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# smids_5x_deit_tiny_adamax_001_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: 1.0054
- Accuracy: 0.8967

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4039        | 1.0   | 375   | 0.4314          | 0.825    |
| 0.345         | 2.0   | 750   | 0.2900          | 0.8967   |
| 0.2226        | 3.0   | 1125  | 0.4260          | 0.8533   |
| 0.2463        | 4.0   | 1500  | 0.4152          | 0.84     |
| 0.132         | 5.0   | 1875  | 0.3792          | 0.8767   |
| 0.0976        | 6.0   | 2250  | 0.4696          | 0.8783   |
| 0.1565        | 7.0   | 2625  | 0.3805          | 0.8833   |
| 0.0928        | 8.0   | 3000  | 0.5026          | 0.8867   |
| 0.0864        | 9.0   | 3375  | 0.4372          | 0.8817   |
| 0.0498        | 10.0  | 3750  | 0.5601          | 0.8617   |
| 0.0645        | 11.0  | 4125  | 0.5273          | 0.8983   |
| 0.0761        | 12.0  | 4500  | 0.5529          | 0.8867   |
| 0.0594        | 13.0  | 4875  | 0.5381          | 0.88     |
| 0.039         | 14.0  | 5250  | 0.6308          | 0.895    |
| 0.0193        | 15.0  | 5625  | 0.5515          | 0.89     |
| 0.0113        | 16.0  | 6000  | 0.7221          | 0.8667   |
| 0.008         | 17.0  | 6375  | 0.6792          | 0.9017   |
| 0.0388        | 18.0  | 6750  | 0.7109          | 0.8883   |
| 0.0009        | 19.0  | 7125  | 0.6719          | 0.8967   |
| 0.023         | 20.0  | 7500  | 0.7328          | 0.8917   |
| 0.0093        | 21.0  | 7875  | 0.8033          | 0.8917   |
| 0.0223        | 22.0  | 8250  | 0.6700          | 0.8983   |
| 0.0002        | 23.0  | 8625  | 0.7140          | 0.8967   |
| 0.0504        | 24.0  | 9000  | 0.8112          | 0.9      |
| 0.0006        | 25.0  | 9375  | 0.7975          | 0.8833   |
| 0.0027        | 26.0  | 9750  | 0.9326          | 0.885    |
| 0.0053        | 27.0  | 10125 | 0.8820          | 0.8817   |
| 0.0           | 28.0  | 10500 | 0.9014          | 0.89     |
| 0.0046        | 29.0  | 10875 | 0.8788          | 0.8917   |
| 0.0025        | 30.0  | 11250 | 0.9368          | 0.8967   |
| 0.0           | 31.0  | 11625 | 0.9273          | 0.895    |
| 0.0           | 32.0  | 12000 | 1.0112          | 0.8817   |
| 0.0014        | 33.0  | 12375 | 0.8964          | 0.89     |
| 0.0           | 34.0  | 12750 | 0.9035          | 0.8983   |
| 0.0           | 35.0  | 13125 | 0.9036          | 0.8983   |
| 0.0           | 36.0  | 13500 | 0.9211          | 0.8983   |
| 0.0           | 37.0  | 13875 | 0.9473          | 0.9033   |
| 0.0039        | 38.0  | 14250 | 0.9999          | 0.8883   |
| 0.0           | 39.0  | 14625 | 0.9726          | 0.8967   |
| 0.0           | 40.0  | 15000 | 0.9581          | 0.8967   |
| 0.0           | 41.0  | 15375 | 0.9592          | 0.9      |
| 0.0           | 42.0  | 15750 | 0.9776          | 0.9      |
| 0.0           | 43.0  | 16125 | 0.9762          | 0.8967   |
| 0.0           | 44.0  | 16500 | 0.9706          | 0.8983   |
| 0.0           | 45.0  | 16875 | 0.9775          | 0.8967   |
| 0.0           | 46.0  | 17250 | 0.9930          | 0.8983   |
| 0.0029        | 47.0  | 17625 | 0.9955          | 0.8983   |
| 0.0           | 48.0  | 18000 | 1.0006          | 0.8983   |
| 0.0           | 49.0  | 18375 | 1.0043          | 0.8967   |
| 0.0021        | 50.0  | 18750 | 1.0054          | 0.8967   |


### 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