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# smids_10x_deit_small_rms_00001_fold2

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: 1.1223
- Accuracy: 0.8952

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2125        | 1.0   | 750   | 0.2971          | 0.8636   |
| 0.1007        | 2.0   | 1500  | 0.3569          | 0.8902   |
| 0.033         | 3.0   | 2250  | 0.4786          | 0.8852   |
| 0.0414        | 4.0   | 3000  | 0.6308          | 0.8719   |
| 0.0169        | 5.0   | 3750  | 0.7881          | 0.8769   |
| 0.0209        | 6.0   | 4500  | 0.8756          | 0.8802   |
| 0.0232        | 7.0   | 5250  | 0.7942          | 0.8785   |
| 0.0001        | 8.0   | 6000  | 0.8024          | 0.8885   |
| 0.0037        | 9.0   | 6750  | 0.9766          | 0.8852   |
| 0.0663        | 10.0  | 7500  | 0.9288          | 0.8785   |
| 0.0416        | 11.0  | 8250  | 1.0051          | 0.8835   |
| 0.0257        | 12.0  | 9000  | 1.1036          | 0.8752   |
| 0.0003        | 13.0  | 9750  | 0.9284          | 0.8835   |
| 0.0007        | 14.0  | 10500 | 0.9766          | 0.8752   |
| 0.0009        | 15.0  | 11250 | 1.0060          | 0.8869   |
| 0.024         | 16.0  | 12000 | 0.9566          | 0.8918   |
| 0.0002        | 17.0  | 12750 | 0.9308          | 0.8985   |
| 0.0226        | 18.0  | 13500 | 0.9878          | 0.8952   |
| 0.0002        | 19.0  | 14250 | 1.0344          | 0.8802   |
| 0.0           | 20.0  | 15000 | 1.0012          | 0.8902   |
| 0.0           | 21.0  | 15750 | 1.0757          | 0.8852   |
| 0.0197        | 22.0  | 16500 | 1.1327          | 0.8918   |
| 0.0059        | 23.0  | 17250 | 1.1959          | 0.8785   |
| 0.014         | 24.0  | 18000 | 0.9244          | 0.8918   |
| 0.0           | 25.0  | 18750 | 1.0134          | 0.8952   |
| 0.0001        | 26.0  | 19500 | 1.2273          | 0.8735   |
| 0.0081        | 27.0  | 20250 | 1.2216          | 0.8735   |
| 0.0           | 28.0  | 21000 | 1.1304          | 0.8769   |
| 0.0           | 29.0  | 21750 | 0.9499          | 0.8902   |
| 0.0           | 30.0  | 22500 | 1.0368          | 0.8885   |
| 0.0           | 31.0  | 23250 | 1.0392          | 0.8852   |
| 0.0038        | 32.0  | 24000 | 1.2288          | 0.8835   |
| 0.0           | 33.0  | 24750 | 1.1678          | 0.8952   |
| 0.0           | 34.0  | 25500 | 1.0162          | 0.8918   |
| 0.0           | 35.0  | 26250 | 1.0770          | 0.8918   |
| 0.0           | 36.0  | 27000 | 1.0678          | 0.8902   |
| 0.0067        | 37.0  | 27750 | 1.0739          | 0.8935   |
| 0.0           | 38.0  | 28500 | 1.1577          | 0.8935   |
| 0.0           | 39.0  | 29250 | 1.1277          | 0.8935   |
| 0.0           | 40.0  | 30000 | 1.1396          | 0.8918   |
| 0.0           | 41.0  | 30750 | 1.1296          | 0.8952   |
| 0.0           | 42.0  | 31500 | 1.1324          | 0.8935   |
| 0.0           | 43.0  | 32250 | 1.1390          | 0.8918   |
| 0.0           | 44.0  | 33000 | 1.1307          | 0.8952   |
| 0.0025        | 45.0  | 33750 | 1.1302          | 0.8918   |
| 0.0           | 46.0  | 34500 | 1.1293          | 0.8935   |
| 0.0           | 47.0  | 35250 | 1.1264          | 0.8935   |
| 0.0           | 48.0  | 36000 | 1.1267          | 0.8952   |
| 0.0           | 49.0  | 36750 | 1.1233          | 0.8952   |
| 0.0           | 50.0  | 37500 | 1.1223          | 0.8952   |


### 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