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# smids_3x_deit_base_rms_001_fold3

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: 0.5835
- Accuracy: 0.76

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9577        | 1.0   | 225   | 0.9911          | 0.4833   |
| 0.8945        | 2.0   | 450   | 0.8723          | 0.515    |
| 0.9126        | 3.0   | 675   | 0.9098          | 0.5583   |
| 0.864         | 4.0   | 900   | 0.9590          | 0.4883   |
| 0.7818        | 5.0   | 1125  | 0.8052          | 0.61     |
| 0.8173        | 6.0   | 1350  | 0.8349          | 0.57     |
| 0.8234        | 7.0   | 1575  | 0.8296          | 0.5633   |
| 0.8025        | 8.0   | 1800  | 0.7926          | 0.6433   |
| 0.794         | 9.0   | 2025  | 0.7671          | 0.6367   |
| 0.7364        | 10.0  | 2250  | 0.7623          | 0.6917   |
| 0.7539        | 11.0  | 2475  | 0.7462          | 0.6567   |
| 0.7493        | 12.0  | 2700  | 0.7599          | 0.6483   |
| 0.8066        | 13.0  | 2925  | 0.7984          | 0.6233   |
| 0.7644        | 14.0  | 3150  | 0.7284          | 0.6767   |
| 0.6406        | 15.0  | 3375  | 0.8986          | 0.6117   |
| 0.7539        | 16.0  | 3600  | 0.7380          | 0.6417   |
| 0.7079        | 17.0  | 3825  | 0.7519          | 0.6483   |
| 0.7112        | 18.0  | 4050  | 0.7274          | 0.6683   |
| 0.709         | 19.0  | 4275  | 0.7182          | 0.6783   |
| 0.6627        | 20.0  | 4500  | 0.6933          | 0.6917   |
| 0.62          | 21.0  | 4725  | 0.7192          | 0.6783   |
| 0.6351        | 22.0  | 4950  | 0.6854          | 0.6967   |
| 0.6169        | 23.0  | 5175  | 0.6958          | 0.6917   |
| 0.6173        | 24.0  | 5400  | 0.6916          | 0.6867   |
| 0.6807        | 25.0  | 5625  | 0.6783          | 0.705    |
| 0.6099        | 26.0  | 5850  | 0.6681          | 0.705    |
| 0.5604        | 27.0  | 6075  | 0.7149          | 0.6767   |
| 0.6004        | 28.0  | 6300  | 0.7253          | 0.6667   |
| 0.6392        | 29.0  | 6525  | 0.6891          | 0.66     |
| 0.5659        | 30.0  | 6750  | 0.6273          | 0.7267   |
| 0.5546        | 31.0  | 6975  | 0.6350          | 0.7317   |
| 0.5835        | 32.0  | 7200  | 0.6529          | 0.6983   |
| 0.6237        | 33.0  | 7425  | 0.6048          | 0.7233   |
| 0.5674        | 34.0  | 7650  | 0.6396          | 0.7167   |
| 0.5405        | 35.0  | 7875  | 0.6074          | 0.7183   |
| 0.5745        | 36.0  | 8100  | 0.5947          | 0.7317   |
| 0.5811        | 37.0  | 8325  | 0.5820          | 0.7383   |
| 0.5642        | 38.0  | 8550  | 0.5685          | 0.7433   |
| 0.5332        | 39.0  | 8775  | 0.5891          | 0.745    |
| 0.5278        | 40.0  | 9000  | 0.5919          | 0.7283   |
| 0.5007        | 41.0  | 9225  | 0.5742          | 0.7567   |
| 0.5377        | 42.0  | 9450  | 0.5885          | 0.76     |
| 0.4913        | 43.0  | 9675  | 0.5649          | 0.755    |
| 0.5315        | 44.0  | 9900  | 0.5703          | 0.74     |
| 0.4857        | 45.0  | 10125 | 0.5619          | 0.765    |
| 0.4747        | 46.0  | 10350 | 0.5832          | 0.7533   |
| 0.5553        | 47.0  | 10575 | 0.5734          | 0.755    |
| 0.452         | 48.0  | 10800 | 0.5866          | 0.7617   |
| 0.4761        | 49.0  | 11025 | 0.5792          | 0.7567   |
| 0.4755        | 50.0  | 11250 | 0.5835          | 0.76     |


### 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