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# hushem_40x_deit_tiny_sgd_00001_fold5

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5613
- Accuracy: 0.1951

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4842        | 1.0   | 220   | 1.6446          | 0.1951   |
| 1.5035        | 2.0   | 440   | 1.6414          | 0.1951   |
| 1.4995        | 3.0   | 660   | 1.6381          | 0.1951   |
| 1.5021        | 4.0   | 880   | 1.6349          | 0.1707   |
| 1.454         | 5.0   | 1100  | 1.6317          | 0.1707   |
| 1.4629        | 6.0   | 1320  | 1.6285          | 0.1707   |
| 1.4161        | 7.0   | 1540  | 1.6253          | 0.1707   |
| 1.4101        | 8.0   | 1760  | 1.6223          | 0.1707   |
| 1.4149        | 9.0   | 1980  | 1.6192          | 0.1707   |
| 1.4443        | 10.0  | 2200  | 1.6162          | 0.1707   |
| 1.4163        | 11.0  | 2420  | 1.6133          | 0.1707   |
| 1.4351        | 12.0  | 2640  | 1.6104          | 0.1707   |
| 1.4104        | 13.0  | 2860  | 1.6076          | 0.1707   |
| 1.3915        | 14.0  | 3080  | 1.6048          | 0.1707   |
| 1.4251        | 15.0  | 3300  | 1.6022          | 0.1707   |
| 1.4091        | 16.0  | 3520  | 1.5996          | 0.1951   |
| 1.384         | 17.0  | 3740  | 1.5971          | 0.1951   |
| 1.3979        | 18.0  | 3960  | 1.5947          | 0.1951   |
| 1.3842        | 19.0  | 4180  | 1.5923          | 0.1951   |
| 1.3555        | 20.0  | 4400  | 1.5900          | 0.1951   |
| 1.3519        | 21.0  | 4620  | 1.5879          | 0.1951   |
| 1.3873        | 22.0  | 4840  | 1.5859          | 0.1951   |
| 1.3791        | 23.0  | 5060  | 1.5839          | 0.1951   |
| 1.3799        | 24.0  | 5280  | 1.5820          | 0.1951   |
| 1.3568        | 25.0  | 5500  | 1.5802          | 0.1951   |
| 1.369         | 26.0  | 5720  | 1.5786          | 0.1951   |
| 1.3732        | 27.0  | 5940  | 1.5770          | 0.1951   |
| 1.3491        | 28.0  | 6160  | 1.5754          | 0.1951   |
| 1.3457        | 29.0  | 6380  | 1.5740          | 0.1951   |
| 1.3169        | 30.0  | 6600  | 1.5726          | 0.1951   |
| 1.3748        | 31.0  | 6820  | 1.5714          | 0.1951   |
| 1.3384        | 32.0  | 7040  | 1.5702          | 0.1951   |
| 1.3281        | 33.0  | 7260  | 1.5691          | 0.1951   |
| 1.3359        | 34.0  | 7480  | 1.5681          | 0.1951   |
| 1.3414        | 35.0  | 7700  | 1.5671          | 0.1951   |
| 1.3339        | 36.0  | 7920  | 1.5662          | 0.1951   |
| 1.3778        | 37.0  | 8140  | 1.5654          | 0.1951   |
| 1.3669        | 38.0  | 8360  | 1.5647          | 0.1951   |
| 1.3509        | 39.0  | 8580  | 1.5641          | 0.1951   |
| 1.3269        | 40.0  | 8800  | 1.5635          | 0.1951   |
| 1.3717        | 41.0  | 9020  | 1.5630          | 0.1951   |
| 1.3455        | 42.0  | 9240  | 1.5626          | 0.1951   |
| 1.3737        | 43.0  | 9460  | 1.5622          | 0.1951   |
| 1.3166        | 44.0  | 9680  | 1.5619          | 0.1951   |
| 1.3504        | 45.0  | 9900  | 1.5617          | 0.1951   |
| 1.3509        | 46.0  | 10120 | 1.5615          | 0.1951   |
| 1.3526        | 47.0  | 10340 | 1.5614          | 0.1951   |
| 1.3222        | 48.0  | 10560 | 1.5613          | 0.1951   |
| 1.3165        | 49.0  | 10780 | 1.5613          | 0.1951   |
| 1.3501        | 50.0  | 11000 | 1.5613          | 0.1951   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

01_normal 02_tapered 03_pyriform 04_amorphous