generated_from_trainer

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

dit-base-finetuned-rvlcdip-small_rvl_cdip-NK1000_simkd

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
0.1387 1.0 1000 0.1293 0.4412 0.8613 3.7912 0.4412 0.3863 0.3246 0.3321
0.1154 2.0 2000 0.1132 0.668 0.7441 2.1523 0.668 0.6478 0.4699 0.1467
0.1074 3.0 3000 0.1075 0.73 0.7150 1.9686 0.7300 0.7323 0.5177 0.1104
0.1036 4.0 4000 0.1068 0.7258 0.6943 2.0361 0.7258 0.7299 0.4903 0.1318
0.0996 5.0 5000 0.1047 0.742 0.6897 2.1166 0.7420 0.7456 0.5061 0.1124
0.0967 6.0 6000 0.0992 0.78 0.6307 1.8492 0.78 0.7875 0.5018 0.0924
0.0923 7.0 7000 0.0985 0.774 0.6055 2.0449 0.774 0.7800 0.4698 0.1056
0.0893 8.0 8000 0.0982 0.7817 0.6012 1.9476 0.7817 0.7814 0.4696 0.1243
0.0871 9.0 9000 0.0954 0.8043 0.5826 1.7573 0.8043 0.8065 0.4925 0.0811
0.0857 10.0 10000 0.0969 0.784 0.5672 2.0878 0.7840 0.7846 0.4417 0.1076
0.083 11.0 11000 0.0934 0.8067 0.5658 1.6628 0.8067 0.8062 0.4809 0.0788
0.0819 12.0 12000 0.0930 0.8027 0.5499 1.6718 0.8027 0.8035 0.4592 0.0851
0.081 13.0 13000 0.0937 0.7957 0.5579 1.7282 0.7957 0.7956 0.4544 0.0948
0.079 14.0 14000 0.0939 0.794 0.5463 1.9406 0.7940 0.7974 0.4344 0.1142
0.0779 15.0 15000 0.0912 0.81 0.5206 1.6776 0.81 0.8141 0.4371 0.0947
0.0767 16.0 16000 0.0905 0.813 0.5165 1.6744 0.813 0.8140 0.4383 0.0864
0.0766 17.0 17000 0.0911 0.8113 0.5239 1.7109 0.8113 0.8104 0.4428 0.0902
0.0762 18.0 18000 0.0914 0.8093 0.5153 1.6778 0.8093 0.8098 0.4290 0.0998
0.0759 19.0 19000 0.0904 0.8163 0.5076 1.6946 0.8163 0.8178 0.4333 0.0939
0.075 20.0 20000 0.0897 0.8133 0.5062 1.5892 0.8133 0.8155 0.4300 0.0898
0.0743 21.0 21000 0.0895 0.8147 0.5058 1.5900 0.8148 0.8149 0.4315 0.0917
0.0745 22.0 22000 0.0898 0.8157 0.5014 1.5523 0.8157 0.8164 0.4287 0.0848
0.0737 23.0 23000 0.0901 0.8127 0.5038 1.6625 0.8128 0.8146 0.4219 0.0978
0.0735 24.0 24000 0.0907 0.8117 0.5082 1.6475 0.8117 0.8133 0.4231 0.1064
0.0732 25.0 25000 0.0901 0.8103 0.5041 1.6830 0.8103 0.8105 0.4187 0.1017
0.0727 26.0 26000 0.0899 0.8135 0.5015 1.6499 0.8135 0.8170 0.4197 0.1020
0.0722 27.0 27000 0.0880 0.8265 0.4931 1.4651 0.8265 0.8273 0.4330 0.0975
0.0718 28.0 28000 0.0876 0.8263 0.4917 1.4213 0.8263 0.8275 0.4354 0.0858
0.0725 29.0 29000 0.0891 0.8247 0.4930 1.5581 0.8247 0.8254 0.4288 0.0946
0.0717 30.0 30000 0.0879 0.8327 0.4913 1.4417 0.8327 0.8326 0.4403 0.0888
0.0715 31.0 31000 0.0872 0.8375 0.4866 1.3775 0.8375 0.8389 0.4435 0.0872
0.0715 32.0 32000 0.0884 0.8297 0.4915 1.5136 0.8297 0.8305 0.4331 0.0946
0.0717 33.0 33000 0.0877 0.8347 0.4851 1.4096 0.8347 0.8347 0.4375 0.0845
0.0716 34.0 34000 0.0880 0.8323 0.4866 1.4547 0.8323 0.8333 0.4323 0.0926
0.0713 35.0 35000 0.0873 0.8343 0.4833 1.3884 0.8343 0.8351 0.4375 0.0810
0.0713 36.0 36000 0.0873 0.8365 0.4843 1.4168 0.8365 0.8372 0.4381 0.0913
0.071 37.0 37000 0.0871 0.8393 0.4831 1.3524 0.8393 0.8399 0.4412 0.0882
0.0709 38.0 38000 0.0877 0.834 0.4862 1.4457 0.834 0.8353 0.4371 0.0929
0.071 39.0 39000 0.0870 0.836 0.4811 1.3954 0.836 0.8367 0.4360 0.0886
0.0708 40.0 40000 0.0867 0.8387 0.4800 1.3687 0.8387 0.8403 0.4390 0.0867
0.0706 41.0 41000 0.0866 0.8395 0.4802 1.3464 0.8395 0.8399 0.4412 0.0860
0.0708 42.0 42000 0.0868 0.8363 0.4796 1.3828 0.8363 0.8371 0.4345 0.0886
0.0709 43.0 43000 0.0866 0.838 0.4790 1.3503 0.838 0.8390 0.4382 0.0860
0.0702 44.0 44000 0.0866 0.8415 0.4787 1.3679 0.8415 0.8425 0.4403 0.0899
0.0703 45.0 45000 0.0866 0.8373 0.4788 1.3192 0.8373 0.8379 0.4374 0.0863
0.0702 46.0 46000 0.0865 0.841 0.4776 1.3357 0.841 0.8417 0.4398 0.0871
0.0703 47.0 47000 0.0864 0.8417 0.4772 1.3302 0.8417 0.8424 0.4406 0.0859
0.0705 48.0 48000 0.0865 0.841 0.4776 1.3096 0.841 0.8417 0.4398 0.0877
0.0703 49.0 49000 0.0864 0.8413 0.4775 1.3022 0.8413 0.8419 0.4400 0.0865
0.0703 50.0 50000 0.0864 0.842 0.4773 1.3055 0.842 0.8427 0.4404 0.0865

Framework versions