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# smids_10x_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: 1.0119
- Accuracy: 0.9015

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3304        | 1.0   | 751   | 0.3159          | 0.8881   |
| 0.1492        | 2.0   | 1502  | 0.3656          | 0.8798   |
| 0.137         | 3.0   | 2253  | 0.4619          | 0.8831   |
| 0.0713        | 4.0   | 3004  | 0.5428          | 0.8798   |
| 0.0396        | 5.0   | 3755  | 0.4807          | 0.9098   |
| 0.041         | 6.0   | 4506  | 0.6616          | 0.8881   |
| 0.0505        | 7.0   | 5257  | 0.6229          | 0.9032   |
| 0.016         | 8.0   | 6008  | 0.6188          | 0.8865   |
| 0.0115        | 9.0   | 6759  | 0.6016          | 0.8915   |
| 0.0369        | 10.0  | 7510  | 0.5294          | 0.8965   |
| 0.0298        | 11.0  | 8261  | 0.5896          | 0.8865   |
| 0.0282        | 12.0  | 9012  | 0.6477          | 0.8815   |
| 0.0159        | 13.0  | 9763  | 0.5597          | 0.9032   |
| 0.0145        | 14.0  | 10514 | 0.5006          | 0.9032   |
| 0.0274        | 15.0  | 11265 | 0.6365          | 0.8932   |
| 0.0233        | 16.0  | 12016 | 0.5679          | 0.9032   |
| 0.0005        | 17.0  | 12767 | 0.7523          | 0.8815   |
| 0.0253        | 18.0  | 13518 | 0.6785          | 0.8915   |
| 0.0256        | 19.0  | 14269 | 0.6246          | 0.8815   |
| 0.0099        | 20.0  | 15020 | 0.6349          | 0.9082   |
| 0.0038        | 21.0  | 15771 | 0.5512          | 0.9115   |
| 0.0001        | 22.0  | 16522 | 0.6204          | 0.9032   |
| 0.0062        | 23.0  | 17273 | 0.7652          | 0.8932   |
| 0.0           | 24.0  | 18024 | 0.6455          | 0.9048   |
| 0.0           | 25.0  | 18775 | 0.8288          | 0.8932   |
| 0.0004        | 26.0  | 19526 | 0.7865          | 0.8982   |
| 0.0072        | 27.0  | 20277 | 0.8381          | 0.8965   |
| 0.0124        | 28.0  | 21028 | 0.6706          | 0.9082   |
| 0.0027        | 29.0  | 21779 | 0.7345          | 0.9115   |
| 0.0001        | 30.0  | 22530 | 0.8086          | 0.9032   |
| 0.0007        | 31.0  | 23281 | 0.9133          | 0.8948   |
| 0.0           | 32.0  | 24032 | 0.9315          | 0.8948   |
| 0.0005        | 33.0  | 24783 | 0.9041          | 0.8865   |
| 0.0002        | 34.0  | 25534 | 0.7984          | 0.9082   |
| 0.0           | 35.0  | 26285 | 0.7336          | 0.9199   |
| 0.0           | 36.0  | 27036 | 0.7739          | 0.9082   |
| 0.0           | 37.0  | 27787 | 0.7374          | 0.9149   |
| 0.0           | 38.0  | 28538 | 0.8856          | 0.8998   |
| 0.0           | 39.0  | 29289 | 0.7863          | 0.9115   |
| 0.0           | 40.0  | 30040 | 0.8280          | 0.9065   |
| 0.0           | 41.0  | 30791 | 0.8525          | 0.9065   |
| 0.0           | 42.0  | 31542 | 0.8579          | 0.9015   |
| 0.0           | 43.0  | 32293 | 0.9128          | 0.9048   |
| 0.0           | 44.0  | 33044 | 0.9440          | 0.8998   |
| 0.0           | 45.0  | 33795 | 0.9799          | 0.8998   |
| 0.0           | 46.0  | 34546 | 0.9704          | 0.9015   |
| 0.0           | 47.0  | 35297 | 0.9969          | 0.9015   |
| 0.0           | 48.0  | 36048 | 1.0043          | 0.9015   |
| 0.0           | 49.0  | 36799 | 1.0140          | 0.8998   |
| 0.0           | 50.0  | 37550 | 1.0119          | 0.9015   |


### 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