generated_from_trainer

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vit-base_rvl-cdip-small_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5_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
4.0737 1.0 1000 3.8362 0.1948 0.8785 4.2153 0.1948 0.1339 0.0877 0.6770
3.2534 2.0 2000 3.0129 0.3772 0.7858 3.2453 0.3773 0.3171 0.1532 0.4263
2.802 3.0 3000 2.6716 0.4472 0.7236 3.0740 0.4472 0.4150 0.1479 0.3537
2.5803 4.0 4000 2.4882 0.4775 0.6581 2.9147 0.4775 0.4658 0.0774 0.2981
2.4445 5.0 5000 2.3888 0.4753 0.6741 2.8504 0.4753 0.4562 0.1529 0.2912
2.2315 6.0 6000 2.2439 0.5132 0.6404 2.7536 0.5132 0.5007 0.1179 0.2659
2.0849 7.0 7000 2.2140 0.5125 0.6441 2.8160 0.5125 0.5119 0.1522 0.2634
1.922 8.0 8000 2.0941 0.5298 0.6202 2.7843 0.5298 0.5267 0.1511 0.2384
1.8235 9.0 9000 2.0477 0.5453 0.5974 2.6273 0.5453 0.5505 0.1131 0.2252
1.6864 10.0 10000 2.0429 0.5575 0.6181 2.7309 0.5575 0.5391 0.1828 0.2172
1.5498 11.0 11000 2.0547 0.5597 0.6272 2.6772 0.5597 0.5444 0.1931 0.2237
1.4502 12.0 12000 2.0086 0.5707 0.6175 2.7419 0.5707 0.5592 0.1935 0.2153
1.3331 13.0 13000 2.0399 0.566 0.6150 2.8462 0.566 0.5660 0.1786 0.2175
1.19 14.0 14000 2.0207 0.5805 0.6209 2.7599 0.5805 0.5793 0.2133 0.2081
1.0615 15.0 15000 2.0089 0.5793 0.6224 2.9014 0.5793 0.5787 0.2157 0.2077
0.9517 16.0 16000 2.0644 0.5765 0.6300 2.9717 0.5765 0.5796 0.2264 0.2047
0.8343 17.0 17000 2.0990 0.5745 0.6473 3.0444 0.5745 0.5712 0.2502 0.2050
0.7191 18.0 18000 2.1704 0.5763 0.6527 3.0809 0.5763 0.5721 0.2539 0.2125
0.638 19.0 19000 2.1930 0.5807 0.6572 3.1618 0.5807 0.5792 0.2620 0.2120
0.5528 20.0 20000 2.1731 0.5885 0.6542 3.1412 0.5885 0.5898 0.2627 0.2067
0.4957 21.0 21000 2.2492 0.5763 0.6708 3.1701 0.5763 0.5760 0.2700 0.2232
0.4096 22.0 22000 2.3164 0.5707 0.6837 3.3874 0.5707 0.5706 0.2761 0.2260
0.3915 23.0 23000 2.3277 0.58 0.6813 3.4230 0.58 0.5814 0.2857 0.2165
0.3412 24.0 24000 2.2947 0.5813 0.6779 3.3373 0.5813 0.5854 0.2870 0.2076
0.3171 25.0 25000 2.2743 0.586 0.6720 3.3310 0.586 0.5848 0.2767 0.2104
0.2734 26.0 26000 2.2762 0.593 0.6696 3.3439 0.593 0.5977 0.2819 0.2044
0.2535 27.0 27000 2.2205 0.5845 0.6605 3.2712 0.5845 0.5864 0.2684 0.2060
0.229 28.0 28000 2.2961 0.5845 0.6821 3.2738 0.5845 0.5902 0.2896 0.2106
0.2202 29.0 29000 2.2698 0.5845 0.6752 3.2127 0.5845 0.5845 0.2835 0.2128
0.1922 30.0 30000 2.2511 0.5787 0.6731 3.3305 0.5787 0.5819 0.2770 0.2065
0.1857 31.0 31000 2.1847 0.5863 0.6598 3.2211 0.5863 0.5889 0.2692 0.2036
0.1678 32.0 32000 2.1752 0.5913 0.6551 3.0691 0.5913 0.5926 0.2680 0.2010
0.1587 33.0 33000 2.1107 0.5972 0.6392 3.0412 0.5972 0.6002 0.2495 0.2002
0.1432 34.0 34000 2.2079 0.5893 0.6593 3.1988 0.5893 0.5917 0.2640 0.2092
0.1291 35.0 35000 2.0788 0.592 0.6388 2.9891 0.592 0.5942 0.2587 0.1887
0.1245 36.0 36000 2.0521 0.601 0.6297 2.9432 0.601 0.6018 0.2464 0.1923
0.1169 37.0 37000 2.0669 0.5935 0.6294 3.0364 0.5935 0.5932 0.2425 0.1878
0.1068 38.0 38000 2.0197 0.606 0.6183 2.9448 0.606 0.6054 0.2375 0.1897
0.1031 39.0 39000 2.0022 0.6032 0.6163 2.8697 0.6032 0.6051 0.2429 0.1880
0.0961 40.0 40000 1.9965 0.6058 0.6147 2.9143 0.6058 0.6091 0.2393 0.1859
0.093 41.0 41000 1.9735 0.6082 0.6080 2.9346 0.6082 0.6100 0.2331 0.1834
0.0859 42.0 42000 1.9383 0.6092 0.6011 2.8692 0.6092 0.6105 0.2281 0.1792
0.0817 43.0 43000 1.9413 0.6115 0.5999 2.8908 0.6115 0.6124 0.2264 0.1802
0.0779 44.0 44000 1.9271 0.6112 0.6001 2.8695 0.6112 0.6127 0.2274 0.1791
0.0745 45.0 45000 1.9259 0.6135 0.5983 2.8467 0.6135 0.6154 0.2206 0.1786
0.0723 46.0 46000 1.9214 0.612 0.5964 2.8289 0.612 0.6136 0.2233 0.1778
0.0673 47.0 47000 1.9117 0.6128 0.5923 2.8226 0.6128 0.6148 0.2197 0.1768
0.0649 48.0 48000 1.9111 0.6152 0.5911 2.8575 0.6152 0.6164 0.2166 0.1757
0.0651 49.0 49000 1.9106 0.6148 0.5914 2.8675 0.6148 0.6169 0.2164 0.1761
0.0629 50.0 50000 1.9130 0.6115 0.5924 2.8591 0.6115 0.6137 0.2201 0.1762

Framework versions