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# smids_10x_deit_base_sgd_001_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: 0.2876
- 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.569         | 1.0   | 750   | 0.5992          | 0.7754   |
| 0.3171        | 2.0   | 1500  | 0.4389          | 0.8136   |
| 0.3055        | 3.0   | 2250  | 0.3875          | 0.8403   |
| 0.3488        | 4.0   | 3000  | 0.3592          | 0.8519   |
| 0.2869        | 5.0   | 3750  | 0.3460          | 0.8586   |
| 0.2849        | 6.0   | 4500  | 0.3276          | 0.8669   |
| 0.2527        | 7.0   | 5250  | 0.3203          | 0.8719   |
| 0.2343        | 8.0   | 6000  | 0.3120          | 0.8802   |
| 0.2514        | 9.0   | 6750  | 0.3061          | 0.8802   |
| 0.2509        | 10.0  | 7500  | 0.3037          | 0.8819   |
| 0.2672        | 11.0  | 8250  | 0.2976          | 0.8835   |
| 0.2698        | 12.0  | 9000  | 0.2941          | 0.8885   |
| 0.2268        | 13.0  | 9750  | 0.2935          | 0.8869   |
| 0.1786        | 14.0  | 10500 | 0.2916          | 0.8835   |
| 0.166         | 15.0  | 11250 | 0.2890          | 0.8885   |
| 0.192         | 16.0  | 12000 | 0.2883          | 0.8869   |
| 0.1507        | 17.0  | 12750 | 0.2880          | 0.8869   |
| 0.1815        | 18.0  | 13500 | 0.2859          | 0.8885   |
| 0.1982        | 19.0  | 14250 | 0.2882          | 0.8902   |
| 0.2           | 20.0  | 15000 | 0.2835          | 0.8869   |
| 0.1801        | 21.0  | 15750 | 0.2845          | 0.8902   |
| 0.1194        | 22.0  | 16500 | 0.2833          | 0.8918   |
| 0.1747        | 23.0  | 17250 | 0.2854          | 0.8935   |
| 0.1283        | 24.0  | 18000 | 0.2844          | 0.8935   |
| 0.1821        | 25.0  | 18750 | 0.2833          | 0.8952   |
| 0.2094        | 26.0  | 19500 | 0.2844          | 0.8918   |
| 0.1857        | 27.0  | 20250 | 0.2847          | 0.8985   |
| 0.1436        | 28.0  | 21000 | 0.2850          | 0.8952   |
| 0.1786        | 29.0  | 21750 | 0.2841          | 0.8985   |
| 0.1785        | 30.0  | 22500 | 0.2847          | 0.8952   |
| 0.1889        | 31.0  | 23250 | 0.2850          | 0.8952   |
| 0.1905        | 32.0  | 24000 | 0.2845          | 0.8968   |
| 0.1812        | 33.0  | 24750 | 0.2853          | 0.8935   |
| 0.1723        | 34.0  | 25500 | 0.2858          | 0.8952   |
| 0.1909        | 35.0  | 26250 | 0.2850          | 0.8952   |
| 0.1661        | 36.0  | 27000 | 0.2862          | 0.8985   |
| 0.1719        | 37.0  | 27750 | 0.2861          | 0.8935   |
| 0.156         | 38.0  | 28500 | 0.2859          | 0.9002   |
| 0.1428        | 39.0  | 29250 | 0.2859          | 0.8985   |
| 0.229         | 40.0  | 30000 | 0.2871          | 0.8985   |
| 0.2023        | 41.0  | 30750 | 0.2873          | 0.8968   |
| 0.1583        | 42.0  | 31500 | 0.2870          | 0.8968   |
| 0.1454        | 43.0  | 32250 | 0.2875          | 0.8985   |
| 0.1231        | 44.0  | 33000 | 0.2872          | 0.8985   |
| 0.1299        | 45.0  | 33750 | 0.2879          | 0.8985   |
| 0.1947        | 46.0  | 34500 | 0.2876          | 0.8985   |
| 0.1774        | 47.0  | 35250 | 0.2875          | 0.8985   |
| 0.1319        | 48.0  | 36000 | 0.2874          | 0.8968   |
| 0.1298        | 49.0  | 36750 | 0.2876          | 0.8985   |
| 0.1348        | 50.0  | 37500 | 0.2876          | 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