
| 0.0           | 48.0  | 10800 | 1.2410          | 0.8867   |
| 0.0           | 49.0  | 11025 | 1.2401          | 0.8867   |
| 0.0           | 50.0  | 11250 | 1.2420          | 0.8867   |


### 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