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# hushem_40x_deit_tiny_sgd_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: 1.0643
- Accuracy: 0.5952

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.4577        | 1.0   | 219   | 1.6228          | 0.1667   |
| 1.3428        | 2.0   | 438   | 1.5859          | 0.1429   |
| 1.3515        | 3.0   | 657   | 1.5534          | 0.1667   |
| 1.3164        | 4.0   | 876   | 1.5241          | 0.1905   |
| 1.262         | 5.0   | 1095  | 1.5001          | 0.1905   |
| 1.2747        | 6.0   | 1314  | 1.4781          | 0.1905   |
| 1.2005        | 7.0   | 1533  | 1.4587          | 0.1905   |
| 1.2174        | 8.0   | 1752  | 1.4397          | 0.2143   |
| 1.1711        | 9.0   | 1971  | 1.4197          | 0.2143   |
| 1.1443        | 10.0  | 2190  | 1.4007          | 0.2619   |
| 1.1123        | 11.0  | 2409  | 1.3821          | 0.2857   |
| 1.164         | 12.0  | 2628  | 1.3642          | 0.4048   |
| 1.0774        | 13.0  | 2847  | 1.3471          | 0.3810   |
| 1.1066        | 14.0  | 3066  | 1.3290          | 0.3810   |
| 1.055         | 15.0  | 3285  | 1.3135          | 0.4286   |
| 1.0496        | 16.0  | 3504  | 1.2984          | 0.4048   |
| 1.112         | 17.0  | 3723  | 1.2838          | 0.4286   |
| 1.0058        | 18.0  | 3942  | 1.2696          | 0.4286   |
| 1.0363        | 19.0  | 4161  | 1.2563          | 0.4286   |
| 1.0446        | 20.0  | 4380  | 1.2431          | 0.4286   |
| 1.0301        | 21.0  | 4599  | 1.2308          | 0.4286   |
| 1.0066        | 22.0  | 4818  | 1.2182          | 0.4524   |
| 0.9188        | 23.0  | 5037  | 1.2068          | 0.4524   |
| 0.9729        | 24.0  | 5256  | 1.1969          | 0.5238   |
| 0.9215        | 25.0  | 5475  | 1.1858          | 0.5238   |
| 0.9604        | 26.0  | 5694  | 1.1753          | 0.5476   |
| 0.9173        | 27.0  | 5913  | 1.1663          | 0.5714   |
| 0.9314        | 28.0  | 6132  | 1.1573          | 0.5714   |
| 0.8654        | 29.0  | 6351  | 1.1486          | 0.5714   |
| 0.9372        | 30.0  | 6570  | 1.1410          | 0.5714   |
| 0.9028        | 31.0  | 6789  | 1.1331          | 0.5714   |
| 0.9732        | 32.0  | 7008  | 1.1254          | 0.5714   |
| 0.9146        | 33.0  | 7227  | 1.1186          | 0.5714   |
| 0.8712        | 34.0  | 7446  | 1.1126          | 0.5714   |
| 0.8981        | 35.0  | 7665  | 1.1068          | 0.5714   |
| 0.8626        | 36.0  | 7884  | 1.1011          | 0.5714   |
| 0.884         | 37.0  | 8103  | 1.0956          | 0.5714   |
| 0.9119        | 38.0  | 8322  | 1.0906          | 0.5714   |
| 0.8378        | 39.0  | 8541  | 1.0862          | 0.5714   |
| 0.8095        | 40.0  | 8760  | 1.0823          | 0.5952   |
| 0.9067        | 41.0  | 8979  | 1.0785          | 0.5952   |
| 0.874         | 42.0  | 9198  | 1.0755          | 0.5714   |
| 0.8784        | 43.0  | 9417  | 1.0728          | 0.5714   |
| 0.8408        | 44.0  | 9636  | 1.0704          | 0.5714   |
| 0.8315        | 45.0  | 9855  | 1.0684          | 0.5714   |
| 0.8598        | 46.0  | 10074 | 1.0667          | 0.5714   |
| 0.8452        | 47.0  | 10293 | 1.0654          | 0.5952   |
| 0.863         | 48.0  | 10512 | 1.0647          | 0.5952   |
| 0.8292        | 49.0  | 10731 | 1.0643          | 0.5952   |
| 0.7869        | 50.0  | 10950 | 1.0643          | 0.5952   |


### 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