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# smids_3x_deit_base_rms_001_fold5

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: 0.6853
- Accuracy: 0.8

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9132        | 1.0   | 225   | 0.8802          | 0.5617   |
| 0.8136        | 2.0   | 450   | 0.8157          | 0.5617   |
| 0.8155        | 3.0   | 675   | 0.8250          | 0.5317   |
| 0.7604        | 4.0   | 900   | 0.7965          | 0.565    |
| 0.7705        | 5.0   | 1125  | 0.7610          | 0.6317   |
| 0.7485        | 6.0   | 1350  | 0.7507          | 0.6383   |
| 0.8596        | 7.0   | 1575  | 0.7348          | 0.645    |
| 0.7401        | 8.0   | 1800  | 0.7401          | 0.6367   |
| 0.7129        | 9.0   | 2025  | 0.7139          | 0.6667   |
| 0.7473        | 10.0  | 2250  | 0.7399          | 0.6617   |
| 0.7702        | 11.0  | 2475  | 0.6996          | 0.6483   |
| 0.707         | 12.0  | 2700  | 0.6871          | 0.69     |
| 0.7396        | 13.0  | 2925  | 0.6860          | 0.6933   |
| 0.7208        | 14.0  | 3150  | 0.6741          | 0.6817   |
| 0.6449        | 15.0  | 3375  | 0.6795          | 0.6867   |
| 0.6507        | 16.0  | 3600  | 0.6654          | 0.7367   |
| 0.6374        | 17.0  | 3825  | 0.6227          | 0.7183   |
| 0.6647        | 18.0  | 4050  | 0.6295          | 0.7133   |
| 0.6463        | 19.0  | 4275  | 0.6181          | 0.73     |
| 0.5869        | 20.0  | 4500  | 0.6227          | 0.7233   |
| 0.5936        | 21.0  | 4725  | 0.5964          | 0.73     |
| 0.6054        | 22.0  | 4950  | 0.6202          | 0.705    |
| 0.6046        | 23.0  | 5175  | 0.5370          | 0.7667   |
| 0.6069        | 24.0  | 5400  | 0.5762          | 0.75     |
| 0.5874        | 25.0  | 5625  | 0.5468          | 0.7667   |
| 0.6566        | 26.0  | 5850  | 0.5470          | 0.745    |
| 0.5832        | 27.0  | 6075  | 0.5273          | 0.7717   |
| 0.5576        | 28.0  | 6300  | 0.5735          | 0.715    |
| 0.5323        | 29.0  | 6525  | 0.5824          | 0.74     |
| 0.531         | 30.0  | 6750  | 0.5710          | 0.7467   |
| 0.4798        | 31.0  | 6975  | 0.5333          | 0.7667   |
| 0.4348        | 32.0  | 7200  | 0.5654          | 0.765    |
| 0.441         | 33.0  | 7425  | 0.5506          | 0.7683   |
| 0.4964        | 34.0  | 7650  | 0.5395          | 0.7817   |
| 0.5026        | 35.0  | 7875  | 0.5510          | 0.7783   |
| 0.4124        | 36.0  | 8100  | 0.5446          | 0.78     |
| 0.5129        | 37.0  | 8325  | 0.5421          | 0.7883   |
| 0.4253        | 38.0  | 8550  | 0.4973          | 0.795    |
| 0.4373        | 39.0  | 8775  | 0.5360          | 0.79     |
| 0.415         | 40.0  | 9000  | 0.5258          | 0.7983   |
| 0.4173        | 41.0  | 9225  | 0.5340          | 0.7867   |
| 0.4858        | 42.0  | 9450  | 0.5549          | 0.8067   |
| 0.3971        | 43.0  | 9675  | 0.5638          | 0.8117   |
| 0.456         | 44.0  | 9900  | 0.5513          | 0.815    |
| 0.3511        | 45.0  | 10125 | 0.5715          | 0.8067   |
| 0.2767        | 46.0  | 10350 | 0.5835          | 0.7967   |
| 0.3597        | 47.0  | 10575 | 0.6128          | 0.805    |
| 0.264         | 48.0  | 10800 | 0.6285          | 0.815    |
| 0.3062        | 49.0  | 11025 | 0.6718          | 0.8017   |
| 0.2147        | 50.0  | 11250 | 0.6853          | 0.8      |


### 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