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# hushem_40x_deit_base_sgd_00001_fold4

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: 1.3940
- Accuracy: 0.3095

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4075        | 1.0   | 219   | 1.4139          | 0.2857   |
| 1.4006        | 2.0   | 438   | 1.4128          | 0.2857   |
| 1.397         | 3.0   | 657   | 1.4119          | 0.2857   |
| 1.4009        | 4.0   | 876   | 1.4109          | 0.2857   |
| 1.4192        | 5.0   | 1095  | 1.4100          | 0.2857   |
| 1.4068        | 6.0   | 1314  | 1.4091          | 0.2857   |
| 1.4024        | 7.0   | 1533  | 1.4083          | 0.2857   |
| 1.3965        | 8.0   | 1752  | 1.4075          | 0.2857   |
| 1.3783        | 9.0   | 1971  | 1.4067          | 0.3095   |
| 1.3738        | 10.0  | 2190  | 1.4060          | 0.3095   |
| 1.3936        | 11.0  | 2409  | 1.4053          | 0.3095   |
| 1.3746        | 12.0  | 2628  | 1.4046          | 0.3095   |
| 1.3536        | 13.0  | 2847  | 1.4040          | 0.3095   |
| 1.4005        | 14.0  | 3066  | 1.4033          | 0.3095   |
| 1.3798        | 15.0  | 3285  | 1.4027          | 0.3095   |
| 1.3748        | 16.0  | 3504  | 1.4022          | 0.3095   |
| 1.3581        | 17.0  | 3723  | 1.4016          | 0.3095   |
| 1.3695        | 18.0  | 3942  | 1.4011          | 0.3095   |
| 1.366         | 19.0  | 4161  | 1.4006          | 0.3095   |
| 1.3735        | 20.0  | 4380  | 1.4001          | 0.3095   |
| 1.3732        | 21.0  | 4599  | 1.3997          | 0.3095   |
| 1.3632        | 22.0  | 4818  | 1.3992          | 0.3095   |
| 1.3525        | 23.0  | 5037  | 1.3988          | 0.3095   |
| 1.3845        | 24.0  | 5256  | 1.3984          | 0.3095   |
| 1.363         | 25.0  | 5475  | 1.3980          | 0.3095   |
| 1.3693        | 26.0  | 5694  | 1.3977          | 0.3095   |
| 1.3693        | 27.0  | 5913  | 1.3973          | 0.3095   |
| 1.3914        | 28.0  | 6132  | 1.3970          | 0.3095   |
| 1.3857        | 29.0  | 6351  | 1.3967          | 0.3095   |
| 1.3681        | 30.0  | 6570  | 1.3964          | 0.3095   |
| 1.3619        | 31.0  | 6789  | 1.3962          | 0.3095   |
| 1.3666        | 32.0  | 7008  | 1.3959          | 0.3095   |
| 1.3733        | 33.0  | 7227  | 1.3957          | 0.3095   |
| 1.3572        | 34.0  | 7446  | 1.3955          | 0.3095   |
| 1.3715        | 35.0  | 7665  | 1.3953          | 0.3095   |
| 1.3581        | 36.0  | 7884  | 1.3951          | 0.3095   |
| 1.3453        | 37.0  | 8103  | 1.3949          | 0.3095   |
| 1.3666        | 38.0  | 8322  | 1.3948          | 0.3095   |
| 1.3416        | 39.0  | 8541  | 1.3946          | 0.3095   |
| 1.3435        | 40.0  | 8760  | 1.3945          | 0.3095   |
| 1.3731        | 41.0  | 8979  | 1.3944          | 0.3095   |
| 1.3652        | 42.0  | 9198  | 1.3943          | 0.3095   |
| 1.3499        | 43.0  | 9417  | 1.3942          | 0.3095   |
| 1.3629        | 44.0  | 9636  | 1.3941          | 0.3095   |
| 1.3332        | 45.0  | 9855  | 1.3941          | 0.3095   |
| 1.3535        | 46.0  | 10074 | 1.3940          | 0.3095   |
| 1.3876        | 47.0  | 10293 | 1.3940          | 0.3095   |
| 1.363         | 48.0  | 10512 | 1.3940          | 0.3095   |
| 1.3575        | 49.0  | 10731 | 1.3940          | 0.3095   |
| 1.3466        | 50.0  | 10950 | 1.3940          | 0.3095   |


### Framework versions

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

01_normal 02_tapered 03_pyriform 04_amorphous