generated_from_trainer

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vit-base_rvl-cdip-small_rvl_cdip-NK1000_hint_rand

This model is a fine-tuned version of google/vit-base-patch16-224-in21k 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
77.1398 1.0 1000 76.8452 0.1923 0.8714 4.4881 0.1923 0.1046 0.0814 0.6378
75.9619 2.0 2000 75.9373 0.3513 0.7709 3.0790 0.3513 0.3110 0.0596 0.4537
75.5047 3.0 3000 75.7291 0.4233 0.7112 3.0280 0.4233 0.3913 0.0610 0.3648
75.4727 4.0 4000 75.5639 0.4163 0.7147 3.0030 0.4163 0.3863 0.0669 0.3662
75.146 5.0 5000 75.4176 0.467 0.6695 2.8545 0.467 0.4530 0.0563 0.3180
74.8201 6.0 6000 74.8222 0.5275 0.6023 2.6409 0.5275 0.5201 0.0587 0.2448
74.4727 7.0 7000 74.6341 0.5403 0.5930 2.5700 0.5403 0.5312 0.0707 0.2393
74.1392 8.0 8000 74.6029 0.5615 0.5669 2.5716 0.5615 0.5496 0.0666 0.2142
74.165 9.0 9000 74.4072 0.5863 0.5479 2.5087 0.5863 0.5793 0.0689 0.1969
73.8821 10.0 10000 74.2595 0.5817 0.5517 2.4910 0.5817 0.5802 0.0733 0.1973
73.6199 11.0 11000 74.2044 0.61 0.5233 2.4183 0.61 0.6001 0.0853 0.1722
73.4772 12.0 12000 73.9341 0.593 0.5520 2.4592 0.593 0.5873 0.1244 0.1847
73.2445 13.0 13000 73.9870 0.614 0.5368 2.5577 0.614 0.6093 0.1303 0.1706
73.1468 14.0 14000 73.9027 0.6212 0.5368 2.6082 0.6212 0.6192 0.1340 0.1691
72.9154 15.0 15000 73.7745 0.6298 0.5353 2.5866 0.6298 0.6260 0.1564 0.1598
72.7416 16.0 16000 73.8946 0.6225 0.5528 2.5893 0.6225 0.6245 0.1810 0.1679
72.4708 17.0 17000 73.9445 0.62 0.5757 2.7436 0.62 0.6217 0.2044 0.1714
72.5169 18.0 18000 73.7757 0.6262 0.5741 2.6894 0.6262 0.6292 0.2139 0.1655
72.2021 19.0 19000 73.9482 0.6192 0.6063 2.8813 0.6192 0.6175 0.2534 0.1706
72.1296 20.0 20000 73.9725 0.6185 0.6135 2.9223 0.6185 0.6223 0.2495 0.1736
72.1903 21.0 21000 74.0277 0.6285 0.6091 2.8760 0.6285 0.6307 0.2588 0.1638
71.9868 22.0 22000 74.1811 0.6218 0.6317 3.0858 0.6218 0.6229 0.2792 0.1698
71.9677 23.0 23000 74.1227 0.6222 0.6442 3.0329 0.6222 0.6214 0.2872 0.1764
71.8254 24.0 24000 74.2927 0.6282 0.6412 3.1773 0.6282 0.6220 0.2928 0.1739
71.7948 25.0 25000 74.1580 0.626 0.6498 3.1230 0.626 0.6286 0.3007 0.1703
71.6915 26.0 26000 74.1776 0.6335 0.6367 3.1272 0.6335 0.6351 0.2937 0.1655
71.4526 27.0 27000 74.4076 0.6335 0.6519 3.3331 0.6335 0.6318 0.3023 0.1749
71.2967 28.0 28000 74.1954 0.6402 0.6361 3.1669 0.6402 0.6392 0.2995 0.1618
71.4139 29.0 29000 74.2737 0.6342 0.6454 3.0744 0.6342 0.6347 0.3070 0.1626
71.3204 30.0 30000 74.2779 0.652 0.6277 3.2286 0.652 0.6550 0.2956 0.1572
71.4168 31.0 31000 74.3630 0.6458 0.6386 3.2327 0.6458 0.6463 0.3032 0.1594
71.387 32.0 32000 74.4710 0.6522 0.6383 3.3193 0.6522 0.6526 0.3016 0.1610
71.2382 33.0 33000 74.4096 0.652 0.6275 3.3440 0.652 0.6522 0.2977 0.1584
71.1387 34.0 34000 74.2451 0.6512 0.6316 3.2834 0.6512 0.6525 0.3022 0.1555
71.0904 35.0 35000 74.2640 0.6525 0.6341 3.1942 0.6525 0.6518 0.3023 0.1521
70.9615 36.0 36000 74.1828 0.6565 0.6239 3.1805 0.6565 0.6568 0.3014 0.1516
71.0673 37.0 37000 74.3405 0.6498 0.6341 3.3365 0.6498 0.6518 0.3071 0.1556
71.0009 38.0 38000 74.2596 0.6595 0.6296 3.3359 0.6595 0.6622 0.2991 0.1512
70.8441 39.0 39000 74.2837 0.6593 0.6254 3.3852 0.6593 0.6609 0.3005 0.1537
70.8273 40.0 40000 74.3321 0.6567 0.6342 3.3111 0.6567 0.6589 0.3068 0.1544
70.8931 41.0 41000 74.3478 0.662 0.6253 3.3022 0.662 0.6604 0.3029 0.1474
70.8954 42.0 42000 74.2638 0.6613 0.6275 3.3811 0.6613 0.6612 0.3033 0.1499
70.7389 43.0 43000 74.2531 0.6633 0.6221 3.3627 0.6633 0.6650 0.2998 0.1489
70.7911 44.0 44000 74.3263 0.6587 0.6299 3.3918 0.6587 0.6588 0.3037 0.1496
70.8719 45.0 45000 74.2778 0.6627 0.6236 3.3826 0.6627 0.6641 0.3009 0.1480
70.7289 46.0 46000 74.2760 0.6625 0.6201 3.3467 0.6625 0.6635 0.3016 0.1469
70.8773 47.0 47000 74.2709 0.6643 0.6185 3.3370 0.6643 0.6660 0.2989 0.1476
70.6951 48.0 48000 74.2857 0.6643 0.6218 3.3545 0.6643 0.6648 0.2995 0.1477
70.8059 49.0 49000 74.3124 0.6623 0.6228 3.3592 0.6623 0.6634 0.3020 0.1470
70.6955 50.0 50000 74.2895 0.6627 0.6224 3.3689 0.6627 0.6637 0.3019 0.1471

Framework versions