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# smids_10x_deit_small_sgd_0001_fold2

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: 0.4097
- Accuracy: 0.8270

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9968        | 1.0   | 750   | 1.0169          | 0.4659   |
| 0.9174        | 2.0   | 1500  | 0.9543          | 0.5308   |
| 0.8121        | 3.0   | 2250  | 0.8838          | 0.6273   |
| 0.7871        | 4.0   | 3000  | 0.8228          | 0.6522   |
| 0.691         | 5.0   | 3750  | 0.7665          | 0.6922   |
| 0.6733        | 6.0   | 4500  | 0.7184          | 0.7271   |
| 0.611         | 7.0   | 5250  | 0.6739          | 0.7488   |
| 0.5495        | 8.0   | 6000  | 0.6348          | 0.7537   |
| 0.5871        | 9.0   | 6750  | 0.6046          | 0.7587   |
| 0.5362        | 10.0  | 7500  | 0.5781          | 0.7754   |
| 0.5478        | 11.0  | 8250  | 0.5567          | 0.7754   |
| 0.5521        | 12.0  | 9000  | 0.5409          | 0.7804   |
| 0.475         | 13.0  | 9750  | 0.5265          | 0.7787   |
| 0.4124        | 14.0  | 10500 | 0.5147          | 0.7887   |
| 0.4689        | 15.0  | 11250 | 0.5048          | 0.7870   |
| 0.4042        | 16.0  | 12000 | 0.4956          | 0.7903   |
| 0.3787        | 17.0  | 12750 | 0.4873          | 0.7937   |
| 0.4203        | 18.0  | 13500 | 0.4799          | 0.7937   |
| 0.4173        | 19.0  | 14250 | 0.4729          | 0.7987   |
| 0.4444        | 20.0  | 15000 | 0.4676          | 0.8020   |
| 0.4225        | 21.0  | 15750 | 0.4619          | 0.8020   |
| 0.3886        | 22.0  | 16500 | 0.4572          | 0.8070   |
| 0.3882        | 23.0  | 17250 | 0.4523          | 0.8120   |
| 0.3793        | 24.0  | 18000 | 0.4484          | 0.8103   |
| 0.4027        | 25.0  | 18750 | 0.4443          | 0.8136   |
| 0.4864        | 26.0  | 19500 | 0.4411          | 0.8136   |
| 0.4229        | 27.0  | 20250 | 0.4378          | 0.8153   |
| 0.4258        | 28.0  | 21000 | 0.4349          | 0.8153   |
| 0.3905        | 29.0  | 21750 | 0.4322          | 0.8170   |
| 0.4099        | 30.0  | 22500 | 0.4297          | 0.8170   |
| 0.3721        | 31.0  | 23250 | 0.4276          | 0.8186   |
| 0.4104        | 32.0  | 24000 | 0.4255          | 0.8203   |
| 0.3815        | 33.0  | 24750 | 0.4237          | 0.8220   |
| 0.3966        | 34.0  | 25500 | 0.4218          | 0.8220   |
| 0.4057        | 35.0  | 26250 | 0.4202          | 0.8220   |
| 0.4004        | 36.0  | 27000 | 0.4187          | 0.8220   |
| 0.3921        | 37.0  | 27750 | 0.4174          | 0.8220   |
| 0.4046        | 38.0  | 28500 | 0.4161          | 0.8220   |
| 0.3819        | 39.0  | 29250 | 0.4149          | 0.8220   |
| 0.4626        | 40.0  | 30000 | 0.4139          | 0.8236   |
| 0.4062        | 41.0  | 30750 | 0.4130          | 0.8236   |
| 0.3793        | 42.0  | 31500 | 0.4123          | 0.8253   |
| 0.3246        | 43.0  | 32250 | 0.4116          | 0.8253   |
| 0.3382        | 44.0  | 33000 | 0.4110          | 0.8270   |
| 0.3636        | 45.0  | 33750 | 0.4106          | 0.8270   |
| 0.4008        | 46.0  | 34500 | 0.4102          | 0.8270   |
| 0.3708        | 47.0  | 35250 | 0.4099          | 0.8270   |
| 0.3436        | 48.0  | 36000 | 0.4098          | 0.8270   |
| 0.3738        | 49.0  | 36750 | 0.4097          | 0.8270   |
| 0.373         | 50.0  | 37500 | 0.4097          | 0.8270   |


### 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