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# smids_10x_deit_small_rms_001_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: 0.7042
- Accuracy: 0.8369

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8864        | 1.0   | 750   | 0.8248          | 0.5474   |
| 0.7722        | 2.0   | 1500  | 0.8988          | 0.5092   |
| 0.7721        | 3.0   | 2250  | 0.7604          | 0.6456   |
| 0.7086        | 4.0   | 3000  | 0.6560          | 0.7371   |
| 0.6588        | 5.0   | 3750  | 0.6906          | 0.7088   |
| 0.5657        | 6.0   | 4500  | 0.5964          | 0.7654   |
| 0.5826        | 7.0   | 5250  | 0.5186          | 0.7854   |
| 0.5637        | 8.0   | 6000  | 0.5513          | 0.7737   |
| 0.5395        | 9.0   | 6750  | 0.5704          | 0.7537   |
| 0.5342        | 10.0  | 7500  | 0.4931          | 0.7987   |
| 0.5349        | 11.0  | 8250  | 0.5109          | 0.7937   |
| 0.596         | 12.0  | 9000  | 0.5425          | 0.7804   |
| 0.5878        | 13.0  | 9750  | 0.4766          | 0.8103   |
| 0.4609        | 14.0  | 10500 | 0.7520          | 0.7022   |
| 0.4491        | 15.0  | 11250 | 0.5442          | 0.7504   |
| 0.4637        | 16.0  | 12000 | 0.5054          | 0.8136   |
| 0.4699        | 17.0  | 12750 | 0.4927          | 0.8037   |
| 0.4528        | 18.0  | 13500 | 0.4576          | 0.8120   |
| 0.4797        | 19.0  | 14250 | 0.4748          | 0.7970   |
| 0.4704        | 20.0  | 15000 | 0.4438          | 0.8070   |
| 0.4406        | 21.0  | 15750 | 0.4383          | 0.8153   |
| 0.4289        | 22.0  | 16500 | 0.4522          | 0.8120   |
| 0.4219        | 23.0  | 17250 | 0.4457          | 0.8286   |
| 0.3979        | 24.0  | 18000 | 0.4791          | 0.8203   |
| 0.476         | 25.0  | 18750 | 0.4867          | 0.8136   |
| 0.4039        | 26.0  | 19500 | 0.4638          | 0.8319   |
| 0.4302        | 27.0  | 20250 | 0.4222          | 0.8303   |
| 0.4091        | 28.0  | 21000 | 0.4516          | 0.8270   |
| 0.3603        | 29.0  | 21750 | 0.5085          | 0.8170   |
| 0.4414        | 30.0  | 22500 | 0.4568          | 0.8353   |
| 0.3768        | 31.0  | 23250 | 0.4984          | 0.8253   |
| 0.3126        | 32.0  | 24000 | 0.4428          | 0.8436   |
| 0.3269        | 33.0  | 24750 | 0.4871          | 0.8236   |
| 0.3283        | 34.0  | 25500 | 0.4708          | 0.8253   |
| 0.3471        | 35.0  | 26250 | 0.4869          | 0.8353   |
| 0.3619        | 36.0  | 27000 | 0.5210          | 0.8153   |
| 0.4176        | 37.0  | 27750 | 0.4744          | 0.8353   |
| 0.3395        | 38.0  | 28500 | 0.5334          | 0.8386   |
| 0.2458        | 39.0  | 29250 | 0.5218          | 0.8286   |
| 0.3331        | 40.0  | 30000 | 0.5874          | 0.8186   |
| 0.3063        | 41.0  | 30750 | 0.5488          | 0.8236   |
| 0.2956        | 42.0  | 31500 | 0.5739          | 0.8220   |
| 0.3105        | 43.0  | 32250 | 0.5441          | 0.8369   |
| 0.2918        | 44.0  | 33000 | 0.6039          | 0.8303   |
| 0.2418        | 45.0  | 33750 | 0.6214          | 0.8303   |
| 0.2859        | 46.0  | 34500 | 0.6601          | 0.8286   |
| 0.2507        | 47.0  | 35250 | 0.6435          | 0.8369   |
| 0.2443        | 48.0  | 36000 | 0.6789          | 0.8336   |
| 0.2825        | 49.0  | 36750 | 0.6931          | 0.8336   |
| 0.1845        | 50.0  | 37500 | 0.7042          | 0.8369   |


### 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