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# smids_5x_deit_small_rms_00001_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: 1.3142
- Accuracy: 0.8636

## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.255         | 1.0   | 375   | 0.2860          | 0.8785   |
| 0.192         | 2.0   | 750   | 0.3380          | 0.8719   |
| 0.111         | 3.0   | 1125  | 0.5269          | 0.8536   |
| 0.0357        | 4.0   | 1500  | 0.6082          | 0.8819   |
| 0.0344        | 5.0   | 1875  | 0.7662          | 0.8586   |
| 0.0306        | 6.0   | 2250  | 0.7220          | 0.8735   |
| 0.0004        | 7.0   | 2625  | 1.0133          | 0.8636   |
| 0.0601        | 8.0   | 3000  | 0.9769          | 0.8602   |
| 0.0002        | 9.0   | 3375  | 1.0509          | 0.8719   |
| 0.0001        | 10.0  | 3750  | 1.0508          | 0.8686   |
| 0.0242        | 11.0  | 4125  | 1.1405          | 0.8619   |
| 0.0086        | 12.0  | 4500  | 0.9578          | 0.8735   |
| 0.0           | 13.0  | 4875  | 0.9452          | 0.8702   |
| 0.0167        | 14.0  | 5250  | 1.1793          | 0.8652   |
| 0.0068        | 15.0  | 5625  | 1.1314          | 0.8569   |
| 0.0242        | 16.0  | 6000  | 1.0830          | 0.8686   |
| 0.0116        | 17.0  | 6375  | 1.0898          | 0.8686   |
| 0.0019        | 18.0  | 6750  | 1.1516          | 0.8702   |
| 0.0           | 19.0  | 7125  | 1.1246          | 0.8686   |
| 0.0357        | 20.0  | 7500  | 1.2754          | 0.8419   |
| 0.0167        | 21.0  | 7875  | 1.1083          | 0.8586   |
| 0.0           | 22.0  | 8250  | 1.1597          | 0.8636   |
| 0.0           | 23.0  | 8625  | 1.1775          | 0.8686   |
| 0.0001        | 24.0  | 9000  | 1.1781          | 0.8735   |
| 0.0           | 25.0  | 9375  | 1.1367          | 0.8752   |
| 0.0           | 26.0  | 9750  | 1.2570          | 0.8602   |
| 0.0           | 27.0  | 10125 | 1.2344          | 0.8669   |
| 0.0           | 28.0  | 10500 | 1.2212          | 0.8686   |
| 0.0           | 29.0  | 10875 | 1.1884          | 0.8686   |
| 0.0           | 30.0  | 11250 | 1.1717          | 0.8819   |
| 0.0041        | 31.0  | 11625 | 1.2327          | 0.8735   |
| 0.0049        | 32.0  | 12000 | 1.2073          | 0.8719   |
| 0.0069        | 33.0  | 12375 | 1.2981          | 0.8669   |
| 0.0           | 34.0  | 12750 | 1.3346          | 0.8602   |
| 0.0           | 35.0  | 13125 | 1.2237          | 0.8719   |
| 0.0           | 36.0  | 13500 | 1.2742          | 0.8702   |
| 0.0           | 37.0  | 13875 | 1.3127          | 0.8702   |
| 0.0           | 38.0  | 14250 | 1.3037          | 0.8702   |
| 0.0           | 39.0  | 14625 | 1.3578          | 0.8636   |
| 0.0033        | 40.0  | 15000 | 1.3159          | 0.8636   |
| 0.0           | 41.0  | 15375 | 1.3110          | 0.8686   |
| 0.0024        | 42.0  | 15750 | 1.3216          | 0.8652   |
| 0.0026        | 43.0  | 16125 | 1.3041          | 0.8686   |
| 0.0026        | 44.0  | 16500 | 1.3057          | 0.8669   |
| 0.0024        | 45.0  | 16875 | 1.3146          | 0.8652   |
| 0.0           | 46.0  | 17250 | 1.3144          | 0.8686   |
| 0.0053        | 47.0  | 17625 | 1.3156          | 0.8652   |
| 0.0           | 48.0  | 18000 | 1.3154          | 0.8652   |
| 0.0023        | 49.0  | 18375 | 1.3139          | 0.8652   |
| 0.0024        | 50.0  | 18750 | 1.3142          | 0.8636   |


### 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