
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# smids_10x_deit_base_sgd_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: 0.4033
- Accuracy: 0.8386

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0726        | 1.0   | 750   | 1.0697          | 0.4193   |
| 0.9885        | 2.0   | 1500  | 1.0230          | 0.5324   |
| 0.9161        | 3.0   | 2250  | 0.9631          | 0.6057   |
| 0.8922        | 4.0   | 3000  | 0.8962          | 0.6389   |
| 0.7847        | 5.0   | 3750  | 0.8318          | 0.6922   |
| 0.7649        | 6.0   | 4500  | 0.7745          | 0.7221   |
| 0.6927        | 7.0   | 5250  | 0.7242          | 0.7454   |
| 0.6518        | 8.0   | 6000  | 0.6810          | 0.7521   |
| 0.6316        | 9.0   | 6750  | 0.6449          | 0.7587   |
| 0.5913        | 10.0  | 7500  | 0.6145          | 0.7720   |
| 0.5908        | 11.0  | 8250  | 0.5890          | 0.7770   |
| 0.5365        | 12.0  | 9000  | 0.5673          | 0.7804   |
| 0.5035        | 13.0  | 9750  | 0.5486          | 0.7870   |
| 0.4588        | 14.0  | 10500 | 0.5325          | 0.7937   |
| 0.4827        | 15.0  | 11250 | 0.5186          | 0.7953   |
| 0.4605        | 16.0  | 12000 | 0.5064          | 0.8020   |
| 0.3999        | 17.0  | 12750 | 0.4957          | 0.8037   |
| 0.4114        | 18.0  | 13500 | 0.4861          | 0.8070   |
| 0.4361        | 19.0  | 14250 | 0.4777          | 0.8070   |
| 0.4491        | 20.0  | 15000 | 0.4702          | 0.8136   |
| 0.4325        | 21.0  | 15750 | 0.4634          | 0.8136   |
| 0.3856        | 22.0  | 16500 | 0.4573          | 0.8136   |
| 0.3915        | 23.0  | 17250 | 0.4519          | 0.8136   |
| 0.3628        | 24.0  | 18000 | 0.4469          | 0.8170   |
| 0.4391        | 25.0  | 18750 | 0.4425          | 0.8170   |
| 0.426         | 26.0  | 19500 | 0.4384          | 0.8153   |
| 0.3848        | 27.0  | 20250 | 0.4347          | 0.8170   |
| 0.4075        | 28.0  | 21000 | 0.4313          | 0.8236   |
| 0.367         | 29.0  | 21750 | 0.4282          | 0.8286   |
| 0.3873        | 30.0  | 22500 | 0.4254          | 0.8303   |
| 0.3615        | 31.0  | 23250 | 0.4227          | 0.8319   |
| 0.4147        | 32.0  | 24000 | 0.4203          | 0.8319   |
| 0.3687        | 33.0  | 24750 | 0.4182          | 0.8319   |
| 0.3673        | 34.0  | 25500 | 0.4163          | 0.8353   |
| 0.3984        | 35.0  | 26250 | 0.4145          | 0.8353   |
| 0.4014        | 36.0  | 27000 | 0.4129          | 0.8353   |
| 0.3649        | 37.0  | 27750 | 0.4114          | 0.8353   |
| 0.3739        | 38.0  | 28500 | 0.4101          | 0.8353   |
| 0.3864        | 39.0  | 29250 | 0.4088          | 0.8353   |
| 0.4645        | 40.0  | 30000 | 0.4079          | 0.8369   |
| 0.3819        | 41.0  | 30750 | 0.4069          | 0.8369   |
| 0.3624        | 42.0  | 31500 | 0.4061          | 0.8369   |
| 0.331         | 43.0  | 32250 | 0.4054          | 0.8369   |
| 0.3186        | 44.0  | 33000 | 0.4047          | 0.8369   |
| 0.3184        | 45.0  | 33750 | 0.4043          | 0.8369   |
| 0.3966        | 46.0  | 34500 | 0.4039          | 0.8386   |
| 0.364         | 47.0  | 35250 | 0.4036          | 0.8386   |
| 0.3848        | 48.0  | 36000 | 0.4034          | 0.8386   |
| 0.3506        | 49.0  | 36750 | 0.4033          | 0.8386   |
| 0.3494        | 50.0  | 37500 | 0.4033          | 0.8386   |


### 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