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# smids_3x_deit_tiny_rms_00001_fold1

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: 0.9365
- Accuracy: 0.8831

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3997        | 1.0   | 226   | 0.3347          | 0.8631   |
| 0.255         | 2.0   | 452   | 0.2918          | 0.8831   |
| 0.1424        | 3.0   | 678   | 0.3193          | 0.8748   |
| 0.1606        | 4.0   | 904   | 0.3236          | 0.8865   |
| 0.1532        | 5.0   | 1130  | 0.3062          | 0.8915   |
| 0.0588        | 6.0   | 1356  | 0.4276          | 0.8915   |
| 0.0136        | 7.0   | 1582  | 0.4629          | 0.8831   |
| 0.0352        | 8.0   | 1808  | 0.5602          | 0.8765   |
| 0.0656        | 9.0   | 2034  | 0.5379          | 0.8765   |
| 0.0146        | 10.0  | 2260  | 0.6661          | 0.8881   |
| 0.0002        | 11.0  | 2486  | 0.7507          | 0.8798   |
| 0.015         | 12.0  | 2712  | 0.6981          | 0.8865   |
| 0.0138        | 13.0  | 2938  | 0.9249          | 0.8715   |
| 0.0124        | 14.0  | 3164  | 0.8454          | 0.8748   |
| 0.0002        | 15.0  | 3390  | 0.8233          | 0.8781   |
| 0.0006        | 16.0  | 3616  | 0.8574          | 0.8698   |
| 0.0171        | 17.0  | 3842  | 0.8765          | 0.8781   |
| 0.0           | 18.0  | 4068  | 0.8826          | 0.8865   |
| 0.0173        | 19.0  | 4294  | 0.7556          | 0.8932   |
| 0.0158        | 20.0  | 4520  | 0.9424          | 0.8748   |
| 0.0001        | 21.0  | 4746  | 1.0298          | 0.8648   |
| 0.0133        | 22.0  | 4972  | 0.9420          | 0.8664   |
| 0.0145        | 23.0  | 5198  | 0.8626          | 0.8865   |
| 0.0001        | 24.0  | 5424  | 0.9250          | 0.8781   |
| 0.0           | 25.0  | 5650  | 0.8112          | 0.8948   |
| 0.0002        | 26.0  | 5876  | 0.8569          | 0.8898   |
| 0.0           | 27.0  | 6102  | 0.8070          | 0.8915   |
| 0.0           | 28.0  | 6328  | 0.8507          | 0.8765   |
| 0.0           | 29.0  | 6554  | 0.8437          | 0.8932   |
| 0.0           | 30.0  | 6780  | 0.8816          | 0.8848   |
| 0.0           | 31.0  | 7006  | 0.8733          | 0.8848   |
| 0.0           | 32.0  | 7232  | 0.9948          | 0.8681   |
| 0.0082        | 33.0  | 7458  | 0.9148          | 0.8831   |
| 0.0           | 34.0  | 7684  | 0.9131          | 0.8881   |
| 0.0           | 35.0  | 7910  | 0.9403          | 0.8781   |
| 0.0           | 36.0  | 8136  | 0.9014          | 0.8765   |
| 0.0           | 37.0  | 8362  | 0.9056          | 0.8798   |
| 0.0           | 38.0  | 8588  | 0.9375          | 0.8781   |
| 0.0           | 39.0  | 8814  | 0.9025          | 0.8831   |
| 0.0           | 40.0  | 9040  | 0.9205          | 0.8815   |
| 0.0051        | 41.0  | 9266  | 0.9089          | 0.8848   |
| 0.0025        | 42.0  | 9492  | 0.9223          | 0.8848   |
| 0.0           | 43.0  | 9718  | 0.9136          | 0.8881   |
| 0.0           | 44.0  | 9944  | 0.9207          | 0.8848   |
| 0.0           | 45.0  | 10170 | 0.9266          | 0.8831   |
| 0.0           | 46.0  | 10396 | 0.9325          | 0.8865   |
| 0.0           | 47.0  | 10622 | 0.9382          | 0.8815   |
| 0.0           | 48.0  | 10848 | 0.9372          | 0.8815   |
| 0.0           | 49.0  | 11074 | 0.9372          | 0.8831   |
| 0.0           | 50.0  | 11300 | 0.9365          | 0.8831   |


### 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