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# smids_5x_deit_tiny_adamax_0001_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: 1.2292
- Accuracy: 0.88

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2019        | 1.0   | 375   | 0.3616          | 0.8683   |
| 0.2348        | 2.0   | 750   | 0.5390          | 0.7983   |
| 0.0464        | 3.0   | 1125  | 0.5043          | 0.88     |
| 0.0924        | 4.0   | 1500  | 0.5883          | 0.8833   |
| 0.0137        | 5.0   | 1875  | 0.7305          | 0.8783   |
| 0.0256        | 6.0   | 2250  | 0.8161          | 0.8783   |
| 0.0006        | 7.0   | 2625  | 0.7997          | 0.8833   |
| 0.0263        | 8.0   | 3000  | 0.8542          | 0.885    |
| 0.0002        | 9.0   | 3375  | 0.9159          | 0.87     |
| 0.0           | 10.0  | 3750  | 0.9248          | 0.8833   |
| 0.0181        | 11.0  | 4125  | 1.0824          | 0.8633   |
| 0.0031        | 12.0  | 4500  | 0.9537          | 0.89     |
| 0.0115        | 13.0  | 4875  | 1.0751          | 0.8667   |
| 0.0169        | 14.0  | 5250  | 0.8764          | 0.8867   |
| 0.0           | 15.0  | 5625  | 0.9541          | 0.8817   |
| 0.0           | 16.0  | 6000  | 1.0324          | 0.87     |
| 0.0003        | 17.0  | 6375  | 1.0424          | 0.8733   |
| 0.0131        | 18.0  | 6750  | 1.0393          | 0.8767   |
| 0.0           | 19.0  | 7125  | 1.0119          | 0.8867   |
| 0.0           | 20.0  | 7500  | 0.9792          | 0.8833   |
| 0.0           | 21.0  | 7875  | 1.0247          | 0.88     |
| 0.0           | 22.0  | 8250  | 1.0061          | 0.885    |
| 0.0           | 23.0  | 8625  | 1.0234          | 0.8867   |
| 0.0           | 24.0  | 9000  | 1.0734          | 0.8733   |
| 0.0           | 25.0  | 9375  | 1.0638          | 0.8867   |
| 0.0           | 26.0  | 9750  | 1.0711          | 0.88     |
| 0.0           | 27.0  | 10125 | 1.1175          | 0.88     |
| 0.0           | 28.0  | 10500 | 1.0879          | 0.8867   |
| 0.0           | 29.0  | 10875 | 1.1361          | 0.8817   |
| 0.0           | 30.0  | 11250 | 1.1028          | 0.89     |
| 0.0           | 31.0  | 11625 | 1.1478          | 0.8817   |
| 0.0           | 32.0  | 12000 | 1.1406          | 0.8833   |
| 0.0           | 33.0  | 12375 | 1.1490          | 0.8833   |
| 0.0           | 34.0  | 12750 | 1.1669          | 0.8817   |
| 0.0           | 35.0  | 13125 | 1.1635          | 0.8833   |
| 0.0           | 36.0  | 13500 | 1.1789          | 0.8817   |
| 0.0           | 37.0  | 13875 | 1.1756          | 0.8833   |
| 0.0029        | 38.0  | 14250 | 1.1808          | 0.8833   |
| 0.0           | 39.0  | 14625 | 1.1891          | 0.8833   |
| 0.0           | 40.0  | 15000 | 1.1976          | 0.8833   |
| 0.0           | 41.0  | 15375 | 1.2036          | 0.8817   |
| 0.0           | 42.0  | 15750 | 1.2058          | 0.88     |
| 0.0           | 43.0  | 16125 | 1.2107          | 0.8817   |
| 0.0           | 44.0  | 16500 | 1.2163          | 0.88     |
| 0.0           | 45.0  | 16875 | 1.2201          | 0.8783   |
| 0.0           | 46.0  | 17250 | 1.2238          | 0.8783   |
| 0.0           | 47.0  | 17625 | 1.2266          | 0.88     |
| 0.0           | 48.0  | 18000 | 1.2286          | 0.88     |
| 0.0           | 49.0  | 18375 | 1.2293          | 0.88     |
| 0.0           | 50.0  | 18750 | 1.2292          | 0.88     |


### 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