generated_from_trainer

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dit-base-tiny_rvl_cdip-NK1000_hint

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
2.7601 1.0 1000 2.6212 0.5952 0.5281 2.4218 0.5952 0.5899 0.0529 0.1801
2.236 2.0 2000 2.1744 0.6847 0.4219 2.1650 0.6847 0.6827 0.0515 0.1135
1.9525 3.0 3000 1.9941 0.7147 0.3889 2.0860 0.7147 0.7163 0.0616 0.0961
1.729 4.0 4000 1.8880 0.7485 0.3585 2.1097 0.7485 0.7476 0.0649 0.0811
1.6172 5.0 5000 1.8693 0.7455 0.3598 2.0514 0.7455 0.7448 0.0733 0.0811
1.4385 6.0 6000 1.8350 0.747 0.3654 2.0917 0.747 0.7536 0.0828 0.0843
1.3098 7.0 7000 1.8317 0.7625 0.3515 2.0367 0.7625 0.7668 0.1039 0.0794
1.1854 8.0 8000 1.8872 0.7675 0.3589 2.1085 0.7675 0.7661 0.1294 0.0694
1.1066 9.0 9000 1.9843 0.7642 0.3707 2.1492 0.7642 0.7670 0.1457 0.0715
1.0518 10.0 10000 1.9993 0.77 0.3660 2.0694 0.7700 0.7702 0.1440 0.0695
0.9741 11.0 11000 2.2346 0.769 0.3870 2.0588 0.769 0.7704 0.1748 0.0735
0.938 12.0 12000 2.2626 0.767 0.3918 2.1412 0.767 0.7679 0.1774 0.0723
0.899 13.0 13000 2.3671 0.7698 0.3995 2.1064 0.7698 0.7725 0.1820 0.0708
0.8708 14.0 14000 2.3386 0.7768 0.3838 2.1948 0.7768 0.7777 0.1724 0.0683
0.8456 15.0 15000 2.3234 0.7827 0.3774 2.0937 0.7828 0.7830 0.1752 0.0638
0.8021 16.0 16000 2.5700 0.7685 0.4092 2.1458 0.7685 0.7684 0.1911 0.0717
0.7921 17.0 17000 2.5721 0.778 0.3934 2.1774 0.778 0.7756 0.1829 0.0708
0.7779 18.0 18000 2.7204 0.772 0.4089 2.1145 0.772 0.7707 0.1926 0.0758
0.7484 19.0 19000 2.7208 0.7752 0.4044 2.1020 0.7752 0.7760 0.1901 0.0746
0.7469 20.0 20000 2.5898 0.7927 0.3711 2.2076 0.7927 0.7909 0.1763 0.0648
0.7256 21.0 21000 2.5658 0.791 0.3727 2.0710 0.791 0.7920 0.1763 0.0650
0.7137 22.0 22000 2.6782 0.7847 0.3851 2.1469 0.7847 0.7854 0.1824 0.0703
0.6912 23.0 23000 2.5574 0.802 0.3539 2.1340 0.802 0.8009 0.1685 0.0615
0.6894 24.0 24000 2.6331 0.7913 0.3760 2.0913 0.7913 0.7944 0.1807 0.0674
0.6692 25.0 25000 2.6074 0.7955 0.3658 2.0837 0.7955 0.7975 0.1745 0.0645
0.6541 26.0 26000 2.6059 0.7945 0.3672 2.0798 0.7945 0.7936 0.1751 0.0616
0.6517 27.0 27000 2.7149 0.7965 0.3697 2.0842 0.7965 0.7987 0.1769 0.0663
0.6484 28.0 28000 2.5700 0.8047 0.3542 2.0142 0.8047 0.8058 0.1685 0.0597
0.6342 29.0 29000 2.6774 0.7987 0.3660 2.1231 0.7987 0.7972 0.1759 0.0622
0.6331 30.0 30000 2.6112 0.7973 0.3621 2.0740 0.7973 0.7981 0.1752 0.0621
0.6204 31.0 31000 2.6470 0.807 0.3521 2.0337 0.807 0.8056 0.1683 0.0638
0.612 32.0 32000 2.6265 0.8053 0.3549 2.0800 0.8053 0.8038 0.1678 0.0596
0.6049 33.0 33000 2.5749 0.8107 0.3428 2.0235 0.8108 0.8114 0.1662 0.0576
0.5984 34.0 34000 2.6667 0.804 0.3572 2.1015 0.804 0.8041 0.1726 0.0632
0.5961 35.0 35000 2.6481 0.8067 0.3521 2.0652 0.8067 0.8068 0.1673 0.0610
0.5958 36.0 36000 2.6831 0.8065 0.3523 2.0272 0.8065 0.8074 0.1681 0.0623
0.5836 37.0 37000 2.6573 0.8067 0.3533 2.0149 0.8067 0.8050 0.1699 0.0624
0.5855 38.0 38000 2.6730 0.8087 0.3510 2.0149 0.8087 0.8087 0.1693 0.0611
0.581 39.0 39000 2.6464 0.815 0.3390 2.0609 0.815 0.8148 0.1628 0.0588
0.5708 40.0 40000 2.7165 0.8077 0.3515 2.0489 0.8077 0.8078 0.1698 0.0644
0.5727 41.0 41000 2.6264 0.8135 0.3402 2.0070 0.8135 0.8130 0.1643 0.0601
0.5688 42.0 42000 2.6522 0.8077 0.3522 2.0064 0.8077 0.8076 0.1687 0.0615
0.5648 43.0 43000 2.5806 0.8193 0.3295 2.0211 0.8193 0.8188 0.1593 0.0585
0.5649 44.0 44000 2.6182 0.8125 0.3372 2.0292 0.8125 0.8122 0.1630 0.0598
0.562 45.0 45000 2.6274 0.8157 0.3366 2.0047 0.8157 0.8158 0.1610 0.0577
0.5592 46.0 46000 2.6069 0.8145 0.3370 2.0276 0.8145 0.8146 0.1632 0.0599
0.5582 47.0 47000 2.5935 0.8187 0.3331 2.0291 0.8187 0.8193 0.1594 0.0588
0.5565 48.0 48000 2.5780 0.8197 0.3302 2.0401 0.8197 0.8197 0.1580 0.0577
0.5571 49.0 49000 2.5781 0.8185 0.3294 2.0367 0.8185 0.8184 0.1590 0.0583
0.5568 50.0 50000 2.5793 0.8185 0.3293 2.0402 0.8185 0.8184 0.1589 0.0584

Framework versions