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# smids_3x_deit_tiny_rms_0001_fold5

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: 0.9250
- Accuracy: 0.8933

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4046        | 1.0   | 225   | 0.3353          | 0.855    |
| 0.2238        | 2.0   | 450   | 0.2977          | 0.8967   |
| 0.2029        | 3.0   | 675   | 0.3292          | 0.8717   |
| 0.1861        | 4.0   | 900   | 0.3918          | 0.8633   |
| 0.097         | 5.0   | 1125  | 0.5728          | 0.87     |
| 0.0763        | 6.0   | 1350  | 0.3602          | 0.8867   |
| 0.1211        | 7.0   | 1575  | 0.3953          | 0.9067   |
| 0.0628        | 8.0   | 1800  | 0.5619          | 0.8917   |
| 0.1484        | 9.0   | 2025  | 0.5750          | 0.88     |
| 0.0452        | 10.0  | 2250  | 0.6659          | 0.89     |
| 0.0229        | 11.0  | 2475  | 0.6256          | 0.8933   |
| 0.0617        | 12.0  | 2700  | 0.7075          | 0.87     |
| 0.0553        | 13.0  | 2925  | 0.6972          | 0.8983   |
| 0.0308        | 14.0  | 3150  | 0.6494          | 0.8983   |
| 0.0312        | 15.0  | 3375  | 0.6866          | 0.9      |
| 0.011         | 16.0  | 3600  | 0.7253          | 0.895    |
| 0.0983        | 17.0  | 3825  | 0.7035          | 0.8933   |
| 0.0451        | 18.0  | 4050  | 0.8265          | 0.8933   |
| 0.0418        | 19.0  | 4275  | 0.8696          | 0.8767   |
| 0.0469        | 20.0  | 4500  | 0.6273          | 0.9133   |
| 0.0203        | 21.0  | 4725  | 0.7939          | 0.895    |
| 0.0102        | 22.0  | 4950  | 0.7226          | 0.8967   |
| 0.0005        | 23.0  | 5175  | 0.8708          | 0.8933   |
| 0.0229        | 24.0  | 5400  | 0.9025          | 0.89     |
| 0.0344        | 25.0  | 5625  | 0.7685          | 0.9033   |
| 0.0016        | 26.0  | 5850  | 0.7805          | 0.9067   |
| 0.0048        | 27.0  | 6075  | 0.7684          | 0.9033   |
| 0.0028        | 28.0  | 6300  | 0.8595          | 0.8933   |
| 0.0098        | 29.0  | 6525  | 0.8847          | 0.8983   |
| 0.0002        | 30.0  | 6750  | 0.8488          | 0.8917   |
| 0.0           | 31.0  | 6975  | 0.9022          | 0.8883   |
| 0.0           | 32.0  | 7200  | 0.8024          | 0.895    |
| 0.0047        | 33.0  | 7425  | 0.8208          | 0.8933   |
| 0.0001        | 34.0  | 7650  | 0.9019          | 0.9017   |
| 0.0033        | 35.0  | 7875  | 0.8774          | 0.8883   |
| 0.0           | 36.0  | 8100  | 0.8642          | 0.885    |
| 0.0189        | 37.0  | 8325  | 0.8309          | 0.8983   |
| 0.0           | 38.0  | 8550  | 0.9322          | 0.89     |
| 0.0           | 39.0  | 8775  | 0.9453          | 0.8933   |
| 0.0           | 40.0  | 9000  | 0.9411          | 0.89     |
| 0.0           | 41.0  | 9225  | 0.9468          | 0.8917   |
| 0.0           | 42.0  | 9450  | 0.9584          | 0.8967   |
| 0.003         | 43.0  | 9675  | 0.9469          | 0.8917   |
| 0.0           | 44.0  | 9900  | 0.9339          | 0.8917   |
| 0.0           | 45.0  | 10125 | 0.9259          | 0.89     |
| 0.0           | 46.0  | 10350 | 0.9294          | 0.8917   |
| 0.0           | 47.0  | 10575 | 0.9214          | 0.8917   |
| 0.0           | 48.0  | 10800 | 0.9235          | 0.8917   |
| 0.0           | 49.0  | 11025 | 0.9243          | 0.8933   |
| 0.0           | 50.0  | 11250 | 0.9250          | 0.8933   |


### 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