generated_from_trainer

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dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5

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 1.1672 0.6082 0.5198 2.4714 0.6082 0.6021 0.0681 0.1731
No log 2.0 334 0.8928 0.7177 0.4028 2.0687 0.7178 0.7161 0.0715 0.0997
1.1617 3.0 501 0.7584 0.7602 0.3454 1.8041 0.7602 0.7637 0.0762 0.0747
1.1617 4.0 668 0.7048 0.768 0.3262 1.6695 0.768 0.7687 0.0550 0.0687
1.1617 5.0 835 0.6921 0.7745 0.3214 1.6428 0.7745 0.7683 0.0477 0.0738
0.4396 6.0 1002 0.6616 0.789 0.3105 1.5655 0.7890 0.7897 0.0586 0.0714
0.4396 7.0 1169 0.6363 0.794 0.3001 1.5694 0.7940 0.7970 0.0549 0.0674
0.4396 8.0 1336 0.6753 0.7792 0.3259 1.4975 0.7792 0.7804 0.0647 0.0777
0.2291 9.0 1503 0.6247 0.8025 0.2979 1.4968 0.8025 0.8037 0.0705 0.0669
0.2291 10.0 1670 0.6347 0.799 0.3032 1.4834 0.799 0.8011 0.0720 0.0743
0.2291 11.0 1837 0.6328 0.7975 0.3045 1.4998 0.7975 0.8031 0.0773 0.0659
0.1575 12.0 2004 0.6442 0.7965 0.3097 1.4447 0.7965 0.7979 0.0714 0.0824
0.1575 13.0 2171 0.6354 0.8013 0.3043 1.4874 0.8013 0.8035 0.0712 0.0741
0.1575 14.0 2338 0.6443 0.799 0.3091 1.5848 0.799 0.8022 0.0791 0.0859
0.1285 15.0 2505 0.6357 0.8017 0.3042 1.5670 0.8017 0.8002 0.0799 0.0685
0.1285 16.0 2672 0.6166 0.807 0.2965 1.4806 0.807 0.8056 0.0720 0.0745
0.1285 17.0 2839 0.6433 0.7993 0.3159 1.5024 0.7993 0.8023 0.0805 0.0857
0.1121 18.0 3006 0.6102 0.8147 0.2960 1.4550 0.8148 0.8144 0.0775 0.0698
0.1121 19.0 3173 0.6616 0.7995 0.3146 1.6009 0.7995 0.7962 0.0892 0.0883
0.1121 20.0 3340 0.6163 0.8037 0.3029 1.4525 0.8037 0.8059 0.0920 0.0771
0.1012 21.0 3507 0.6186 0.8093 0.3017 1.5539 0.8093 0.8111 0.0920 0.0712
0.1012 22.0 3674 0.5982 0.8137 0.2930 1.4533 0.8137 0.8140 0.0815 0.0668
0.1012 23.0 3841 0.5928 0.822 0.2864 1.4312 0.822 0.8218 0.0723 0.0818
0.0888 24.0 4008 0.5931 0.8135 0.2900 1.4129 0.8135 0.8143 0.0894 0.0706
0.0888 25.0 4175 0.5807 0.8183 0.2849 1.4241 0.8183 0.8203 0.0903 0.0683
0.0888 26.0 4342 0.5859 0.8193 0.2869 1.4385 0.8193 0.8194 0.0879 0.0698
0.0828 27.0 4509 0.5957 0.8147 0.2941 1.4132 0.8148 0.8151 0.0847 0.0732
0.0828 28.0 4676 0.5791 0.818 0.2852 1.4231 0.818 0.8185 0.0896 0.0612
0.0828 29.0 4843 0.5888 0.8137 0.2895 1.3998 0.8137 0.8148 0.0925 0.0740
0.0776 30.0 5010 0.5633 0.8225 0.2798 1.3391 0.8225 0.8234 0.0878 0.0760
0.0776 31.0 5177 0.5635 0.8247 0.2785 1.3193 0.8247 0.8256 0.0900 0.0587
0.0776 32.0 5344 0.5580 0.8223 0.2784 1.2970 0.8223 0.8241 0.0905 0.0704
0.0727 33.0 5511 0.5502 0.826 0.2724 1.2733 0.826 0.8268 0.0865 0.0619
0.0727 34.0 5678 0.5448 0.8293 0.2720 1.2237 0.8293 0.8303 0.0820 0.0639
0.0727 35.0 5845 0.5480 0.8257 0.2729 1.2867 0.8257 0.8271 0.0928 0.0586
0.0696 36.0 6012 0.5437 0.8293 0.2703 1.2427 0.8293 0.8298 0.0871 0.0630
0.0696 37.0 6179 0.5460 0.8253 0.2712 1.2629 0.8253 0.8262 0.0912 0.0598
0.0696 38.0 6346 0.5425 0.8295 0.2703 1.2440 0.8295 0.8303 0.0899 0.0611
0.0677 39.0 6513 0.5421 0.8307 0.2690 1.2453 0.8308 0.8319 0.0835 0.0665
0.0677 40.0 6680 0.5406 0.8287 0.2689 1.2465 0.8287 0.8296 0.0895 0.0612
0.0677 41.0 6847 0.5423 0.8277 0.2696 1.2735 0.8277 0.8284 0.0893 0.0604
0.0663 42.0 7014 0.5406 0.8297 0.2676 1.2403 0.8297 0.8306 0.0894 0.0657
0.0663 43.0 7181 0.5410 0.8313 0.2686 1.2359 0.8313 0.8323 0.0895 0.0635
0.0663 44.0 7348 0.5416 0.8287 0.2685 1.2308 0.8287 0.8295 0.0883 0.0647
0.0652 45.0 7515 0.5431 0.8275 0.2697 1.2374 0.8275 0.8282 0.0932 0.0648
0.0652 46.0 7682 0.5433 0.8295 0.2693 1.2347 0.8295 0.8303 0.0891 0.0681
0.0652 47.0 7849 0.5441 0.8277 0.2696 1.2433 0.8277 0.8286 0.0882 0.0681
0.0651 48.0 8016 0.5439 0.8293 0.2695 1.2358 0.8293 0.8301 0.0888 0.0692
0.0651 49.0 8183 0.5445 0.8287 0.2696 1.2499 0.8287 0.8296 0.0882 0.0695
0.0651 50.0 8350 0.5443 0.8293 0.2695 1.2500 0.8293 0.8301 0.0874 0.0693

Framework versions