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# smids_5x_deit_base_adamax_001_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: 1.4274
- Accuracy: 0.8733

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3484        | 1.0   | 375   | 0.4288          | 0.8417   |
| 0.3018        | 2.0   | 750   | 0.4109          | 0.84     |
| 0.131         | 3.0   | 1125  | 0.4491          | 0.8367   |
| 0.167         | 4.0   | 1500  | 0.4912          | 0.8583   |
| 0.1356        | 5.0   | 1875  | 0.4970          | 0.8617   |
| 0.074         | 6.0   | 2250  | 0.5520          | 0.8617   |
| 0.126         | 7.0   | 2625  | 0.5266          | 0.8683   |
| 0.1043        | 8.0   | 3000  | 0.5883          | 0.86     |
| 0.0184        | 9.0   | 3375  | 0.7003          | 0.8583   |
| 0.0576        | 10.0  | 3750  | 0.6626          | 0.87     |
| 0.0647        | 11.0  | 4125  | 0.5819          | 0.8667   |
| 0.0295        | 12.0  | 4500  | 0.8380          | 0.855    |
| 0.0198        | 13.0  | 4875  | 0.7725          | 0.8667   |
| 0.0803        | 14.0  | 5250  | 0.7242          | 0.86     |
| 0.0028        | 15.0  | 5625  | 0.5735          | 0.88     |
| 0.018         | 16.0  | 6000  | 0.9546          | 0.855    |
| 0.0295        | 17.0  | 6375  | 0.8527          | 0.8683   |
| 0.0122        | 18.0  | 6750  | 0.8464          | 0.8617   |
| 0.0006        | 19.0  | 7125  | 0.8600          | 0.8683   |
| 0.0121        | 20.0  | 7500  | 0.8637          | 0.8667   |
| 0.0034        | 21.0  | 7875  | 0.8894          | 0.8783   |
| 0.0002        | 22.0  | 8250  | 0.9509          | 0.855    |
| 0.0032        | 23.0  | 8625  | 1.0099          | 0.865    |
| 0.0103        | 24.0  | 9000  | 1.0826          | 0.8783   |
| 0.0066        | 25.0  | 9375  | 1.2355          | 0.8367   |
| 0.0001        | 26.0  | 9750  | 1.1335          | 0.8683   |
| 0.0066        | 27.0  | 10125 | 0.8709          | 0.88     |
| 0.0           | 28.0  | 10500 | 1.0074          | 0.88     |
| 0.0           | 29.0  | 10875 | 1.1392          | 0.8633   |
| 0.0           | 30.0  | 11250 | 1.2579          | 0.8617   |
| 0.0009        | 31.0  | 11625 | 1.1228          | 0.87     |
| 0.0           | 32.0  | 12000 | 1.2029          | 0.8733   |
| 0.0           | 33.0  | 12375 | 1.1147          | 0.87     |
| 0.0           | 34.0  | 12750 | 1.1837          | 0.865    |
| 0.0           | 35.0  | 13125 | 1.2046          | 0.87     |
| 0.0           | 36.0  | 13500 | 1.2160          | 0.8717   |
| 0.0           | 37.0  | 13875 | 1.2236          | 0.8767   |
| 0.004         | 38.0  | 14250 | 1.2489          | 0.8767   |
| 0.0           | 39.0  | 14625 | 1.2705          | 0.8767   |
| 0.0           | 40.0  | 15000 | 1.2929          | 0.8767   |
| 0.0           | 41.0  | 15375 | 1.3044          | 0.8767   |
| 0.0           | 42.0  | 15750 | 1.3306          | 0.8733   |
| 0.0           | 43.0  | 16125 | 1.3359          | 0.875    |
| 0.0           | 44.0  | 16500 | 1.3566          | 0.8733   |
| 0.0           | 45.0  | 16875 | 1.3753          | 0.875    |
| 0.0           | 46.0  | 17250 | 1.3919          | 0.875    |
| 0.0           | 47.0  | 17625 | 1.4064          | 0.875    |
| 0.0           | 48.0  | 18000 | 1.4171          | 0.8733   |
| 0.0           | 49.0  | 18375 | 1.4242          | 0.8733   |
| 0.0           | 50.0  | 18750 | 1.4274          | 0.8733   |


### 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