generated_from_trainer

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vit-base_rvl-cdip-small_rvl_cdip-NK1000_kd_MSE

This model is a fine-tuned version of WinKawaks/vit-small-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 1.0 167 2.0205 0.6145 0.5068 2.2570 0.6145 0.6027 0.0547 0.1646
No log 2.0 334 1.3347 0.7055 0.3976 1.8932 0.7055 0.7001 0.0615 0.1010
2.0481 3.0 501 0.9336 0.764 0.3401 1.6693 0.764 0.7665 0.0772 0.0686
2.0481 4.0 668 0.7982 0.7895 0.3047 1.5439 0.7895 0.7930 0.0670 0.0569
2.0481 5.0 835 0.7154 0.7973 0.3037 1.5600 0.7973 0.7969 0.0836 0.0571
0.5656 6.0 1002 0.6158 0.8113 0.2903 1.4591 0.8113 0.8139 0.0921 0.0493
0.5656 7.0 1169 0.5531 0.8207 0.2707 1.4410 0.8207 0.8236 0.0832 0.0431
0.5656 8.0 1336 0.5706 0.815 0.2826 1.4722 0.815 0.8208 0.0881 0.0465
0.2988 9.0 1503 0.4654 0.8355 0.2488 1.3791 0.8355 0.8368 0.0745 0.0382
0.2988 10.0 1670 0.4695 0.8315 0.2579 1.3701 0.8315 0.8333 0.0813 0.0403
0.2988 11.0 1837 0.4358 0.8405 0.2424 1.3500 0.8405 0.8424 0.0725 0.0361
0.1829 12.0 2004 0.4333 0.8425 0.2402 1.3740 0.8425 0.8446 0.0662 0.0362
0.1829 13.0 2171 0.4239 0.8462 0.2326 1.3541 0.8462 0.8477 0.0648 0.0335
0.1829 14.0 2338 0.3902 0.8488 0.2263 1.2996 0.8488 0.8512 0.0642 0.0318
0.1215 15.0 2505 0.3740 0.8522 0.2194 1.3374 0.8522 0.8543 0.0595 0.0313
0.1215 16.0 2672 0.3735 0.8548 0.2189 1.3420 0.8547 0.8553 0.0525 0.0320
0.1215 17.0 2839 0.3700 0.8538 0.2161 1.3217 0.8537 0.8561 0.0521 0.0304
0.082 18.0 3006 0.3574 0.8548 0.2164 1.3245 0.8547 0.8561 0.0583 0.0301
0.082 19.0 3173 0.3669 0.8555 0.2140 1.3197 0.8555 0.8572 0.0538 0.0304
0.082 20.0 3340 0.3561 0.8548 0.2125 1.3367 0.8547 0.8560 0.0540 0.0296
0.0535 21.0 3507 0.3495 0.854 0.2116 1.3422 0.854 0.8558 0.0556 0.0294
0.0535 22.0 3674 0.3412 0.8602 0.2092 1.2970 0.8602 0.8621 0.0527 0.0293
0.0535 23.0 3841 0.3445 0.8595 0.2086 1.2979 0.8595 0.8613 0.0500 0.0286
0.0309 24.0 4008 0.3456 0.8585 0.2105 1.3220 0.8585 0.8601 0.0507 0.0292
0.0309 25.0 4175 0.3451 0.862 0.2091 1.3080 0.8620 0.8640 0.0465 0.0290
0.0309 26.0 4342 0.3484 0.8578 0.2090 1.3165 0.8578 0.8596 0.0527 0.0290
0.019 27.0 4509 0.3452 0.8612 0.2072 1.3133 0.8612 0.8634 0.0494 0.0288
0.019 28.0 4676 0.3451 0.8598 0.2089 1.3197 0.8598 0.8619 0.0515 0.0295
0.019 29.0 4843 0.3445 0.8618 0.2072 1.3057 0.8618 0.8633 0.0496 0.0294
0.0137 30.0 5010 0.3452 0.8592 0.2078 1.3108 0.8592 0.8609 0.0499 0.0292
0.0137 31.0 5177 0.3439 0.8615 0.2074 1.2960 0.8615 0.8631 0.0495 0.0286
0.0137 32.0 5344 0.3475 0.8618 0.2080 1.3146 0.8618 0.8638 0.0468 0.0288
0.01 33.0 5511 0.3468 0.8605 0.2080 1.3095 0.8605 0.8624 0.0470 0.0291
0.01 34.0 5678 0.3454 0.8638 0.2060 1.3094 0.8638 0.8653 0.0465 0.0285
0.01 35.0 5845 0.3463 0.8612 0.2067 1.3145 0.8612 0.8632 0.0479 0.0287
0.0071 36.0 6012 0.3466 0.8615 0.2070 1.3189 0.8615 0.8634 0.0449 0.0289
0.0071 37.0 6179 0.3457 0.8635 0.2065 1.3085 0.8635 0.8653 0.0487 0.0287
0.0071 38.0 6346 0.3471 0.8618 0.2066 1.3132 0.8618 0.8637 0.0488 0.0286
0.0047 39.0 6513 0.3481 0.8615 0.2067 1.3116 0.8615 0.8632 0.0485 0.0288
0.0047 40.0 6680 0.3482 0.8618 0.2074 1.3149 0.8618 0.8638 0.0512 0.0290
0.0047 41.0 6847 0.3488 0.862 0.2072 1.3162 0.8620 0.8640 0.0467 0.0287
0.0029 42.0 7014 0.3485 0.862 0.2069 1.3136 0.8620 0.8640 0.0466 0.0288
0.0029 43.0 7181 0.3492 0.8612 0.2072 1.3151 0.8612 0.8633 0.0470 0.0288
0.0029 44.0 7348 0.3492 0.8615 0.2070 1.3117 0.8615 0.8634 0.0459 0.0289
0.0019 45.0 7515 0.3502 0.8612 0.2073 1.3153 0.8612 0.8632 0.0460 0.0289
0.0019 46.0 7682 0.3500 0.8615 0.2072 1.3136 0.8615 0.8634 0.0474 0.0290
0.0019 47.0 7849 0.3505 0.862 0.2072 1.3153 0.8620 0.8640 0.0457 0.0289
0.0014 48.0 8016 0.3507 0.861 0.2072 1.3113 0.861 0.8630 0.0475 0.0290
0.0014 49.0 8183 0.3508 0.861 0.2071 1.3111 0.861 0.8630 0.0474 0.0290
0.0014 50.0 8350 0.3508 0.861 0.2072 1.3138 0.861 0.8630 0.0470 0.0290

Framework versions