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# hushem_40x_deit_tiny_rms_0001_fold4

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: 0.4037
- Accuracy: 0.9524

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1838        | 1.0   | 219   | 0.4926          | 0.8571   |
| 0.0446        | 2.0   | 438   | 0.2754          | 0.9286   |
| 0.0295        | 3.0   | 657   | 0.9751          | 0.8810   |
| 0.0096        | 4.0   | 876   | 0.1123          | 0.9762   |
| 0.0055        | 5.0   | 1095  | 0.3687          | 0.9048   |
| 0.0033        | 6.0   | 1314  | 0.3076          | 0.9524   |
| 0.0283        | 7.0   | 1533  | 0.8089          | 0.8571   |
| 0.0044        | 8.0   | 1752  | 0.2435          | 0.9286   |
| 0.0018        | 9.0   | 1971  | 0.7038          | 0.8571   |
| 0.0191        | 10.0  | 2190  | 0.5242          | 0.9048   |
| 0.0001        | 11.0  | 2409  | 0.8130          | 0.9286   |
| 0.0007        | 12.0  | 2628  | 0.6030          | 0.9048   |
| 0.0189        | 13.0  | 2847  | 0.5406          | 0.9048   |
| 0.0002        | 14.0  | 3066  | 0.6774          | 0.8571   |
| 0.0018        | 15.0  | 3285  | 0.6982          | 0.9286   |
| 0.0001        | 16.0  | 3504  | 0.3877          | 0.9524   |
| 0.0008        | 17.0  | 3723  | 0.6996          | 0.8810   |
| 0.0           | 18.0  | 3942  | 0.5507          | 0.9286   |
| 0.0           | 19.0  | 4161  | 0.3796          | 0.9524   |
| 0.0001        | 20.0  | 4380  | 0.3967          | 0.9286   |
| 0.0           | 21.0  | 4599  | 0.4081          | 0.9286   |
| 0.0           | 22.0  | 4818  | 0.3898          | 0.9286   |
| 0.0           | 23.0  | 5037  | 0.3709          | 0.9286   |
| 0.0           | 24.0  | 5256  | 0.3640          | 0.9524   |
| 0.0           | 25.0  | 5475  | 0.3789          | 0.9524   |
| 0.0           | 26.0  | 5694  | 0.3987          | 0.9286   |
| 0.0           | 27.0  | 5913  | 0.4326          | 0.9286   |
| 0.0           | 28.0  | 6132  | 0.4566          | 0.9286   |
| 0.0           | 29.0  | 6351  | 0.4673          | 0.9286   |
| 0.0           | 30.0  | 6570  | 0.4642          | 0.9286   |
| 0.0           | 31.0  | 6789  | 0.4534          | 0.9286   |
| 0.0           | 32.0  | 7008  | 0.4388          | 0.9286   |
| 0.0           | 33.0  | 7227  | 0.4268          | 0.9286   |
| 0.0           | 34.0  | 7446  | 0.4182          | 0.9286   |
| 0.0           | 35.0  | 7665  | 0.4134          | 0.9286   |
| 0.0           | 36.0  | 7884  | 0.4102          | 0.9286   |
| 0.0           | 37.0  | 8103  | 0.4079          | 0.9286   |
| 0.0           | 38.0  | 8322  | 0.4066          | 0.9286   |
| 0.0           | 39.0  | 8541  | 0.4041          | 0.9286   |
| 0.0           | 40.0  | 8760  | 0.4048          | 0.9286   |
| 0.0           | 41.0  | 8979  | 0.4034          | 0.9524   |
| 0.0           | 42.0  | 9198  | 0.4032          | 0.9524   |
| 0.0           | 43.0  | 9417  | 0.4038          | 0.9524   |
| 0.0           | 44.0  | 9636  | 0.4040          | 0.9524   |
| 0.0           | 45.0  | 9855  | 0.4040          | 0.9524   |
| 0.0           | 46.0  | 10074 | 0.4038          | 0.9524   |
| 0.0           | 47.0  | 10293 | 0.4038          | 0.9524   |
| 0.0           | 48.0  | 10512 | 0.4039          | 0.9524   |
| 0.0           | 49.0  | 10731 | 0.4037          | 0.9524   |
| 0.0           | 50.0  | 10950 | 0.4037          | 0.9524   |


### 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