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# smids_3x_deit_base_rms_0001_fold2

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: 1.0815
- Accuracy: 0.8968

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3102        | 1.0   | 225   | 0.3377          | 0.8835   |
| 0.1924        | 2.0   | 450   | 0.3736          | 0.8502   |
| 0.1398        | 3.0   | 675   | 0.4023          | 0.8702   |
| 0.092         | 4.0   | 900   | 0.4813          | 0.8602   |
| 0.0716        | 5.0   | 1125  | 0.5407          | 0.8735   |
| 0.0363        | 6.0   | 1350  | 0.4732          | 0.8885   |
| 0.08          | 7.0   | 1575  | 0.7140          | 0.8636   |
| 0.0264        | 8.0   | 1800  | 0.5793          | 0.8935   |
| 0.0763        | 9.0   | 2025  | 0.7289          | 0.8735   |
| 0.0049        | 10.0  | 2250  | 0.6838          | 0.8835   |
| 0.0012        | 11.0  | 2475  | 0.8072          | 0.8719   |
| 0.0101        | 12.0  | 2700  | 0.5863          | 0.8918   |
| 0.0357        | 13.0  | 2925  | 0.5868          | 0.8735   |
| 0.0178        | 14.0  | 3150  | 0.5718          | 0.8918   |
| 0.0206        | 15.0  | 3375  | 0.6533          | 0.8802   |
| 0.0277        | 16.0  | 3600  | 0.7054          | 0.8785   |
| 0.0053        | 17.0  | 3825  | 0.6235          | 0.8752   |
| 0.007         | 18.0  | 4050  | 0.8905          | 0.8769   |
| 0.0143        | 19.0  | 4275  | 0.6956          | 0.8902   |
| 0.0002        | 20.0  | 4500  | 0.7774          | 0.8918   |
| 0.0108        | 21.0  | 4725  | 0.9943          | 0.8519   |
| 0.0012        | 22.0  | 4950  | 0.6337          | 0.8785   |
| 0.0038        | 23.0  | 5175  | 0.7133          | 0.8769   |
| 0.0262        | 24.0  | 5400  | 0.7303          | 0.8835   |
| 0.0085        | 25.0  | 5625  | 0.7888          | 0.8952   |
| 0.0003        | 26.0  | 5850  | 0.8473          | 0.8735   |
| 0.0002        | 27.0  | 6075  | 0.7966          | 0.8885   |
| 0.0019        | 28.0  | 6300  | 0.9703          | 0.8719   |
| 0.001         | 29.0  | 6525  | 0.7345          | 0.9002   |
| 0.0098        | 30.0  | 6750  | 0.9157          | 0.8869   |
| 0.0006        | 31.0  | 6975  | 0.9400          | 0.8719   |
| 0.0           | 32.0  | 7200  | 1.0399          | 0.8686   |
| 0.033         | 33.0  | 7425  | 0.9460          | 0.8769   |
| 0.0           | 34.0  | 7650  | 0.9321          | 0.8669   |
| 0.0           | 35.0  | 7875  | 0.9694          | 0.8819   |
| 0.0048        | 36.0  | 8100  | 0.8934          | 0.8802   |
| 0.0           | 37.0  | 8325  | 0.9996          | 0.8885   |
| 0.0           | 38.0  | 8550  | 0.9941          | 0.8835   |
| 0.0051        | 39.0  | 8775  | 1.0816          | 0.8719   |
| 0.0156        | 40.0  | 9000  | 1.1318          | 0.8819   |
| 0.0           | 41.0  | 9225  | 1.0295          | 0.8785   |
| 0.0           | 42.0  | 9450  | 1.0354          | 0.8852   |
| 0.0           | 43.0  | 9675  | 1.0603          | 0.8918   |
| 0.0           | 44.0  | 9900  | 1.0590          | 0.8902   |
| 0.0           | 45.0  | 10125 | 1.0556          | 0.8918   |
| 0.0           | 46.0  | 10350 | 1.0644          | 0.8935   |
| 0.0           | 47.0  | 10575 | 1.0611          | 0.8952   |
| 0.0           | 48.0  | 10800 | 1.0714          | 0.8952   |
| 0.0024        | 49.0  | 11025 | 1.0784          | 0.8952   |
| 0.0024        | 50.0  | 11250 | 1.0815          | 0.8968   |


### 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