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# smids_3x_deit_small_adamax_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: 0.6646
- Accuracy: 0.9115

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3694        | 1.0   | 226   | 0.3043          | 0.8765   |
| 0.1918        | 2.0   | 452   | 0.2837          | 0.8948   |
| 0.1232        | 3.0   | 678   | 0.5268          | 0.8464   |
| 0.0814        | 4.0   | 904   | 0.3209          | 0.9048   |
| 0.0289        | 5.0   | 1130  | 0.4828          | 0.8948   |
| 0.009         | 6.0   | 1356  | 0.6672          | 0.8932   |
| 0.057         | 7.0   | 1582  | 0.5442          | 0.9032   |
| 0.0008        | 8.0   | 1808  | 0.5498          | 0.9082   |
| 0.037         | 9.0   | 2034  | 0.4929          | 0.9182   |
| 0.0001        | 10.0  | 2260  | 0.5526          | 0.9082   |
| 0.0002        | 11.0  | 2486  | 0.5981          | 0.9048   |
| 0.0001        | 12.0  | 2712  | 0.5439          | 0.9115   |
| 0.0           | 13.0  | 2938  | 0.5693          | 0.9082   |
| 0.017         | 14.0  | 3164  | 0.5385          | 0.9149   |
| 0.003         | 15.0  | 3390  | 0.6151          | 0.9165   |
| 0.0           | 16.0  | 3616  | 0.5918          | 0.9149   |
| 0.0028        | 17.0  | 3842  | 0.5844          | 0.9115   |
| 0.0           | 18.0  | 4068  | 0.5936          | 0.9149   |
| 0.0042        | 19.0  | 4294  | 0.5972          | 0.9032   |
| 0.0           | 20.0  | 4520  | 0.5981          | 0.9098   |
| 0.0           | 21.0  | 4746  | 0.6811          | 0.9082   |
| 0.0168        | 22.0  | 4972  | 0.5972          | 0.9115   |
| 0.0028        | 23.0  | 5198  | 0.6127          | 0.9132   |
| 0.0           | 24.0  | 5424  | 0.6115          | 0.9149   |
| 0.0           | 25.0  | 5650  | 0.6222          | 0.9149   |
| 0.0           | 26.0  | 5876  | 0.6074          | 0.9215   |
| 0.0           | 27.0  | 6102  | 0.6129          | 0.9165   |
| 0.0           | 28.0  | 6328  | 0.6078          | 0.9165   |
| 0.0           | 29.0  | 6554  | 0.6211          | 0.9215   |
| 0.0           | 30.0  | 6780  | 0.6156          | 0.9149   |
| 0.0           | 31.0  | 7006  | 0.6329          | 0.9149   |
| 0.0           | 32.0  | 7232  | 0.6211          | 0.9215   |
| 0.0031        | 33.0  | 7458  | 0.6254          | 0.9165   |
| 0.0           | 34.0  | 7684  | 0.6575          | 0.9149   |
| 0.0           | 35.0  | 7910  | 0.6417          | 0.9132   |
| 0.0           | 36.0  | 8136  | 0.6653          | 0.9199   |
| 0.0           | 37.0  | 8362  | 0.6525          | 0.9149   |
| 0.0           | 38.0  | 8588  | 0.6394          | 0.9149   |
| 0.0           | 39.0  | 8814  | 0.6447          | 0.9165   |
| 0.0           | 40.0  | 9040  | 0.6473          | 0.9132   |
| 0.0027        | 41.0  | 9266  | 0.6511          | 0.9149   |
| 0.0028        | 42.0  | 9492  | 0.6536          | 0.9149   |
| 0.0           | 43.0  | 9718  | 0.6481          | 0.9149   |
| 0.0           | 44.0  | 9944  | 0.6547          | 0.9132   |
| 0.0           | 45.0  | 10170 | 0.6581          | 0.9132   |
| 0.0           | 46.0  | 10396 | 0.6606          | 0.9132   |
| 0.0           | 47.0  | 10622 | 0.6627          | 0.9132   |
| 0.0           | 48.0  | 10848 | 0.6635          | 0.9132   |
| 0.0           | 49.0  | 11074 | 0.6649          | 0.9115   |
| 0.0           | 50.0  | 11300 | 0.6646          | 0.9115   |


### 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