generated_from_trainer

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vit-base_rvl-cdip-small_rvl_cdip-NK1000_kd_MSE_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
4.1455 1.0 1000 3.9403 0.1898 0.8859 5.5812 0.1898 0.1327 0.0656 0.6790
3.4397 2.0 2000 3.3498 0.346 0.8036 4.5993 0.346 0.2909 0.1060 0.4698
2.983 3.0 3000 3.1592 0.384 0.8070 3.7537 0.384 0.3451 0.1795 0.4280
2.7406 4.0 4000 2.7614 0.4447 0.6956 3.6359 0.4447 0.4361 0.0840 0.3307
2.5937 5.0 5000 2.7073 0.4462 0.7000 3.5795 0.4462 0.4321 0.1292 0.3212
2.3878 6.0 6000 2.3935 0.5012 0.6651 3.1888 0.5012 0.4842 0.1429 0.2829
2.2284 7.0 7000 2.3189 0.5022 0.6378 3.0628 0.5022 0.5109 0.1027 0.2630
2.0759 8.0 8000 2.3408 0.4993 0.6550 3.3921 0.4993 0.4923 0.1398 0.2640
1.9764 9.0 9000 2.0531 0.5563 0.5946 2.9188 0.5563 0.5619 0.1202 0.2170
1.8232 10.0 10000 2.1083 0.5505 0.6295 3.0945 0.5505 0.5445 0.1794 0.2322
1.7049 11.0 11000 2.0447 0.5653 0.6142 2.9718 0.5653 0.5605 0.1684 0.2207
1.6182 12.0 12000 2.0684 0.5637 0.6462 3.0100 0.5637 0.5595 0.2095 0.2272
1.4886 13.0 13000 1.9374 0.5735 0.6132 2.9415 0.5735 0.5806 0.1874 0.2042
1.3538 14.0 14000 2.0147 0.5895 0.6174 3.0835 0.5895 0.5851 0.1966 0.2109
1.2304 15.0 15000 1.9766 0.5867 0.6203 3.1471 0.5867 0.5846 0.2229 0.2091
1.1124 16.0 16000 1.8998 0.6008 0.6044 2.9169 0.6008 0.5943 0.2144 0.1911
1.0197 17.0 17000 1.9309 0.5955 0.6123 3.1166 0.5955 0.5979 0.2299 0.1876
0.8763 18.0 18000 1.9741 0.5952 0.6316 3.2227 0.5952 0.5971 0.2439 0.1957
0.8042 19.0 19000 1.9944 0.592 0.6318 3.1537 0.592 0.5898 0.2439 0.2024
0.7059 20.0 20000 1.9259 0.6082 0.6124 3.0665 0.6082 0.6093 0.2344 0.1889
0.632 21.0 21000 1.9444 0.6095 0.6148 3.0133 0.6095 0.6111 0.2281 0.1917
0.5641 22.0 22000 1.9830 0.5968 0.6282 3.0999 0.5968 0.5984 0.2442 0.1913
0.5138 23.0 23000 2.0190 0.5962 0.6331 3.1937 0.5962 0.5966 0.2501 0.2033
0.457 24.0 24000 1.9340 0.6075 0.6151 2.9559 0.6075 0.6096 0.2333 0.1888
0.3999 25.0 25000 1.9742 0.6048 0.6285 3.0455 0.6048 0.6080 0.2461 0.1939
0.3629 26.0 26000 1.9308 0.6142 0.6027 3.1686 0.6142 0.6169 0.2244 0.1850
0.3132 27.0 27000 1.9468 0.6175 0.6076 3.0271 0.6175 0.6189 0.2374 0.1863
0.2818 28.0 28000 1.9392 0.6095 0.6152 3.0499 0.6095 0.6079 0.2422 0.1894
0.2584 29.0 29000 1.8976 0.6202 0.6040 2.9355 0.6202 0.6204 0.2340 0.1834
0.228 30.0 30000 1.9111 0.617 0.6020 3.0272 0.617 0.6192 0.2336 0.1780
0.2041 31.0 31000 1.8513 0.6272 0.5835 2.8808 0.6272 0.6293 0.2222 0.1733
0.1834 32.0 32000 1.8501 0.6262 0.5782 2.8280 0.6262 0.6275 0.2142 0.1702
0.1613 33.0 33000 1.8250 0.6292 0.5712 2.8863 0.6292 0.6338 0.2021 0.1691
0.1437 34.0 34000 1.8457 0.6228 0.5773 2.9046 0.6228 0.6232 0.2114 0.1717
0.1275 35.0 35000 1.8088 0.6315 0.5646 2.8124 0.6315 0.6328 0.2039 0.1638
0.1127 36.0 36000 1.8204 0.6335 0.5647 2.7943 0.6335 0.6373 0.1993 0.1661
0.1026 37.0 37000 1.8070 0.631 0.5641 2.7537 0.631 0.6326 0.2015 0.1634
0.0894 38.0 38000 1.8068 0.63 0.5606 2.7461 0.63 0.6317 0.1998 0.1634
0.0785 39.0 39000 1.7894 0.6312 0.5550 2.7333 0.6312 0.6351 0.1963 0.1599
0.0696 40.0 40000 1.7996 0.6288 0.5607 2.7489 0.6288 0.6334 0.1986 0.1645
0.0626 41.0 41000 1.7963 0.6328 0.5532 2.7232 0.6328 0.6349 0.1933 0.1632
0.055 42.0 42000 1.7959 0.6268 0.5556 2.6877 0.6268 0.6298 0.1957 0.1617
0.0475 43.0 43000 1.8018 0.632 0.5522 2.7232 0.632 0.6354 0.1934 0.1598
0.0419 44.0 44000 1.7930 0.6325 0.5507 2.6842 0.6325 0.6361 0.1906 0.1612
0.0367 45.0 45000 1.8064 0.6265 0.5577 2.6772 0.6265 0.6299 0.1994 0.1632
0.0328 46.0 46000 1.8044 0.6228 0.5524 2.6611 0.6228 0.6263 0.1971 0.1620
0.0289 47.0 47000 1.8101 0.6248 0.5544 2.6841 0.6248 0.6284 0.1943 0.1624
0.0265 48.0 48000 1.8088 0.6242 0.5531 2.6870 0.6242 0.6283 0.1943 0.1622
0.0238 49.0 49000 1.8107 0.6255 0.5533 2.7007 0.6255 0.6292 0.1923 0.1621
0.022 50.0 50000 1.8136 0.6262 0.5539 2.6914 0.6262 0.6298 0.1916 0.1624

Framework versions