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# hushem_40x_deit_tiny_sgd_0001_fold1

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: 1.1471
- Accuracy: 0.5333

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4892        | 1.0   | 215   | 1.3892          | 0.2222   |
| 1.3775        | 2.0   | 430   | 1.3751          | 0.2889   |
| 1.3266        | 3.0   | 645   | 1.3631          | 0.3111   |
| 1.2619        | 4.0   | 860   | 1.3523          | 0.3111   |
| 1.235         | 5.0   | 1075  | 1.3429          | 0.3111   |
| 1.1826        | 6.0   | 1290  | 1.3347          | 0.3778   |
| 1.2015        | 7.0   | 1505  | 1.3284          | 0.3778   |
| 1.2072        | 8.0   | 1720  | 1.3225          | 0.4      |
| 1.1254        | 9.0   | 1935  | 1.3170          | 0.4      |
| 1.1293        | 10.0  | 2150  | 1.3118          | 0.3556   |
| 1.0925        | 11.0  | 2365  | 1.3069          | 0.3778   |
| 1.0731        | 12.0  | 2580  | 1.3024          | 0.3778   |
| 1.0421        | 13.0  | 2795  | 1.2976          | 0.3778   |
| 1.0531        | 14.0  | 3010  | 1.2928          | 0.3778   |
| 1.0284        | 15.0  | 3225  | 1.2877          | 0.3778   |
| 1.0283        | 16.0  | 3440  | 1.2824          | 0.4      |
| 1.0283        | 17.0  | 3655  | 1.2767          | 0.4222   |
| 1.0038        | 18.0  | 3870  | 1.2712          | 0.4444   |
| 0.9952        | 19.0  | 4085  | 1.2654          | 0.4444   |
| 0.9413        | 20.0  | 4300  | 1.2587          | 0.4444   |
| 0.9562        | 21.0  | 4515  | 1.2529          | 0.4667   |
| 1.0163        | 22.0  | 4730  | 1.2467          | 0.4667   |
| 0.9391        | 23.0  | 4945  | 1.2401          | 0.4667   |
| 0.955         | 24.0  | 5160  | 1.2340          | 0.4889   |
| 0.9454        | 25.0  | 5375  | 1.2281          | 0.4889   |
| 0.9013        | 26.0  | 5590  | 1.2229          | 0.4889   |
| 0.8818        | 27.0  | 5805  | 1.2169          | 0.5111   |
| 0.8594        | 28.0  | 6020  | 1.2115          | 0.5111   |
| 0.8984        | 29.0  | 6235  | 1.2064          | 0.5111   |
| 0.8277        | 30.0  | 6450  | 1.2009          | 0.5111   |
| 0.8636        | 31.0  | 6665  | 1.1955          | 0.5111   |
| 0.8466        | 32.0  | 6880  | 1.1910          | 0.5111   |
| 0.8955        | 33.0  | 7095  | 1.1866          | 0.5111   |
| 0.817         | 34.0  | 7310  | 1.1825          | 0.5111   |
| 0.8132        | 35.0  | 7525  | 1.1781          | 0.5111   |
| 0.7914        | 36.0  | 7740  | 1.1742          | 0.5111   |
| 0.835         | 37.0  | 7955  | 1.1705          | 0.5111   |
| 0.8383        | 38.0  | 8170  | 1.1668          | 0.5111   |
| 0.828         | 39.0  | 8385  | 1.1638          | 0.5111   |
| 0.7822        | 40.0  | 8600  | 1.1606          | 0.5111   |
| 0.8243        | 41.0  | 8815  | 1.1580          | 0.5333   |
| 0.9371        | 42.0  | 9030  | 1.1556          | 0.5333   |
| 0.8482        | 43.0  | 9245  | 1.1533          | 0.5333   |
| 0.8054        | 44.0  | 9460  | 1.1516          | 0.5333   |
| 0.8152        | 45.0  | 9675  | 1.1501          | 0.5333   |
| 0.8013        | 46.0  | 9890  | 1.1489          | 0.5333   |
| 0.7786        | 47.0  | 10105 | 1.1481          | 0.5333   |
| 0.7918        | 48.0  | 10320 | 1.1474          | 0.5333   |
| 0.8671        | 49.0  | 10535 | 1.1471          | 0.5333   |
| 0.8286        | 50.0  | 10750 | 1.1471          | 0.5333   |


### 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