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# smids_3x_deit_tiny_adamax_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: 0.9289
- Accuracy: 0.8715

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.49          | 1.0   | 226   | 0.4492          | 0.8013   |
| 0.3613        | 2.0   | 452   | 0.3379          | 0.8614   |
| 0.2434        | 3.0   | 678   | 0.3074          | 0.8648   |
| 0.2553        | 4.0   | 904   | 0.3243          | 0.8648   |
| 0.2473        | 5.0   | 1130  | 0.2827          | 0.8831   |
| 0.1686        | 6.0   | 1356  | 0.3078          | 0.8765   |
| 0.1222        | 7.0   | 1582  | 0.3023          | 0.8998   |
| 0.1406        | 8.0   | 1808  | 0.3325          | 0.8865   |
| 0.0989        | 9.0   | 2034  | 0.3862          | 0.8798   |
| 0.0281        | 10.0  | 2260  | 0.3985          | 0.8748   |
| 0.0373        | 11.0  | 2486  | 0.4395          | 0.8831   |
| 0.0324        | 12.0  | 2712  | 0.4479          | 0.8898   |
| 0.0085        | 13.0  | 2938  | 0.5150          | 0.8865   |
| 0.0336        | 14.0  | 3164  | 0.5239          | 0.8831   |
| 0.0184        | 15.0  | 3390  | 0.5580          | 0.8798   |
| 0.0187        | 16.0  | 3616  | 0.6394          | 0.8798   |
| 0.0341        | 17.0  | 3842  | 0.7055          | 0.8715   |
| 0.0009        | 18.0  | 4068  | 0.6833          | 0.8698   |
| 0.0242        | 19.0  | 4294  | 0.6897          | 0.8731   |
| 0.0002        | 20.0  | 4520  | 0.7463          | 0.8715   |
| 0.0021        | 21.0  | 4746  | 0.7865          | 0.8664   |
| 0.0168        | 22.0  | 4972  | 0.7905          | 0.8715   |
| 0.0077        | 23.0  | 5198  | 0.7986          | 0.8715   |
| 0.0002        | 24.0  | 5424  | 0.8358          | 0.8715   |
| 0.0002        | 25.0  | 5650  | 0.8300          | 0.8698   |
| 0.0001        | 26.0  | 5876  | 0.8435          | 0.8681   |
| 0.0001        | 27.0  | 6102  | 0.8418          | 0.8681   |
| 0.0001        | 28.0  | 6328  | 0.8696          | 0.8681   |
| 0.0           | 29.0  | 6554  | 0.8706          | 0.8698   |
| 0.0001        | 30.0  | 6780  | 0.9033          | 0.8698   |
| 0.0001        | 31.0  | 7006  | 0.9296          | 0.8681   |
| 0.0001        | 32.0  | 7232  | 0.8999          | 0.8698   |
| 0.0096        | 33.0  | 7458  | 0.9062          | 0.8681   |
| 0.0001        | 34.0  | 7684  | 0.9009          | 0.8715   |
| 0.0           | 35.0  | 7910  | 0.8975          | 0.8765   |
| 0.0           | 36.0  | 8136  | 0.9003          | 0.8748   |
| 0.0           | 37.0  | 8362  | 0.9103          | 0.8731   |
| 0.0           | 38.0  | 8588  | 0.9226          | 0.8664   |
| 0.0           | 39.0  | 8814  | 0.9185          | 0.8698   |
| 0.0           | 40.0  | 9040  | 0.9208          | 0.8715   |
| 0.0079        | 41.0  | 9266  | 0.9347          | 0.8698   |
| 0.0103        | 42.0  | 9492  | 0.9073          | 0.8731   |
| 0.0           | 43.0  | 9718  | 0.9457          | 0.8664   |
| 0.0           | 44.0  | 9944  | 0.9277          | 0.8698   |
| 0.0           | 45.0  | 10170 | 0.9217          | 0.8715   |
| 0.0           | 46.0  | 10396 | 0.9203          | 0.8715   |
| 0.0           | 47.0  | 10622 | 0.9223          | 0.8715   |
| 0.0           | 48.0  | 10848 | 0.9286          | 0.8715   |
| 0.0           | 49.0  | 11074 | 0.9289          | 0.8715   |
| 0.0           | 50.0  | 11300 | 0.9289          | 0.8715   |


### 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