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# smids_3x_deit_small_rms_001_fold4

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: 1.1078
- Accuracy: 0.7533

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1005        | 1.0   | 225   | 0.9456          | 0.5067   |
| 0.8365        | 2.0   | 450   | 0.8727          | 0.46     |
| 0.7918        | 3.0   | 675   | 0.7967          | 0.555    |
| 0.7924        | 4.0   | 900   | 0.7472          | 0.62     |
| 0.8029        | 5.0   | 1125  | 0.7002          | 0.6433   |
| 0.7311        | 6.0   | 1350  | 0.6514          | 0.685    |
| 0.6939        | 7.0   | 1575  | 0.6643          | 0.7133   |
| 0.6634        | 8.0   | 1800  | 0.6358          | 0.715    |
| 0.6019        | 9.0   | 2025  | 0.6144          | 0.7067   |
| 0.6316        | 10.0  | 2250  | 0.6281          | 0.6967   |
| 0.6363        | 11.0  | 2475  | 0.6280          | 0.7233   |
| 0.645         | 12.0  | 2700  | 0.5846          | 0.7433   |
| 0.6675        | 13.0  | 2925  | 0.5877          | 0.7333   |
| 0.5165        | 14.0  | 3150  | 0.5728          | 0.75     |
| 0.52          | 15.0  | 3375  | 0.6706          | 0.73     |
| 0.5715        | 16.0  | 3600  | 0.6345          | 0.6983   |
| 0.4691        | 17.0  | 3825  | 0.6573          | 0.7167   |
| 0.5674        | 18.0  | 4050  | 0.5616          | 0.7633   |
| 0.5252        | 19.0  | 4275  | 0.5645          | 0.755    |
| 0.4602        | 20.0  | 4500  | 0.6017          | 0.755    |
| 0.4817        | 21.0  | 4725  | 0.5934          | 0.73     |
| 0.5616        | 22.0  | 4950  | 0.5845          | 0.7583   |
| 0.4883        | 23.0  | 5175  | 0.6144          | 0.7483   |
| 0.4781        | 24.0  | 5400  | 0.6001          | 0.7517   |
| 0.4229        | 25.0  | 5625  | 0.5953          | 0.77     |
| 0.4499        | 26.0  | 5850  | 0.6184          | 0.7767   |
| 0.4522        | 27.0  | 6075  | 0.6104          | 0.7433   |
| 0.4849        | 28.0  | 6300  | 0.6429          | 0.745    |
| 0.3967        | 29.0  | 6525  | 0.6127          | 0.7533   |
| 0.429         | 30.0  | 6750  | 0.6125          | 0.775    |
| 0.3969        | 31.0  | 6975  | 0.6078          | 0.7733   |
| 0.3935        | 32.0  | 7200  | 0.6333          | 0.7517   |
| 0.3619        | 33.0  | 7425  | 0.6571          | 0.795    |
| 0.4192        | 34.0  | 7650  | 0.6837          | 0.7833   |
| 0.4245        | 35.0  | 7875  | 0.6680          | 0.76     |
| 0.3704        | 36.0  | 8100  | 0.6540          | 0.78     |
| 0.393         | 37.0  | 8325  | 0.6566          | 0.7867   |
| 0.314         | 38.0  | 8550  | 0.7028          | 0.7783   |
| 0.3398        | 39.0  | 8775  | 0.7698          | 0.7667   |
| 0.3529        | 40.0  | 9000  | 0.7537          | 0.7633   |
| 0.3444        | 41.0  | 9225  | 0.8464          | 0.775    |
| 0.2933        | 42.0  | 9450  | 0.7973          | 0.7717   |
| 0.2384        | 43.0  | 9675  | 0.9278          | 0.765    |
| 0.3008        | 44.0  | 9900  | 0.9100          | 0.7767   |
| 0.2711        | 45.0  | 10125 | 0.9061          | 0.7683   |
| 0.3037        | 46.0  | 10350 | 0.9619          | 0.7783   |
| 0.2347        | 47.0  | 10575 | 1.0097          | 0.765    |
| 0.2672        | 48.0  | 10800 | 1.0493          | 0.75     |
| 0.2045        | 49.0  | 11025 | 1.0959          | 0.76     |
| 0.1823        | 50.0  | 11250 | 1.1078          | 0.7533   |


### 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