--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - image-classification - vision - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-letter-identification-v3 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.7615384615384615 --- # 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