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# smids_5x_deit_base_rms_00001_fold3

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8646
- Accuracy: 0.9133

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1923        | 1.0   | 375   | 0.2761          | 0.89     |
| 0.0794        | 2.0   | 750   | 0.2797          | 0.92     |
| 0.0089        | 3.0   | 1125  | 0.3862          | 0.9083   |
| 0.0263        | 4.0   | 1500  | 0.4507          | 0.915    |
| 0.0241        | 5.0   | 1875  | 0.5940          | 0.9033   |
| 0.0093        | 6.0   | 2250  | 0.5438          | 0.915    |
| 0.0074        | 7.0   | 2625  | 0.6908          | 0.9      |
| 0.003         | 8.0   | 3000  | 0.6531          | 0.905    |
| 0.038         | 9.0   | 3375  | 0.6121          | 0.9133   |
| 0.0021        | 10.0  | 3750  | 0.6007          | 0.9167   |
| 0.0           | 11.0  | 4125  | 0.7290          | 0.9017   |
| 0.0           | 12.0  | 4500  | 0.6566          | 0.905    |
| 0.0057        | 13.0  | 4875  | 0.7475          | 0.905    |
| 0.0           | 14.0  | 5250  | 0.7562          | 0.915    |
| 0.0001        | 15.0  | 5625  | 0.7103          | 0.9117   |
| 0.0           | 16.0  | 6000  | 0.7619          | 0.9133   |
| 0.023         | 17.0  | 6375  | 0.8515          | 0.9017   |
| 0.0           | 18.0  | 6750  | 0.7788          | 0.9167   |
| 0.0668        | 19.0  | 7125  | 0.9370          | 0.8917   |
| 0.0002        | 20.0  | 7500  | 0.9448          | 0.8933   |
| 0.0           | 21.0  | 7875  | 0.8080          | 0.9117   |
| 0.0           | 22.0  | 8250  | 0.8600          | 0.9017   |
| 0.0029        | 23.0  | 8625  | 0.9399          | 0.9017   |
| 0.0           | 24.0  | 9000  | 0.8645          | 0.9067   |
| 0.0           | 25.0  | 9375  | 0.8436          | 0.9117   |
| 0.0           | 26.0  | 9750  | 0.8337          | 0.905    |
| 0.0           | 27.0  | 10125 | 0.8133          | 0.9117   |
| 0.0           | 28.0  | 10500 | 0.8299          | 0.9217   |
| 0.0           | 29.0  | 10875 | 0.8115          | 0.9117   |
| 0.0038        | 30.0  | 11250 | 0.8569          | 0.9183   |
| 0.0           | 31.0  | 11625 | 0.7968          | 0.9133   |
| 0.0           | 32.0  | 12000 | 0.8353          | 0.9133   |
| 0.0           | 33.0  | 12375 | 0.8387          | 0.9117   |
| 0.0           | 34.0  | 12750 | 0.8604          | 0.9117   |
| 0.0           | 35.0  | 13125 | 0.8144          | 0.9133   |
| 0.0           | 36.0  | 13500 | 0.8218          | 0.9033   |
| 0.0           | 37.0  | 13875 | 0.8203          | 0.9133   |
| 0.0           | 38.0  | 14250 | 0.8248          | 0.9133   |
| 0.0           | 39.0  | 14625 | 0.8352          | 0.9133   |
| 0.0031        | 40.0  | 15000 | 0.8391          | 0.9233   |
| 0.0           | 41.0  | 15375 | 0.8462          | 0.9167   |
| 0.0           | 42.0  | 15750 | 0.8442          | 0.9217   |
| 0.0           | 43.0  | 16125 | 0.8498          | 0.9133   |
| 0.0           | 44.0  | 16500 | 0.8546          | 0.9117   |
| 0.0           | 45.0  | 16875 | 0.8578          | 0.9117   |
| 0.0           | 46.0  | 17250 | 0.8603          | 0.9117   |
| 0.0           | 47.0  | 17625 | 0.8622          | 0.9117   |
| 0.0           | 48.0  | 18000 | 0.8633          | 0.9117   |
| 0.0           | 49.0  | 18375 | 0.8638          | 0.9133   |
| 0.0           | 50.0  | 18750 | 0.8646          | 0.9133   |


### 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