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# vit-letter-identification-v3

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3440
- Accuracy: 0.7615

## 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: 2e-05
- train_batch_size: 80
- eval_batch_size: 80
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 250.0

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| No log        | 1.0   | 7    | 0.0154   | 3.9449          |
| 3.9333        | 2.0   | 14   | 0.0231   | 3.9367          |
| 3.8939        | 3.0   | 21   | 0.0308   | 3.9280          |
| 3.8939        | 4.0   | 28   | 0.0462   | 3.9167          |
| 3.8562        | 5.0   | 35   | 0.0692   | 3.9033          |
| 3.8008        | 6.0   | 42   | 0.0769   | 3.8874          |
| 3.8008        | 7.0   | 49   | 0.1077   | 3.8670          |
| 3.7555        | 8.0   | 56   | 0.1      | 3.8495          |
| 3.6917        | 9.0   | 63   | 0.1154   | 3.8305          |
| 3.6372        | 10.0  | 70   | 0.1385   | 3.8138          |
| 3.6372        | 11.0  | 77   | 0.1231   | 3.7966          |
| 3.5846        | 12.0  | 84   | 0.1538   | 3.7767          |
| 3.5047        | 13.0  | 91   | 0.2308   | 3.7516          |
| 3.5047        | 14.0  | 98   | 0.2385   | 3.7279          |
| 3.4547        | 15.0  | 105  | 0.2385   | 3.7031          |
| 3.3796        | 16.0  | 112  | 0.2692   | 3.6725          |
| 3.3796        | 17.0  | 119  | 0.2769   | 3.6462          |
| 3.3283        | 18.0  | 126  | 0.2923   | 3.6226          |
| 3.2728        | 19.0  | 133  | 0.2846   | 3.6022          |
| 3.2229        | 20.0  | 140  | 0.2769   | 3.5930          |
| 3.2229        | 21.0  | 147  | 0.3308   | 3.5748          |
| 3.1514        | 22.0  | 154  | 0.3385   | 3.5404          |
| 3.1179        | 23.0  | 161  | 0.3385   | 3.5146          |
| 3.1179        | 24.0  | 168  | 0.3462   | 3.4916          |
| 3.0559        | 25.0  | 175  | 0.3385   | 3.4733          |
| 3.0051        | 26.0  | 182  | 0.3615   | 3.4540          |
| 3.0051        | 27.0  | 189  | 0.3692   | 3.4499          |
| 2.9775        | 28.0  | 196  | 0.3769   | 3.4355          |
| 2.9277        | 29.0  | 203  | 0.3846   | 3.4166          |
| 2.9066        | 30.0  | 210  | 0.4      | 3.4007          |
| 2.9066        | 31.0  | 217  | 0.3692   | 3.3826          |
| 2.8464        | 32.0  | 224  | 0.4077   | 3.3698          |
| 2.8044        | 33.0  | 231  | 0.4077   | 3.3509          |
| 2.8044        | 34.0  | 238  | 0.3769   | 3.3243          |
| 2.7699        | 35.0  | 245  | 0.3923   | 3.3201          |
| 2.7251        | 36.0  | 252  | 0.4      | 3.3013          |
| 2.7251        | 37.0  | 259  | 0.4231   | 3.2936          |
| 2.6915        | 38.0  | 266  | 0.4538   | 3.2827          |
| 2.6527        | 39.0  | 273  | 0.4615   | 3.2627          |
| 2.6541        | 40.0  | 280  | 0.4615   | 3.2581          |
| 2.6541        | 41.0  | 287  | 0.4231   | 3.2342          |
| 2.5968        | 42.0  | 294  | 0.4385   | 3.2211          |
| 2.573         | 43.0  | 301  | 0.4077   | 3.2122          |
| 2.573         | 44.0  | 308  | 0.4615   | 3.2259          |
| 2.554         | 45.0  | 315  | 0.4308   | 3.2271          |
| 2.5222        | 46.0  | 322  | 0.4462   | 3.2208          |
| 2.5222        | 47.0  | 329  | 0.4462   | 3.2139          |
| 2.5085        | 48.0  | 336  | 0.4538   | 3.2040          |
