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# smids_10x_deit_base_adamax_0001_fold2

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: 1.0292
- Accuracy: 0.8985

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1628        | 1.0   | 750   | 0.2778          | 0.8885   |
| 0.0865        | 2.0   | 1500  | 0.3575          | 0.9002   |
| 0.0255        | 3.0   | 2250  | 0.5656          | 0.8885   |
| 0.0266        | 4.0   | 3000  | 0.5733          | 0.9002   |
| 0.0169        | 5.0   | 3750  | 0.7037          | 0.9002   |
| 0.0024        | 6.0   | 4500  | 0.6843          | 0.8918   |
| 0.0124        | 7.0   | 5250  | 0.7219          | 0.8985   |
| 0.0002        | 8.0   | 6000  | 0.8057          | 0.8935   |
| 0.002         | 9.0   | 6750  | 0.7964          | 0.8985   |
| 0.0           | 10.0  | 7500  | 0.7467          | 0.9002   |
| 0.0004        | 11.0  | 8250  | 0.6482          | 0.9002   |
| 0.0           | 12.0  | 9000  | 0.8380          | 0.8968   |
| 0.0           | 13.0  | 9750  | 0.8814          | 0.8902   |
| 0.0           | 14.0  | 10500 | 0.8400          | 0.8985   |
| 0.0           | 15.0  | 11250 | 0.7646          | 0.9068   |
| 0.0003        | 16.0  | 12000 | 0.8183          | 0.9068   |
| 0.0           | 17.0  | 12750 | 0.8397          | 0.8985   |
| 0.0           | 18.0  | 13500 | 0.9452          | 0.9052   |
| 0.0           | 19.0  | 14250 | 0.8230          | 0.9085   |
| 0.0           | 20.0  | 15000 | 0.8880          | 0.9085   |
| 0.0           | 21.0  | 15750 | 0.9221          | 0.9035   |
| 0.004         | 22.0  | 16500 | 0.9029          | 0.8968   |
| 0.0026        | 23.0  | 17250 | 0.9288          | 0.9002   |
| 0.0           | 24.0  | 18000 | 0.9054          | 0.9002   |
| 0.0           | 25.0  | 18750 | 0.9636          | 0.8952   |
| 0.0           | 26.0  | 19500 | 0.9715          | 0.8935   |
| 0.0035        | 27.0  | 20250 | 0.9914          | 0.9002   |
| 0.0           | 28.0  | 21000 | 1.0064          | 0.9002   |
| 0.0           | 29.0  | 21750 | 0.9401          | 0.8985   |
| 0.0           | 30.0  | 22500 | 0.9859          | 0.9002   |
| 0.0           | 31.0  | 23250 | 1.0344          | 0.8952   |
| 0.0           | 32.0  | 24000 | 1.0163          | 0.8935   |
| 0.0           | 33.0  | 24750 | 0.9803          | 0.9018   |
| 0.0           | 34.0  | 25500 | 1.0043          | 0.8918   |
| 0.0           | 35.0  | 26250 | 1.0148          | 0.8968   |
| 0.0           | 36.0  | 27000 | 1.0177          | 0.9035   |
| 0.0028        | 37.0  | 27750 | 1.0101          | 0.8985   |
| 0.0           | 38.0  | 28500 | 0.9955          | 0.9018   |
| 0.0           | 39.0  | 29250 | 1.0034          | 0.8985   |
| 0.0           | 40.0  | 30000 | 1.0043          | 0.8985   |
| 0.0           | 41.0  | 30750 | 0.9850          | 0.9002   |
| 0.0           | 42.0  | 31500 | 0.9989          | 0.8985   |
| 0.0           | 43.0  | 32250 | 1.0060          | 0.8985   |
| 0.0           | 44.0  | 33000 | 1.0153          | 0.8985   |
| 0.0024        | 45.0  | 33750 | 1.0191          | 0.8985   |
| 0.0           | 46.0  | 34500 | 1.0252          | 0.8985   |
| 0.0           | 47.0  | 35250 | 1.0250          | 0.8985   |
| 0.0           | 48.0  | 36000 | 1.0256          | 0.8985   |
| 0.0           | 49.0  | 36750 | 1.0285          | 0.8985   |
| 0.0           | 50.0  | 37500 | 1.0292          | 0.8985   |


### 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