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# smids_3x_deit_small_adamax_001_fold4

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: 1.4399
- Accuracy: 0.86

## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3979        | 1.0   | 225   | 0.5683          | 0.7933   |
| 0.2606        | 2.0   | 450   | 0.4043          | 0.84     |
| 0.2162        | 3.0   | 675   | 0.5101          | 0.82     |
| 0.2443        | 4.0   | 900   | 0.4744          | 0.84     |
| 0.1743        | 5.0   | 1125  | 0.4964          | 0.8567   |
| 0.1364        | 6.0   | 1350  | 0.5063          | 0.8433   |
| 0.1949        | 7.0   | 1575  | 0.5298          | 0.855    |
| 0.116         | 8.0   | 1800  | 0.6753          | 0.8617   |
| 0.1838        | 9.0   | 2025  | 0.6150          | 0.84     |
| 0.0967        | 10.0  | 2250  | 0.7712          | 0.8433   |
| 0.0509        | 11.0  | 2475  | 0.7887          | 0.8417   |
| 0.0235        | 12.0  | 2700  | 0.6823          | 0.8633   |
| 0.0623        | 13.0  | 2925  | 0.6980          | 0.8533   |
| 0.0443        | 14.0  | 3150  | 0.6897          | 0.87     |
| 0.0341        | 15.0  | 3375  | 0.8718          | 0.8417   |
| 0.037         | 16.0  | 3600  | 0.8120          | 0.865    |
| 0.0314        | 17.0  | 3825  | 0.8372          | 0.8467   |
| 0.0477        | 18.0  | 4050  | 0.7518          | 0.85     |
| 0.0002        | 19.0  | 4275  | 1.0178          | 0.8517   |
| 0.0091        | 20.0  | 4500  | 1.1728          | 0.8333   |
| 0.0291        | 21.0  | 4725  | 0.9139          | 0.85     |
| 0.0076        | 22.0  | 4950  | 1.0132          | 0.8533   |
| 0.0005        | 23.0  | 5175  | 1.0336          | 0.8567   |
| 0.0002        | 24.0  | 5400  | 1.1694          | 0.85     |
| 0.0032        | 25.0  | 5625  | 1.1362          | 0.86     |
| 0.0002        | 26.0  | 5850  | 1.2122          | 0.85     |
| 0.011         | 27.0  | 6075  | 1.2712          | 0.8567   |
| 0.0237        | 28.0  | 6300  | 1.3743          | 0.85     |
| 0.0           | 29.0  | 6525  | 1.2063          | 0.86     |
| 0.0           | 30.0  | 6750  | 1.3085          | 0.86     |
| 0.0           | 31.0  | 6975  | 1.3297          | 0.8567   |
| 0.0           | 32.0  | 7200  | 1.2473          | 0.855    |
| 0.0           | 33.0  | 7425  | 1.1982          | 0.8617   |
| 0.0           | 34.0  | 7650  | 1.2288          | 0.8617   |
| 0.0           | 35.0  | 7875  | 1.2397          | 0.8617   |
| 0.0           | 36.0  | 8100  | 1.2697          | 0.8617   |
| 0.0           | 37.0  | 8325  | 1.2895          | 0.8583   |
| 0.0           | 38.0  | 8550  | 1.3064          | 0.8583   |
| 0.0           | 39.0  | 8775  | 1.3040          | 0.8583   |
| 0.0           | 40.0  | 9000  | 1.3224          | 0.8617   |
| 0.0           | 41.0  | 9225  | 1.3481          | 0.8567   |
| 0.0032        | 42.0  | 9450  | 1.3550          | 0.8583   |
| 0.0           | 43.0  | 9675  | 1.3631          | 0.86     |
| 0.0029        | 44.0  | 9900  | 1.3898          | 0.8567   |
| 0.0           | 45.0  | 10125 | 1.3948          | 0.86     |
| 0.0           | 46.0  | 10350 | 1.4079          | 0.86     |
| 0.0           | 47.0  | 10575 | 1.4219          | 0.86     |
| 0.0           | 48.0  | 10800 | 1.4319          | 0.8583   |
| 0.0           | 49.0  | 11025 | 1.4436          | 0.8583   |
| 0.0           | 50.0  | 11250 | 1.4399          | 0.86     |


### 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