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# smids_3x_deit_small_rms_001_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: 0.6403
- Accuracy: 0.7033

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1309        | 1.0   | 225   | 1.0865          | 0.4083   |
| 0.9745        | 2.0   | 450   | 0.8750          | 0.56     |
| 0.8495        | 3.0   | 675   | 0.8352          | 0.5333   |
| 0.841         | 4.0   | 900   | 0.7966          | 0.555    |
| 0.8226        | 5.0   | 1125  | 0.7921          | 0.6      |
| 0.853         | 6.0   | 1350  | 0.8095          | 0.5533   |
| 0.8653        | 7.0   | 1575  | 0.9709          | 0.5433   |
| 0.819         | 8.0   | 1800  | 0.7655          | 0.6383   |
| 0.785         | 9.0   | 2025  | 0.7896          | 0.625    |
| 0.8631        | 10.0  | 2250  | 0.8879          | 0.525    |
| 0.8663        | 11.0  | 2475  | 0.8802          | 0.535    |
| 0.7946        | 12.0  | 2700  | 0.8056          | 0.6      |
| 0.881         | 13.0  | 2925  | 0.8010          | 0.5733   |
| 0.8182        | 14.0  | 3150  | 0.7603          | 0.6483   |
| 0.7224        | 15.0  | 3375  | 0.7710          | 0.645    |
| 0.7369        | 16.0  | 3600  | 0.7559          | 0.615    |
| 0.7647        | 17.0  | 3825  | 0.7835          | 0.5983   |
| 0.7746        | 18.0  | 4050  | 0.7447          | 0.645    |
| 0.7626        | 19.0  | 4275  | 0.7290          | 0.66     |
| 0.7327        | 20.0  | 4500  | 0.7697          | 0.6333   |
| 0.7163        | 21.0  | 4725  | 0.7314          | 0.6433   |
| 0.8028        | 22.0  | 4950  | 0.7791          | 0.6      |
| 0.7667        | 23.0  | 5175  | 0.7694          | 0.64     |
| 0.7558        | 24.0  | 5400  | 0.7110          | 0.6567   |
| 0.7363        | 25.0  | 5625  | 0.7150          | 0.655    |
| 0.7997        | 26.0  | 5850  | 0.7280          | 0.6617   |
| 0.7362        | 27.0  | 6075  | 0.7643          | 0.625    |
| 0.7462        | 28.0  | 6300  | 0.7414          | 0.6233   |
| 0.7001        | 29.0  | 6525  | 0.6980          | 0.665    |
| 0.6874        | 30.0  | 6750  | 0.7253          | 0.6283   |
| 0.6271        | 31.0  | 6975  | 0.7291          | 0.6433   |
| 0.6551        | 32.0  | 7200  | 0.6785          | 0.6567   |
| 0.7073        | 33.0  | 7425  | 0.6937          | 0.6717   |
| 0.6958        | 34.0  | 7650  | 0.7523          | 0.6367   |
| 0.6565        | 35.0  | 7875  | 0.6954          | 0.6533   |
| 0.6367        | 36.0  | 8100  | 0.6638          | 0.6817   |
| 0.6915        | 37.0  | 8325  | 0.6412          | 0.7083   |
| 0.5928        | 38.0  | 8550  | 0.7119          | 0.6533   |
| 0.6501        | 39.0  | 8775  | 0.6232          | 0.7133   |
| 0.5902        | 40.0  | 9000  | 0.6501          | 0.71     |
| 0.6466        | 41.0  | 9225  | 0.6743          | 0.6683   |
| 0.7124        | 42.0  | 9450  | 0.7091          | 0.6783   |
| 0.6189        | 43.0  | 9675  | 0.6361          | 0.71     |
| 0.6514        | 44.0  | 9900  | 0.6443          | 0.7      |
| 0.6016        | 45.0  | 10125 | 0.6523          | 0.6883   |
| 0.5823        | 46.0  | 10350 | 0.6473          | 0.685    |
| 0.6451        | 47.0  | 10575 | 0.6348          | 0.69     |
| 0.5906        | 48.0  | 10800 | 0.6387          | 0.695    |
| 0.5662        | 49.0  | 11025 | 0.6330          | 0.7017   |
| 0.6218        | 50.0  | 11250 | 0.6403          | 0.7033   |


### 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