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# smids_3x_deit_tiny_rms_001_fold4

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: 2.2714
- Accuracy: 0.7733

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8968        | 1.0   | 225   | 0.7639          | 0.6083   |
| 0.8626        | 2.0   | 450   | 0.7483          | 0.6217   |
| 0.7883        | 3.0   | 675   | 0.8572          | 0.525    |
| 0.7849        | 4.0   | 900   | 1.5683          | 0.415    |
| 0.8414        | 5.0   | 1125  | 0.7843          | 0.605    |
| 0.7458        | 6.0   | 1350  | 0.6775          | 0.6783   |
| 0.7372        | 7.0   | 1575  | 0.7834          | 0.63     |
| 0.7187        | 8.0   | 1800  | 0.6584          | 0.6983   |
| 0.6863        | 9.0   | 2025  | 0.6503          | 0.6817   |
| 0.6631        | 10.0  | 2250  | 0.6660          | 0.6967   |
| 0.6649        | 11.0  | 2475  | 0.6379          | 0.725    |
| 0.6942        | 12.0  | 2700  | 0.6307          | 0.7233   |
| 0.6966        | 13.0  | 2925  | 0.6415          | 0.735    |
| 0.5939        | 14.0  | 3150  | 0.6031          | 0.7383   |
| 0.6282        | 15.0  | 3375  | 0.6088          | 0.7217   |
| 0.602         | 16.0  | 3600  | 0.6243          | 0.7367   |
| 0.5204        | 17.0  | 3825  | 0.6588          | 0.7317   |
| 0.5969        | 18.0  | 4050  | 0.5604          | 0.7533   |
| 0.5806        | 19.0  | 4275  | 0.5450          | 0.7517   |
| 0.5391        | 20.0  | 4500  | 0.5377          | 0.7517   |
| 0.5038        | 21.0  | 4725  | 0.5743          | 0.7633   |
| 0.5713        | 22.0  | 4950  | 0.5280          | 0.7683   |
| 0.5554        | 23.0  | 5175  | 0.6486          | 0.74     |
| 0.5176        | 24.0  | 5400  | 0.5324          | 0.775    |
| 0.495         | 25.0  | 5625  | 0.5436          | 0.7867   |
| 0.5361        | 26.0  | 5850  | 0.5289          | 0.7717   |
| 0.4994        | 27.0  | 6075  | 0.5964          | 0.765    |
| 0.4408        | 28.0  | 6300  | 0.5977          | 0.755    |
| 0.4143        | 29.0  | 6525  | 0.5955          | 0.77     |
| 0.4019        | 30.0  | 6750  | 0.5477          | 0.79     |
| 0.3202        | 31.0  | 6975  | 0.6202          | 0.7733   |
| 0.2909        | 32.0  | 7200  | 0.6279          | 0.7667   |
| 0.3341        | 33.0  | 7425  | 0.6209          | 0.7933   |
| 0.3201        | 34.0  | 7650  | 0.6151          | 0.7883   |
| 0.3077        | 35.0  | 7875  | 0.6910          | 0.79     |
| 0.2136        | 36.0  | 8100  | 0.8074          | 0.7567   |
| 0.1784        | 37.0  | 8325  | 0.7942          | 0.785    |
| 0.2068        | 38.0  | 8550  | 0.8891          | 0.78     |
| 0.1962        | 39.0  | 8775  | 0.8894          | 0.775    |
| 0.1531        | 40.0  | 9000  | 1.1161          | 0.7783   |
| 0.0708        | 41.0  | 9225  | 1.1305          | 0.7867   |
| 0.0729        | 42.0  | 9450  | 1.2984          | 0.755    |
| 0.0781        | 43.0  | 9675  | 1.3627          | 0.7867   |
| 0.0522        | 44.0  | 9900  | 1.6222          | 0.7683   |
| 0.0281        | 45.0  | 10125 | 1.8871          | 0.7683   |
| 0.0088        | 46.0  | 10350 | 2.0271          | 0.7767   |
| 0.0017        | 47.0  | 10575 | 2.0484          | 0.7717   |
| 0.002         | 48.0  | 10800 | 2.2088          | 0.7767   |
| 0.0003        | 49.0  | 11025 | 2.2142          | 0.7717   |
| 0.0001        | 50.0  | 11250 | 2.2714          | 0.7733   |


### 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