generated_from_trainer

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cdip-tiny_rvl_cdip-NK1000_kd_test

This model is a fine-tuned version of WinKawaks/vit-tiny-patch16-224 on the None dataset. It achieves the following results on the evaluation set:

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:

Training results

Training Loss Epoch Step Validation Loss Accuracy Brier Loss Nll F1 Micro F1 Macro Ece Aurc
No log 1.0 125 1.3371 0.5423 0.5838 2.6217 0.5423 0.5341 0.0583 0.2219
No log 2.0 250 0.9983 0.646 0.4725 2.3143 0.646 0.6407 0.0490 0.1432
No log 3.0 375 0.8094 0.7085 0.3977 2.2571 0.7085 0.7034 0.0500 0.1017
1.2477 4.0 500 0.7633 0.7215 0.3806 2.2013 0.7215 0.7275 0.0447 0.0926
1.2477 5.0 625 0.7295 0.7505 0.3565 2.1741 0.7505 0.7417 0.0631 0.0775
1.2477 6.0 750 0.6706 0.7692 0.3321 2.1869 0.7692 0.7669 0.0614 0.0690
1.2477 7.0 875 0.6933 0.767 0.3344 2.1685 0.767 0.7650 0.0811 0.0676
0.3434 8.0 1000 0.6640 0.7778 0.3251 2.1666 0.7778 0.7795 0.0681 0.0650
0.3434 9.0 1125 0.6874 0.774 0.3328 2.1401 0.774 0.7739 0.0863 0.0660
0.3434 10.0 1250 0.6639 0.7795 0.3194 2.1335 0.7795 0.7800 0.0800 0.0618
0.3434 11.0 1375 0.6827 0.7728 0.3332 2.1140 0.7728 0.7771 0.0891 0.0650
0.1507 12.0 1500 0.6197 0.786 0.3106 2.1052 0.786 0.7873 0.0716 0.0586
0.1507 13.0 1625 0.6264 0.7823 0.3133 2.1077 0.7823 0.7834 0.0784 0.0595
0.1507 14.0 1750 0.5822 0.796 0.2964 2.0567 0.796 0.7983 0.0676 0.0549
0.1507 15.0 1875 0.5900 0.7923 0.3016 2.0704 0.7923 0.7936 0.0724 0.0541
0.107 16.0 2000 0.6044 0.7855 0.3099 2.0625 0.7855 0.7901 0.0730 0.0617
0.107 17.0 2125 0.5692 0.7973 0.2930 2.0627 0.7973 0.7990 0.0676 0.0528
0.107 18.0 2250 0.5836 0.7907 0.2984 2.0575 0.7907 0.7922 0.0749 0.0554
0.107 19.0 2375 0.5469 0.806 0.2835 2.0754 0.806 0.8060 0.0576 0.0498
0.0879 20.0 2500 0.5427 0.804 0.2892 2.0655 0.804 0.8089 0.0593 0.0528
0.0879 21.0 2625 0.5305 0.806 0.2777 2.0213 0.806 0.8070 0.0604 0.0495
0.0879 22.0 2750 0.5146 0.8113 0.2741 2.0127 0.8113 0.8121 0.0534 0.0480
0.0879 23.0 2875 0.5196 0.8107 0.2750 2.0261 0.8108 0.8117 0.0541 0.0489
0.0755 24.0 3000 0.5169 0.8123 0.2743 1.9561 0.8123 0.8127 0.0595 0.0478
0.0755 25.0 3125 0.5129 0.8073 0.2777 2.0020 0.8073 0.8089 0.0552 0.0491
0.0755 26.0 3250 0.4898 0.8177 0.2649 1.9710 0.8178 0.8177 0.0474 0.0451
0.0755 27.0 3375 0.4966 0.8155 0.2682 2.0075 0.8155 0.8163 0.0514 0.0458
0.0652 28.0 3500 0.4883 0.813 0.2690 1.9655 0.813 0.8141 0.0557 0.0465
0.0652 29.0 3625 0.4860 0.8185 0.2659 1.9593 0.8185 0.8194 0.0481 0.0456
0.0652 30.0 3750 0.4760 0.818 0.2600 1.9517 0.818 0.8194 0.0505 0.0441
0.0652 31.0 3875 0.4755 0.8195 0.2611 1.9593 0.8195 0.8196 0.0507 0.0440
0.0568 32.0 4000 0.4763 0.8155 0.2628 1.9508 0.8155 0.8161 0.0484 0.0451
0.0568 33.0 4125 0.4675 0.8225 0.2574 1.9474 0.8225 0.8238 0.0477 0.0433
0.0568 34.0 4250 0.4664 0.8207 0.2579 1.9478 0.8207 0.8220 0.0498 0.0431
0.0568 35.0 4375 0.4635 0.8213 0.2567 1.9233 0.8213 0.8219 0.0481 0.0427
0.0514 36.0 4500 0.4584 0.8245 0.2551 1.9196 0.8245 0.8260 0.0461 0.0424
0.0514 37.0 4625 0.4627 0.825 0.2557 1.9274 0.825 0.8256 0.0454 0.0424
0.0514 38.0 4750 0.4603 0.8213 0.2552 1.9319 0.8213 0.8221 0.0478 0.0425
0.0514 39.0 4875 0.4610 0.8245 0.2560 1.9337 0.8245 0.8252 0.0476 0.0424
0.0483 40.0 5000 0.4603 0.825 0.2559 1.9319 0.825 0.8262 0.0460 0.0421
0.0483 41.0 5125 0.4589 0.8253 0.2545 1.9317 0.8253 0.8260 0.0459 0.0421
0.0483 42.0 5250 0.4586 0.8245 0.2552 1.9192 0.8245 0.8260 0.0524 0.0420
0.0483 43.0 5375 0.4581 0.825 0.2552 1.9179 0.825 0.8263 0.0477 0.0421
0.0465 44.0 5500 0.4573 0.8245 0.2543 1.9187 0.8245 0.8257 0.0457 0.0417
0.0465 45.0 5625 0.4589 0.8225 0.2554 1.9184 0.8225 0.8235 0.0549 0.0421
0.0465 46.0 5750 0.4582 0.823 0.2547 1.9128 0.823 0.8242 0.0512 0.0420
0.0465 47.0 5875 0.4587 0.823 0.2551 1.9135 0.823 0.8241 0.0484 0.0420
0.0458 48.0 6000 0.4585 0.8235 0.2550 1.9127 0.8235 0.8246 0.0479 0.0420
0.0458 49.0 6125 0.4589 0.8227 0.2553 1.9117 0.8227 0.8238 0.0490 0.0421
0.0458 50.0 6250 0.4588 0.8227 0.2552 1.9117 0.8227 0.8239 0.0507 0.0420

Framework versions