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# hushem_40x_deit_tiny_sgd_0001_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: 1.1237
- Accuracy: 0.5122

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4318        | 1.0   | 220   | 1.6149          | 0.1707   |
| 1.3729        | 2.0   | 440   | 1.5770          | 0.1951   |
| 1.3561        | 3.0   | 660   | 1.5432          | 0.1707   |
| 1.3096        | 4.0   | 880   | 1.5177          | 0.1463   |
| 1.2756        | 5.0   | 1100  | 1.4946          | 0.1951   |
| 1.2485        | 6.0   | 1320  | 1.4738          | 0.2195   |
| 1.1719        | 7.0   | 1540  | 1.4569          | 0.2195   |
| 1.1324        | 8.0   | 1760  | 1.4401          | 0.2439   |
| 1.1522        | 9.0   | 1980  | 1.4251          | 0.2683   |
| 1.1548        | 10.0  | 2200  | 1.4097          | 0.2439   |
| 1.1099        | 11.0  | 2420  | 1.3960          | 0.2683   |
| 1.0799        | 12.0  | 2640  | 1.3821          | 0.2683   |
| 1.072         | 13.0  | 2860  | 1.3689          | 0.2927   |
| 1.0381        | 14.0  | 3080  | 1.3552          | 0.3171   |
| 1.0533        | 15.0  | 3300  | 1.3423          | 0.2927   |
| 1.0294        | 16.0  | 3520  | 1.3293          | 0.3171   |
| 1.004         | 17.0  | 3740  | 1.3169          | 0.3171   |
| 1.0138        | 18.0  | 3960  | 1.3048          | 0.3171   |
| 0.9902        | 19.0  | 4180  | 1.2935          | 0.3171   |
| 0.9047        | 20.0  | 4400  | 1.2817          | 0.3171   |
| 0.9213        | 21.0  | 4620  | 1.2707          | 0.3415   |
| 0.9555        | 22.0  | 4840  | 1.2595          | 0.3415   |
| 0.9607        | 23.0  | 5060  | 1.2491          | 0.3415   |
| 0.9344        | 24.0  | 5280  | 1.2391          | 0.3415   |
| 0.8688        | 25.0  | 5500  | 1.2295          | 0.3902   |
| 0.9175        | 26.0  | 5720  | 1.2208          | 0.4146   |
| 0.887         | 27.0  | 5940  | 1.2120          | 0.4390   |
| 0.905         | 28.0  | 6160  | 1.2036          | 0.4634   |
| 0.8477        | 29.0  | 6380  | 1.1957          | 0.4878   |
| 0.8486        | 30.0  | 6600  | 1.1887          | 0.4878   |
| 0.9203        | 31.0  | 6820  | 1.1822          | 0.4878   |
| 0.8893        | 32.0  | 7040  | 1.1760          | 0.4878   |
| 0.8469        | 33.0  | 7260  | 1.1702          | 0.4878   |
| 0.7935        | 34.0  | 7480  | 1.1645          | 0.4878   |
| 0.7904        | 35.0  | 7700  | 1.1593          | 0.4878   |
| 0.7994        | 36.0  | 7920  | 1.1544          | 0.5122   |
| 0.8205        | 37.0  | 8140  | 1.1499          | 0.5122   |
| 0.8696        | 38.0  | 8360  | 1.1458          | 0.5122   |
| 0.8262        | 39.0  | 8580  | 1.1421          | 0.5122   |
| 0.7584        | 40.0  | 8800  | 1.1388          | 0.5122   |
| 0.8457        | 41.0  | 9020  | 1.1358          | 0.5122   |
| 0.8307        | 42.0  | 9240  | 1.1331          | 0.5122   |
| 0.8183        | 43.0  | 9460  | 1.1307          | 0.5122   |
| 0.7718        | 44.0  | 9680  | 1.1287          | 0.5122   |
| 0.7855        | 45.0  | 9900  | 1.1271          | 0.5122   |
| 0.7875        | 46.0  | 10120 | 1.1258          | 0.5122   |
| 0.8109        | 47.0  | 10340 | 1.1248          | 0.5122   |
| 0.7297        | 48.0  | 10560 | 1.1241          | 0.5122   |
| 0.7352        | 49.0  | 10780 | 1.1238          | 0.5122   |
| 0.7935        | 50.0  | 11000 | 1.1237          | 0.5122   |


### 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