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# smids_3x_deit_small_rms_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: 0.9415
- Accuracy: 0.9067

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3501        | 1.0   | 225   | 0.3758          | 0.8567   |
| 0.1897        | 2.0   | 450   | 0.3539          | 0.89     |
| 0.192         | 3.0   | 675   | 0.3659          | 0.87     |
| 0.1205        | 4.0   | 900   | 0.5440          | 0.87     |
| 0.0584        | 5.0   | 1125  | 0.5709          | 0.8783   |
| 0.0533        | 6.0   | 1350  | 0.5437          | 0.8933   |
| 0.1081        | 7.0   | 1575  | 0.4958          | 0.8917   |
| 0.0507        | 8.0   | 1800  | 0.5623          | 0.89     |
| 0.0576        | 9.0   | 2025  | 0.6335          | 0.8733   |
| 0.0323        | 10.0  | 2250  | 0.7129          | 0.8867   |
| 0.0328        | 11.0  | 2475  | 0.6336          | 0.8933   |
| 0.1145        | 12.0  | 2700  | 0.6382          | 0.885    |
| 0.0008        | 13.0  | 2925  | 0.6581          | 0.8983   |
| 0.0337        | 14.0  | 3150  | 0.7043          | 0.91     |
| 0.0295        | 15.0  | 3375  | 0.6839          | 0.905    |
| 0.0175        | 16.0  | 3600  | 0.6638          | 0.8983   |
| 0.0432        | 17.0  | 3825  | 0.6947          | 0.8883   |
| 0.0184        | 18.0  | 4050  | 0.7199          | 0.8867   |
| 0.0276        | 19.0  | 4275  | 0.8282          | 0.8767   |
| 0.011         | 20.0  | 4500  | 0.7312          | 0.8933   |
| 0.0094        | 21.0  | 4725  | 0.7792          | 0.8817   |
| 0.008         | 22.0  | 4950  | 0.8134          | 0.8833   |
| 0.0128        | 23.0  | 5175  | 0.6449          | 0.8917   |
| 0.0051        | 24.0  | 5400  | 0.9415          | 0.8983   |
| 0.0033        | 25.0  | 5625  | 0.9520          | 0.8833   |
| 0.0003        | 26.0  | 5850  | 0.8717          | 0.875    |
| 0.0001        | 27.0  | 6075  | 0.8347          | 0.9      |
| 0.0178        | 28.0  | 6300  | 0.8466          | 0.8883   |
| 0.0036        | 29.0  | 6525  | 0.9051          | 0.9      |
| 0.0021        | 30.0  | 6750  | 0.9411          | 0.89     |
| 0.0           | 31.0  | 6975  | 0.8526          | 0.895    |
| 0.0           | 32.0  | 7200  | 0.9459          | 0.8883   |
| 0.0028        | 33.0  | 7425  | 0.8486          | 0.8967   |
| 0.0016        | 34.0  | 7650  | 0.8125          | 0.9083   |
| 0.0029        | 35.0  | 7875  | 0.8544          | 0.9      |
| 0.0           | 36.0  | 8100  | 0.7896          | 0.9      |
| 0.0049        | 37.0  | 8325  | 0.8566          | 0.9033   |
| 0.0022        | 38.0  | 8550  | 0.9049          | 0.9017   |
| 0.0           | 39.0  | 8775  | 0.8095          | 0.9083   |
| 0.0           | 40.0  | 9000  | 0.8266          | 0.91     |
| 0.0           | 41.0  | 9225  | 0.8487          | 0.91     |
| 0.0           | 42.0  | 9450  | 0.8743          | 0.9117   |
| 0.0027        | 43.0  | 9675  | 0.9417          | 0.9067   |
| 0.0           | 44.0  | 9900  | 0.9107          | 0.905    |
| 0.0           | 45.0  | 10125 | 0.9200          | 0.905    |
| 0.0           | 46.0  | 10350 | 0.9166          | 0.905    |
| 0.0           | 47.0  | 10575 | 0.9308          | 0.905    |
| 0.0           | 48.0  | 10800 | 0.9335          | 0.9067   |
| 0.0           | 49.0  | 11025 | 0.9388          | 0.9067   |
| 0.0           | 50.0  | 11250 | 0.9415          | 0.9067   |


### 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