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# smids_5x_deit_small_rms_00001_fold5

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: 0.9811
- Accuracy: 0.905

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2316        | 1.0   | 375   | 0.3828          | 0.845    |
| 0.1221        | 2.0   | 750   | 0.3043          | 0.8767   |
| 0.1068        | 3.0   | 1125  | 0.3175          | 0.8983   |
| 0.0662        | 4.0   | 1500  | 0.4134          | 0.91     |
| 0.0386        | 5.0   | 1875  | 0.4725          | 0.9017   |
| 0.0006        | 6.0   | 2250  | 0.5991          | 0.8967   |
| 0.0164        | 7.0   | 2625  | 0.5133          | 0.9083   |
| 0.0227        | 8.0   | 3000  | 0.6114          | 0.905    |
| 0.0539        | 9.0   | 3375  | 0.7854          | 0.8933   |
| 0.0093        | 10.0  | 3750  | 0.6820          | 0.8983   |
| 0.0296        | 11.0  | 4125  | 0.8028          | 0.88     |
| 0.0005        | 12.0  | 4500  | 0.7438          | 0.8983   |
| 0.0103        | 13.0  | 4875  | 0.8815          | 0.885    |
| 0.0           | 14.0  | 5250  | 0.8305          | 0.89     |
| 0.0056        | 15.0  | 5625  | 0.8083          | 0.895    |
| 0.0           | 16.0  | 6000  | 0.7453          | 0.8917   |
| 0.0           | 17.0  | 6375  | 0.7352          | 0.8967   |
| 0.0086        | 18.0  | 6750  | 0.8865          | 0.8883   |
| 0.0           | 19.0  | 7125  | 0.9006          | 0.8867   |
| 0.0007        | 20.0  | 7500  | 0.8620          | 0.8883   |
| 0.0001        | 21.0  | 7875  | 0.8055          | 0.8967   |
| 0.0167        | 22.0  | 8250  | 0.9436          | 0.8917   |
| 0.0           | 23.0  | 8625  | 0.9512          | 0.8883   |
| 0.0           | 24.0  | 9000  | 0.8722          | 0.9017   |
| 0.014         | 25.0  | 9375  | 0.8674          | 0.9      |
| 0.0           | 26.0  | 9750  | 0.8631          | 0.9017   |
| 0.0           | 27.0  | 10125 | 0.8607          | 0.9017   |
| 0.0054        | 28.0  | 10500 | 0.8949          | 0.9017   |
| 0.0           | 29.0  | 10875 | 0.9221          | 0.895    |
| 0.0052        | 30.0  | 11250 | 0.8532          | 0.905    |
| 0.0           | 31.0  | 11625 | 0.8797          | 0.9017   |
| 0.0           | 32.0  | 12000 | 0.8663          | 0.8983   |
| 0.0           | 33.0  | 12375 | 0.9338          | 0.8983   |
| 0.0           | 34.0  | 12750 | 0.9397          | 0.9033   |
| 0.0           | 35.0  | 13125 | 0.9338          | 0.905    |
| 0.0           | 36.0  | 13500 | 0.9706          | 0.9      |
| 0.0           | 37.0  | 13875 | 0.9486          | 0.9      |
| 0.0028        | 38.0  | 14250 | 0.9187          | 0.9033   |
| 0.0           | 39.0  | 14625 | 0.9340          | 0.9067   |
| 0.0           | 40.0  | 15000 | 0.9523          | 0.905    |
| 0.0           | 41.0  | 15375 | 0.9602          | 0.9067   |
| 0.0           | 42.0  | 15750 | 0.9532          | 0.905    |
| 0.0           | 43.0  | 16125 | 0.9551          | 0.9067   |
| 0.0           | 44.0  | 16500 | 0.9611          | 0.905    |
| 0.0           | 45.0  | 16875 | 0.9674          | 0.905    |
| 0.0           | 46.0  | 17250 | 0.9798          | 0.905    |
| 0.0027        | 47.0  | 17625 | 0.9798          | 0.905    |
| 0.0           | 48.0  | 18000 | 0.9814          | 0.905    |
| 0.0           | 49.0  | 18375 | 0.9829          | 0.905    |
| 0.0022        | 50.0  | 18750 | 0.9811          | 0.905    |


### 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