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# smids_3x_deit_small_rms_0001_fold1

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: 0.8635
- Accuracy: 0.9065

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3954        | 1.0   | 226   | 0.3893          | 0.8280   |
| 0.2465        | 2.0   | 452   | 0.3428          | 0.8881   |
| 0.1728        | 3.0   | 678   | 0.3745          | 0.8831   |
| 0.1347        | 4.0   | 904   | 0.4194          | 0.8848   |
| 0.0709        | 5.0   | 1130  | 0.4431          | 0.8898   |
| 0.0807        | 6.0   | 1356  | 0.4957          | 0.8781   |
| 0.0722        | 7.0   | 1582  | 0.4748          | 0.8898   |
| 0.036         | 8.0   | 1808  | 0.6178          | 0.8831   |
| 0.0808        | 9.0   | 2034  | 0.5850          | 0.8831   |
| 0.0404        | 10.0  | 2260  | 0.5350          | 0.9015   |
| 0.016         | 11.0  | 2486  | 0.5574          | 0.8831   |
| 0.0147        | 12.0  | 2712  | 0.5709          | 0.8865   |
| 0.0113        | 13.0  | 2938  | 0.6888          | 0.8815   |
| 0.0209        | 14.0  | 3164  | 0.4757          | 0.9149   |
| 0.0245        | 15.0  | 3390  | 0.6913          | 0.8815   |
| 0.0203        | 16.0  | 3616  | 0.6653          | 0.8865   |
| 0.0109        | 17.0  | 3842  | 0.7353          | 0.8898   |
| 0.0341        | 18.0  | 4068  | 0.7660          | 0.8865   |
| 0.0053        | 19.0  | 4294  | 0.6013          | 0.8965   |
| 0.0015        | 20.0  | 4520  | 0.6073          | 0.8965   |
| 0.0003        | 21.0  | 4746  | 0.7366          | 0.8965   |
| 0.0274        | 22.0  | 4972  | 0.7587          | 0.8798   |
| 0.0019        | 23.0  | 5198  | 0.6702          | 0.8998   |
| 0.0001        | 24.0  | 5424  | 0.7767          | 0.8815   |
| 0.0008        | 25.0  | 5650  | 0.6634          | 0.8998   |
| 0.023         | 26.0  | 5876  | 0.7380          | 0.8915   |
| 0.0           | 27.0  | 6102  | 0.8025          | 0.8898   |
| 0.0797        | 28.0  | 6328  | 0.7171          | 0.8948   |
| 0.0492        | 29.0  | 6554  | 0.6827          | 0.8982   |
| 0.0           | 30.0  | 6780  | 0.7690          | 0.9048   |
| 0.0           | 31.0  | 7006  | 0.7411          | 0.9048   |
| 0.0           | 32.0  | 7232  | 0.7425          | 0.8965   |
| 0.0032        | 33.0  | 7458  | 0.7178          | 0.9115   |
| 0.0006        | 34.0  | 7684  | 0.7893          | 0.9082   |
| 0.0           | 35.0  | 7910  | 0.8185          | 0.8932   |
| 0.0181        | 36.0  | 8136  | 0.8745          | 0.8932   |
| 0.0003        | 37.0  | 8362  | 0.8672          | 0.8932   |
| 0.0           | 38.0  | 8588  | 0.8314          | 0.8982   |
| 0.0           | 39.0  | 8814  | 0.8333          | 0.8965   |
| 0.0           | 40.0  | 9040  | 0.7854          | 0.9065   |
| 0.0036        | 41.0  | 9266  | 0.8828          | 0.9015   |
| 0.0028        | 42.0  | 9492  | 0.8402          | 0.9065   |
| 0.0           | 43.0  | 9718  | 0.8689          | 0.8982   |
| 0.0           | 44.0  | 9944  | 0.8390          | 0.9065   |
| 0.0           | 45.0  | 10170 | 0.8434          | 0.9082   |
| 0.0           | 46.0  | 10396 | 0.8531          | 0.9132   |
| 0.0           | 47.0  | 10622 | 0.8589          | 0.9098   |
| 0.0           | 48.0  | 10848 | 0.8632          | 0.9065   |
| 0.0           | 49.0  | 11074 | 0.8630          | 0.9065   |
| 0.0           | 50.0  | 11300 | 0.8635          | 0.9065   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

abnormal_sperm non-sperm normal_sperm