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# smids_5x_deit_base_rms_0001_fold4

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4272
- Accuracy: 0.885

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2738        | 1.0   | 375   | 0.3688          | 0.8617   |
| 0.164         | 2.0   | 750   | 0.4071          | 0.86     |
| 0.1224        | 3.0   | 1125  | 0.5148          | 0.87     |
| 0.1153        | 4.0   | 1500  | 0.5755          | 0.8833   |
| 0.0768        | 5.0   | 1875  | 0.5201          | 0.8733   |
| 0.0128        | 6.0   | 2250  | 0.6962          | 0.88     |
| 0.0434        | 7.0   | 2625  | 0.7802          | 0.8733   |
| 0.011         | 8.0   | 3000  | 0.8928          | 0.8567   |
| 0.0054        | 9.0   | 3375  | 0.8190          | 0.8583   |
| 0.0033        | 10.0  | 3750  | 0.8366          | 0.8683   |
| 0.0196        | 11.0  | 4125  | 0.7608          | 0.8817   |
| 0.0229        | 12.0  | 4500  | 0.8098          | 0.8683   |
| 0.032         | 13.0  | 4875  | 0.8776          | 0.865    |
| 0.0132        | 14.0  | 5250  | 0.9792          | 0.8517   |
| 0.0019        | 15.0  | 5625  | 0.8844          | 0.8783   |
| 0.0007        | 16.0  | 6000  | 0.9761          | 0.8683   |
| 0.0282        | 17.0  | 6375  | 0.8085          | 0.855    |
| 0.0269        | 18.0  | 6750  | 0.8221          | 0.875    |
| 0.0           | 19.0  | 7125  | 0.8222          | 0.8833   |
| 0.0006        | 20.0  | 7500  | 0.9491          | 0.8633   |
| 0.0061        | 21.0  | 7875  | 0.9907          | 0.86     |
| 0.0014        | 22.0  | 8250  | 1.0614          | 0.86     |
| 0.0136        | 23.0  | 8625  | 0.8637          | 0.8717   |
| 0.0038        | 24.0  | 9000  | 0.9073          | 0.8717   |
| 0.003         | 25.0  | 9375  | 1.0178          | 0.875    |
| 0.0311        | 26.0  | 9750  | 0.9666          | 0.8817   |
| 0.0228        | 27.0  | 10125 | 0.9904          | 0.8783   |
| 0.0           | 28.0  | 10500 | 1.1423          | 0.8617   |
| 0.0001        | 29.0  | 10875 | 1.1415          | 0.865    |
| 0.0142        | 30.0  | 11250 | 1.0575          | 0.88     |
| 0.0005        | 31.0  | 11625 | 1.2704          | 0.8683   |
| 0.0           | 32.0  | 12000 | 1.1747          | 0.875    |
| 0.0055        | 33.0  | 12375 | 1.1305          | 0.8717   |
| 0.0001        | 34.0  | 12750 | 1.1488          | 0.8817   |
| 0.0           | 35.0  | 13125 | 1.1227          | 0.8767   |
| 0.0           | 36.0  | 13500 | 1.1854          | 0.8733   |
| 0.0           | 37.0  | 13875 | 1.1866          | 0.8817   |
| 0.0066        | 38.0  | 14250 | 1.1780          | 0.885    |
| 0.0           | 39.0  | 14625 | 1.2908          | 0.88     |
| 0.0           | 40.0  | 15000 | 1.2885          | 0.8817   |
| 0.0           | 41.0  | 15375 | 1.3508          | 0.885    |
| 0.0           | 42.0  | 15750 | 1.3636          | 0.8817   |
| 0.0           | 43.0  | 16125 | 1.3897          | 0.8817   |
| 0.0           | 44.0  | 16500 | 1.3912          | 0.8783   |
| 0.0           | 45.0  | 16875 | 1.4003          | 0.885    |
| 0.0           | 46.0  | 17250 | 1.4104          | 0.885    |
| 0.0           | 47.0  | 17625 | 1.4228          | 0.885    |
| 0.0           | 48.0  | 18000 | 1.4261          | 0.885    |
| 0.0           | 49.0  | 18375 | 1.4274          | 0.885    |
| 0.0           | 50.0  | 18750 | 1.4272          | 0.885    |


### 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