| 2.4593        | 49.0  | 343  | 0.4923   | 3.2053          |
| 2.4585        | 50.0  | 350  | 0.4769   | 3.1822          |
| 2.4585        | 51.0  | 357  | 0.4692   | 3.1697          |
| 2.4228        | 52.0  | 364  | 0.4692   | 3.1589          |
| 2.3954        | 53.0  | 371  | 0.4769   | 3.1375          |
| 2.3954        | 54.0  | 378  | 0.4538   | 3.1092          |
| 2.3641        | 55.0  | 385  | 0.4769   | 3.0999          |
| 2.3651        | 56.0  | 392  | 0.4615   | 3.0860          |
| 2.3651        | 57.0  | 399  | 0.4615   | 3.0813          |
| 2.3182        | 58.0  | 406  | 0.4923   | 3.0692          |
| 2.3029        | 59.0  | 413  | 0.4846   | 3.0610          |
| 2.2988        | 60.0  | 420  | 0.4615   | 3.0627          |
| 2.2988        | 61.0  | 427  | 0.4692   | 3.0520          |
| 2.2865        | 62.0  | 434  | 0.4538   | 3.0395          |
| 2.2623        | 63.0  | 441  | 0.4615   | 3.0357          |
| 2.2623        | 64.0  | 448  | 0.4615   | 3.0333          |
| 2.2252        | 65.0  | 455  | 0.4769   | 3.0229          |
| 2.2339        | 66.0  | 462  | 0.4769   | 3.0203          |
| 2.2339        | 67.0  | 469  | 0.4923   | 3.0076          |
| 2.2017        | 68.0  | 476  | 0.4846   | 2.9876          |
| 2.1972        | 69.0  | 483  | 0.4923   | 2.9716          |
| 2.1964        | 70.0  | 490  | 0.5      | 2.9632          |
| 2.1964        | 71.0  | 497  | 0.4923   | 2.9597          |
| 2.1775        | 72.0  | 504  | 0.5      | 2.9581          |
| 2.1619        | 73.0  | 511  | 0.5077   | 2.9516          |
| 2.1619        | 74.0  | 518  | 0.5154   | 2.9356          |
| 2.1633        | 75.0  | 525  | 0.5077   | 2.9286          |
| 2.1207        | 76.0  | 532  | 0.5154   | 2.9266          |
| 2.1207        | 77.0  | 539  | 0.5231   | 2.9205          |
| 2.1353        | 78.0  | 546  | 0.5154   | 2.9131          |
| 2.1075        | 79.0  | 553  | 0.5231   | 2.9075          |
| 2.1025        | 80.0  | 560  | 0.5231   | 2.9073          |
| 2.1025        | 81.0  | 567  | 0.5154   | 2.9174          |
| 2.1031        | 82.0  | 574  | 0.5308   | 2.9131          |
| 2.0932        | 83.0  | 581  | 0.5308   | 2.9092          |
| 2.0932        | 84.0  | 588  | 0.5308   | 2.8978          |
| 2.0861        | 85.0  | 595  | 0.5308   | 2.8871          |
| 2.0478        | 86.0  | 602  | 0.5385   | 2.8829          |
| 2.0478        | 87.0  | 609  | 0.5462   | 2.8804          |
| 2.0815        | 88.0  | 616  | 0.5462   | 2.8725          |
| 2.0756        | 89.0  | 623  | 0.5462   | 2.8694          |
| 2.065         | 90.0  | 630  | 0.5462   | 2.8665          |
| 2.065         | 91.0  | 637  | 0.5462   | 2.8615          |
| 2.0572        | 92.0  | 644  | 0.5462   | 2.8599          |
| 2.0358        | 93.0  | 651  | 0.5462   | 2.8620          |
| 2.0358        | 94.0  | 658  | 0.5462   | 2.8629          |
| 2.0663        | 95.0  | 665  | 0.5538   | 2.8625          |
| 2.0353        | 96.0  | 672  | 0.5538   | 2.8628          |
| 2.0353        | 97.0  | 679  | 0.5538   | 2.8629          |
| 2.0506        | 98.0  | 686  | 0.5538   | 2.8622          |
| 2.0494        | 99.0  | 693  | 0.5538   | 2.8622          |
| 2.0566        | 100.0 | 700  | 0.5538   | 2.8622          |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.4.0
- Tokenizers 0.15.0

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