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# smids_3x_deit_tiny_sgd_0001_fold3

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6313
- Accuracy: 0.7683

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2114        | 1.0   | 225   | 1.2115          | 0.3783   |
| 1.0735        | 2.0   | 450   | 1.1384          | 0.3983   |
| 1.097         | 3.0   | 675   | 1.1002          | 0.4133   |
| 1.0469        | 4.0   | 900   | 1.0702          | 0.4533   |
| 1.0229        | 5.0   | 1125  | 1.0448          | 0.48     |
| 0.99          | 6.0   | 1350  | 1.0213          | 0.5      |
| 0.9781        | 7.0   | 1575  | 0.9993          | 0.5117   |
| 0.9907        | 8.0   | 1800  | 0.9784          | 0.54     |
| 0.927         | 9.0   | 2025  | 0.9582          | 0.545    |
| 0.8847        | 10.0  | 2250  | 0.9391          | 0.5583   |
| 0.9329        | 11.0  | 2475  | 0.9207          | 0.5733   |
| 0.8984        | 12.0  | 2700  | 0.9031          | 0.59     |
| 0.8494        | 13.0  | 2925  | 0.8859          | 0.605    |
| 0.8194        | 14.0  | 3150  | 0.8694          | 0.6183   |
| 0.7869        | 15.0  | 3375  | 0.8536          | 0.6283   |
| 0.8309        | 16.0  | 3600  | 0.8389          | 0.635    |
| 0.7966        | 17.0  | 3825  | 0.8246          | 0.64     |
| 0.8108        | 18.0  | 4050  | 0.8113          | 0.64     |
| 0.801         | 19.0  | 4275  | 0.7985          | 0.6533   |
| 0.771         | 20.0  | 4500  | 0.7864          | 0.66     |
| 0.7097        | 21.0  | 4725  | 0.7747          | 0.67     |
| 0.7109        | 22.0  | 4950  | 0.7636          | 0.6767   |
| 0.7079        | 23.0  | 5175  | 0.7529          | 0.6867   |
| 0.7294        | 24.0  | 5400  | 0.7431          | 0.69     |
| 0.7458        | 25.0  | 5625  | 0.7335          | 0.6883   |
| 0.6793        | 26.0  | 5850  | 0.7246          | 0.6917   |
| 0.6665        | 27.0  | 6075  | 0.7159          | 0.7017   |
| 0.6522        | 28.0  | 6300  | 0.7080          | 0.7083   |
| 0.7013        | 29.0  | 6525  | 0.7004          | 0.715    |
| 0.6636        | 30.0  | 6750  | 0.6932          | 0.7183   |
| 0.6224        | 31.0  | 6975  | 0.6867          | 0.72     |
| 0.6822        | 32.0  | 7200  | 0.6803          | 0.725    |
| 0.6885        | 33.0  | 7425  | 0.6745          | 0.7283   |
| 0.6623        | 34.0  | 7650  | 0.6692          | 0.7333   |
| 0.6059        | 35.0  | 7875  | 0.6642          | 0.735    |
| 0.6546        | 36.0  | 8100  | 0.6598          | 0.7417   |
| 0.6233        | 37.0  | 8325  | 0.6556          | 0.7433   |
| 0.6474        | 38.0  | 8550  | 0.6519          | 0.7467   |
| 0.606         | 39.0  | 8775  | 0.6483          | 0.75     |
| 0.6243        | 40.0  | 9000  | 0.6453          | 0.755    |
| 0.6167        | 41.0  | 9225  | 0.6425          | 0.7567   |
| 0.6518        | 42.0  | 9450  | 0.6401          | 0.7617   |
| 0.5844        | 43.0  | 9675  | 0.6380          | 0.7633   |
| 0.6425        | 44.0  | 9900  | 0.6361          | 0.7633   |
| 0.6354        | 45.0  | 10125 | 0.6346          | 0.7633   |
| 0.5465        | 46.0  | 10350 | 0.6333          | 0.765    |
| 0.6036        | 47.0  | 10575 | 0.6324          | 0.7667   |
| 0.5553        | 48.0  | 10800 | 0.6318          | 0.7683   |
| 0.6342        | 49.0  | 11025 | 0.6314          | 0.7683   |
| 0.5635        | 50.0  | 11250 | 0.6313          | 0.7683   |


### 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