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# smids_5x_deit_small_sgd_0001_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: 0.5006
- Accuracy: 0.8037

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0629        | 1.0   | 375   | 1.0383          | 0.4592   |
| 1.0151        | 2.0   | 750   | 1.0009          | 0.4925   |
| 0.9588        | 3.0   | 1125  | 0.9619          | 0.5574   |
| 0.924         | 4.0   | 1500  | 0.9255          | 0.5890   |
| 0.8743        | 5.0   | 1875  | 0.8899          | 0.6290   |
| 0.8177        | 6.0   | 2250  | 0.8563          | 0.6522   |
| 0.7888        | 7.0   | 2625  | 0.8262          | 0.6755   |
| 0.7921        | 8.0   | 3000  | 0.7964          | 0.7005   |
| 0.7372        | 9.0   | 3375  | 0.7699          | 0.7138   |
| 0.7291        | 10.0  | 3750  | 0.7453          | 0.7221   |
| 0.7295        | 11.0  | 4125  | 0.7221          | 0.7255   |
| 0.6995        | 12.0  | 4500  | 0.7007          | 0.7288   |
| 0.621         | 13.0  | 4875  | 0.6811          | 0.7388   |
| 0.6398        | 14.0  | 5250  | 0.6638          | 0.7504   |
| 0.6383        | 15.0  | 5625  | 0.6483          | 0.7587   |
| 0.5747        | 16.0  | 6000  | 0.6341          | 0.7587   |
| 0.6097        | 17.0  | 6375  | 0.6214          | 0.7604   |
| 0.594         | 18.0  | 6750  | 0.6099          | 0.7604   |
| 0.5533        | 19.0  | 7125  | 0.5997          | 0.7654   |
| 0.5984        | 20.0  | 7500  | 0.5904          | 0.7687   |
| 0.5406        | 21.0  | 7875  | 0.5822          | 0.7720   |
| 0.525         | 22.0  | 8250  | 0.5743          | 0.7704   |
| 0.5434        | 23.0  | 8625  | 0.5673          | 0.7720   |
| 0.5253        | 24.0  | 9000  | 0.5609          | 0.7737   |
| 0.5143        | 25.0  | 9375  | 0.5549          | 0.7754   |
| 0.5351        | 26.0  | 9750  | 0.5494          | 0.7787   |
| 0.5716        | 27.0  | 10125 | 0.5444          | 0.7787   |
| 0.4849        | 28.0  | 10500 | 0.5399          | 0.7820   |
| 0.4878        | 29.0  | 10875 | 0.5357          | 0.7887   |
| 0.4887        | 30.0  | 11250 | 0.5319          | 0.7920   |
| 0.4866        | 31.0  | 11625 | 0.5283          | 0.7920   |
| 0.5025        | 32.0  | 12000 | 0.5250          | 0.7937   |
| 0.4672        | 33.0  | 12375 | 0.5219          | 0.7903   |
| 0.4395        | 34.0  | 12750 | 0.5192          | 0.7887   |
| 0.473         | 35.0  | 13125 | 0.5166          | 0.7920   |
| 0.4458        | 36.0  | 13500 | 0.5143          | 0.7920   |
| 0.4639        | 37.0  | 13875 | 0.5122          | 0.7937   |
| 0.4488        | 38.0  | 14250 | 0.5103          | 0.7953   |
| 0.4766        | 39.0  | 14625 | 0.5086          | 0.7970   |
| 0.4603        | 40.0  | 15000 | 0.5071          | 0.7987   |
| 0.4461        | 41.0  | 15375 | 0.5058          | 0.8003   |
| 0.4671        | 42.0  | 15750 | 0.5046          | 0.8003   |
| 0.4415        | 43.0  | 16125 | 0.5036          | 0.8020   |
| 0.4496        | 44.0  | 16500 | 0.5027          | 0.8020   |
| 0.4327        | 45.0  | 16875 | 0.5020          | 0.8020   |
| 0.5062        | 46.0  | 17250 | 0.5015          | 0.8020   |
| 0.4692        | 47.0  | 17625 | 0.5010          | 0.8037   |
| 0.426         | 48.0  | 18000 | 0.5008          | 0.8037   |
| 0.518         | 49.0  | 18375 | 0.5006          | 0.8037   |
| 0.4765        | 50.0  | 18750 | 0.5006          | 0.8037   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm