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# smids_1x_deit_small_adamax_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: 0.8040
- Accuracy: 0.8769

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3172        | 1.0   | 75   | 0.3354          | 0.8636   |
| 0.1909        | 2.0   | 150  | 0.2950          | 0.8785   |
| 0.1696        | 3.0   | 225  | 0.3030          | 0.8902   |
| 0.1088        | 4.0   | 300  | 0.3512          | 0.8735   |
| 0.0828        | 5.0   | 375  | 0.3819          | 0.8719   |
| 0.0816        | 6.0   | 450  | 0.5235          | 0.8735   |
| 0.017         | 7.0   | 525  | 0.4625          | 0.8802   |
| 0.0096        | 8.0   | 600  | 0.6732          | 0.8536   |
| 0.0297        | 9.0   | 675  | 0.5099          | 0.8852   |
| 0.0129        | 10.0  | 750  | 0.6168          | 0.8819   |
| 0.0004        | 11.0  | 825  | 0.6434          | 0.8769   |
| 0.0003        | 12.0  | 900  | 0.6532          | 0.8752   |
| 0.0115        | 13.0  | 975  | 0.7781          | 0.8669   |
| 0.0025        | 14.0  | 1050 | 0.6839          | 0.8735   |
| 0.0031        | 15.0  | 1125 | 0.6481          | 0.8802   |
| 0.0037        | 16.0  | 1200 | 0.7018          | 0.8719   |
| 0.0001        | 17.0  | 1275 | 0.6843          | 0.8752   |
| 0.0057        | 18.0  | 1350 | 0.6963          | 0.8819   |
| 0.0001        | 19.0  | 1425 | 0.6873          | 0.8802   |
| 0.0036        | 20.0  | 1500 | 0.7059          | 0.8785   |
| 0.0001        | 21.0  | 1575 | 0.7123          | 0.8819   |
| 0.0           | 22.0  | 1650 | 0.7298          | 0.8785   |
| 0.0           | 23.0  | 1725 | 0.7182          | 0.8785   |
| 0.0           | 24.0  | 1800 | 0.7389          | 0.8752   |
| 0.0           | 25.0  | 1875 | 0.7283          | 0.8785   |
| 0.0           | 26.0  | 1950 | 0.7283          | 0.8802   |
| 0.0038        | 27.0  | 2025 | 0.7334          | 0.8819   |
| 0.0034        | 28.0  | 2100 | 0.7554          | 0.8735   |
| 0.0022        | 29.0  | 2175 | 0.7526          | 0.8752   |
| 0.0035        | 30.0  | 2250 | 0.7536          | 0.8769   |
| 0.0026        | 31.0  | 2325 | 0.7690          | 0.8719   |
| 0.0           | 32.0  | 2400 | 0.7598          | 0.8769   |
| 0.0           | 33.0  | 2475 | 0.7644          | 0.8752   |
| 0.0           | 34.0  | 2550 | 0.7770          | 0.8769   |
| 0.0081        | 35.0  | 2625 | 0.7696          | 0.8735   |
| 0.0           | 36.0  | 2700 | 0.7747          | 0.8735   |
| 0.0           | 37.0  | 2775 | 0.7776          | 0.8735   |
| 0.0           | 38.0  | 2850 | 0.7800          | 0.8735   |
| 0.0022        | 39.0  | 2925 | 0.7797          | 0.8735   |
| 0.0           | 40.0  | 3000 | 0.7884          | 0.8752   |
| 0.0028        | 41.0  | 3075 | 0.7926          | 0.8785   |
| 0.0           | 42.0  | 3150 | 0.7941          | 0.8769   |
| 0.0025        | 43.0  | 3225 | 0.7995          | 0.8752   |
| 0.0026        | 44.0  | 3300 | 0.7969          | 0.8752   |
| 0.0           | 45.0  | 3375 | 0.7932          | 0.8785   |
| 0.0           | 46.0  | 3450 | 0.8020          | 0.8752   |
| 0.0023        | 47.0  | 3525 | 0.8011          | 0.8702   |
| 0.0           | 48.0  | 3600 | 0.8043          | 0.8769   |
| 0.0022        | 49.0  | 3675 | 0.8040          | 0.8769   |
| 0.0022        | 50.0  | 3750 | 0.8040          | 0.8769   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

abnormal_sperm non-sperm normal_sperm