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# smids_5x_deit_base_sgd_00001_fold1

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: 1.0498
- Accuracy: 0.5008

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1042        | 1.0   | 376   | 1.0929          | 0.3856   |
| 1.1057        | 2.0   | 752   | 1.0909          | 0.3923   |
| 1.1149        | 3.0   | 1128  | 1.0890          | 0.3940   |
| 1.1189        | 4.0   | 1504  | 1.0872          | 0.3907   |
| 1.1034        | 5.0   | 1880  | 1.0854          | 0.3973   |
| 1.0984        | 6.0   | 2256  | 1.0837          | 0.4023   |
| 1.1017        | 7.0   | 2632  | 1.0821          | 0.4073   |
| 1.0896        | 8.0   | 3008  | 1.0805          | 0.4157   |
| 1.0923        | 9.0   | 3384  | 1.0789          | 0.4240   |
| 1.0904        | 10.0  | 3760  | 1.0774          | 0.4257   |
| 1.0756        | 11.0  | 4136  | 1.0759          | 0.4324   |
| 1.0821        | 12.0  | 4512  | 1.0745          | 0.4357   |
| 1.0908        | 13.0  | 4888  | 1.0731          | 0.4424   |
| 1.0966        | 14.0  | 5264  | 1.0718          | 0.4441   |
| 1.0817        | 15.0  | 5640  | 1.0706          | 0.4441   |
| 1.0679        | 16.0  | 6016  | 1.0693          | 0.4457   |
| 1.0876        | 17.0  | 6392  | 1.0681          | 0.4457   |
| 1.064         | 18.0  | 6768  | 1.0670          | 0.4474   |
| 1.072         | 19.0  | 7144  | 1.0658          | 0.4474   |
| 1.09          | 20.0  | 7520  | 1.0648          | 0.4474   |
| 1.081         | 21.0  | 7896  | 1.0637          | 0.4508   |
| 1.0655        | 22.0  | 8272  | 1.0627          | 0.4558   |
| 1.0774        | 23.0  | 8648  | 1.0618          | 0.4574   |
| 1.0736        | 24.0  | 9024  | 1.0609          | 0.4608   |
| 1.0774        | 25.0  | 9400  | 1.0600          | 0.4691   |
| 1.055         | 26.0  | 9776  | 1.0591          | 0.4691   |
| 1.0689        | 27.0  | 10152 | 1.0583          | 0.4674   |
| 1.0612        | 28.0  | 10528 | 1.0576          | 0.4691   |
| 1.0701        | 29.0  | 10904 | 1.0568          | 0.4691   |
| 1.0631        | 30.0  | 11280 | 1.0561          | 0.4741   |
| 1.0623        | 31.0  | 11656 | 1.0555          | 0.4758   |
| 1.0571        | 32.0  | 12032 | 1.0549          | 0.4791   |
| 1.0769        | 33.0  | 12408 | 1.0543          | 0.4841   |
| 1.0511        | 34.0  | 12784 | 1.0537          | 0.4891   |
| 1.0652        | 35.0  | 13160 | 1.0532          | 0.4891   |
| 1.0631        | 36.0  | 13536 | 1.0527          | 0.4908   |
| 1.0446        | 37.0  | 13912 | 1.0523          | 0.4908   |
| 1.0591        | 38.0  | 14288 | 1.0519          | 0.4925   |
| 1.0589        | 39.0  | 14664 | 1.0516          | 0.4925   |
| 1.0552        | 40.0  | 15040 | 1.0512          | 0.4942   |
| 1.0353        | 41.0  | 15416 | 1.0509          | 0.4925   |
| 1.0348        | 42.0  | 15792 | 1.0507          | 0.4958   |
| 1.0561        | 43.0  | 16168 | 1.0505          | 0.4992   |
| 1.0679        | 44.0  | 16544 | 1.0503          | 0.4992   |
| 1.0611        | 45.0  | 16920 | 1.0501          | 0.5008   |
| 1.0413        | 46.0  | 17296 | 1.0500          | 0.5008   |
| 1.0517        | 47.0  | 17672 | 1.0499          | 0.5008   |
| 1.0644        | 48.0  | 18048 | 1.0499          | 0.5008   |
| 1.052         | 49.0  | 18424 | 1.0498          | 0.5008   |
| 1.0428        | 50.0  | 18800 | 1.0498          | 0.5008   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

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