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# hushem_40x_deit_tiny_sgd_0001_fold2

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: 1.2757
- Accuracy: 0.4667

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3997        | 1.0   | 215   | 1.4647          | 0.2444   |
| 1.3365        | 2.0   | 430   | 1.4330          | 0.2222   |
| 1.2917        | 3.0   | 645   | 1.4094          | 0.2444   |
| 1.2427        | 4.0   | 860   | 1.3927          | 0.2667   |
| 1.2441        | 5.0   | 1075  | 1.3794          | 0.2667   |
| 1.1872        | 6.0   | 1290  | 1.3684          | 0.2667   |
| 1.1795        | 7.0   | 1505  | 1.3589          | 0.3111   |
| 1.1209        | 8.0   | 1720  | 1.3504          | 0.3333   |
| 1.1403        | 9.0   | 1935  | 1.3427          | 0.3778   |
| 1.0825        | 10.0  | 2150  | 1.3360          | 0.3778   |
| 1.0205        | 11.0  | 2365  | 1.3306          | 0.3778   |
| 1.0287        | 12.0  | 2580  | 1.3256          | 0.4222   |
| 1.0526        | 13.0  | 2795  | 1.3201          | 0.4444   |
| 0.979         | 14.0  | 3010  | 1.3158          | 0.4444   |
| 1.009         | 15.0  | 3225  | 1.3119          | 0.4444   |
| 1.0242        | 16.0  | 3440  | 1.3068          | 0.4444   |
| 0.9586        | 17.0  | 3655  | 1.3041          | 0.4222   |
| 0.9705        | 18.0  | 3870  | 1.3009          | 0.4222   |
| 0.9559        | 19.0  | 4085  | 1.2993          | 0.4222   |
| 0.95          | 20.0  | 4300  | 1.2983          | 0.4444   |
| 0.9501        | 21.0  | 4515  | 1.2955          | 0.4444   |
| 0.9287        | 22.0  | 4730  | 1.2949          | 0.4444   |
| 0.8978        | 23.0  | 4945  | 1.2936          | 0.4444   |
| 0.8221        | 24.0  | 5160  | 1.2913          | 0.4444   |
| 0.8642        | 25.0  | 5375  | 1.2902          | 0.4444   |
| 0.8893        | 26.0  | 5590  | 1.2888          | 0.4444   |
| 0.8888        | 27.0  | 5805  | 1.2875          | 0.4444   |
| 0.8399        | 28.0  | 6020  | 1.2872          | 0.4444   |
| 0.8384        | 29.0  | 6235  | 1.2862          | 0.4444   |
| 0.8557        | 30.0  | 6450  | 1.2852          | 0.4444   |
| 0.8264        | 31.0  | 6665  | 1.2846          | 0.4444   |
| 0.7947        | 32.0  | 6880  | 1.2839          | 0.4222   |
| 0.7889        | 33.0  | 7095  | 1.2827          | 0.4222   |
| 0.829         | 34.0  | 7310  | 1.2822          | 0.4444   |
| 0.754         | 35.0  | 7525  | 1.2813          | 0.4444   |
| 0.7758        | 36.0  | 7740  | 1.2807          | 0.4444   |
| 0.8928        | 37.0  | 7955  | 1.2794          | 0.4444   |
| 0.734         | 38.0  | 8170  | 1.2794          | 0.4444   |
| 0.7594        | 39.0  | 8385  | 1.2785          | 0.4444   |
| 0.775         | 40.0  | 8600  | 1.2779          | 0.4444   |
| 0.7835        | 41.0  | 8815  | 1.2773          | 0.4667   |
| 0.7569        | 42.0  | 9030  | 1.2769          | 0.4667   |
| 0.7974        | 43.0  | 9245  | 1.2769          | 0.4667   |
| 0.7959        | 44.0  | 9460  | 1.2766          | 0.4667   |
| 0.8113        | 45.0  | 9675  | 1.2762          | 0.4667   |
| 0.7344        | 46.0  | 9890  | 1.2759          | 0.4667   |
| 0.7955        | 47.0  | 10105 | 1.2758          | 0.4667   |
| 0.7831        | 48.0  | 10320 | 1.2757          | 0.4667   |
| 0.7467        | 49.0  | 10535 | 1.2757          | 0.4667   |
| 0.8192        | 50.0  | 10750 | 1.2757          | 0.4667   |


### 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