generated_from_trainer

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rvlcdip-tiny_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5

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
No log 1.0 125 1.3808 0.541 0.5996 3.3159 0.541 0.5235 0.1039 0.2209
No log 2.0 250 1.0577 0.6525 0.4662 2.6310 0.6525 0.6396 0.0871 0.1302
No log 3.0 375 0.9165 0.7075 0.4104 2.2685 0.7075 0.7041 0.0788 0.1048
1.3004 4.0 500 0.8505 0.7298 0.3804 2.1171 0.7298 0.7380 0.0622 0.0934
1.3004 5.0 625 0.8063 0.745 0.3603 2.1178 0.745 0.7359 0.0588 0.0814
1.3004 6.0 750 0.7441 0.7662 0.3348 1.9219 0.7663 0.7636 0.0545 0.0741
1.3004 7.0 875 0.6987 0.7732 0.3193 1.8601 0.7732 0.7741 0.0509 0.0697
0.4682 8.0 1000 0.7033 0.773 0.3240 1.8889 0.7730 0.7733 0.0516 0.0776
0.4682 9.0 1125 0.6973 0.7865 0.3151 1.9589 0.7865 0.7838 0.0441 0.0760
0.4682 10.0 1250 0.7068 0.7748 0.3252 2.0362 0.7748 0.7749 0.0515 0.0791
0.4682 11.0 1375 0.6988 0.7768 0.3285 1.9227 0.7768 0.7801 0.0555 0.0840
0.1899 12.0 1500 0.7048 0.7762 0.3303 1.9777 0.7762 0.7719 0.0627 0.0809
0.1899 13.0 1625 0.6842 0.7785 0.3240 1.9360 0.7785 0.7784 0.0614 0.0808
0.1899 14.0 1750 0.6993 0.7742 0.3319 1.9508 0.7742 0.7727 0.0731 0.0759
0.1899 15.0 1875 0.6936 0.7742 0.3333 1.9042 0.7742 0.7760 0.0717 0.0853
0.1304 16.0 2000 0.6818 0.7837 0.3233 1.9541 0.7837 0.7855 0.0713 0.0853
0.1304 17.0 2125 0.6757 0.78 0.3255 1.8818 0.78 0.7829 0.0755 0.0834
0.1304 18.0 2250 0.7018 0.781 0.3348 2.0078 0.7810 0.7829 0.0786 0.0876
0.1304 19.0 2375 0.6872 0.7775 0.3340 1.8345 0.7775 0.7786 0.0864 0.0787
0.11 20.0 2500 0.7054 0.7758 0.3379 1.9542 0.7758 0.7747 0.0731 0.0847
0.11 21.0 2625 0.7006 0.782 0.3371 1.8610 0.782 0.7813 0.0821 0.0891
0.11 22.0 2750 0.7046 0.775 0.3428 1.8464 0.775 0.7772 0.0833 0.0814
0.11 23.0 2875 0.6620 0.789 0.3201 1.8174 0.7890 0.7908 0.0761 0.0799
0.0979 24.0 3000 0.6886 0.783 0.3324 1.8706 0.7830 0.7848 0.0807 0.0773
0.0979 25.0 3125 0.6600 0.7847 0.3236 1.8218 0.7847 0.7863 0.0833 0.0749
0.0979 26.0 3250 0.6777 0.7798 0.3349 1.7189 0.7798 0.7812 0.0951 0.0752
0.0979 27.0 3375 0.6554 0.7857 0.3212 1.7356 0.7857 0.7888 0.0871 0.0709
0.087 28.0 3500 0.6460 0.7955 0.3140 1.7680 0.7955 0.7970 0.0761 0.0696
0.087 29.0 3625 0.6371 0.7935 0.3136 1.6350 0.7935 0.7946 0.0830 0.0706
0.087 30.0 3750 0.6334 0.7915 0.3127 1.7187 0.7915 0.7933 0.0857 0.0712
0.087 31.0 3875 0.6293 0.7977 0.3075 1.7781 0.7977 0.7999 0.0799 0.0661
0.0793 32.0 4000 0.6273 0.7973 0.3076 1.6439 0.7973 0.7976 0.0782 0.0695
0.0793 33.0 4125 0.6320 0.7933 0.3123 1.6486 0.7932 0.7954 0.0899 0.0679
0.0793 34.0 4250 0.6345 0.79 0.3154 1.6402 0.79 0.7903 0.0922 0.0675
0.0793 35.0 4375 0.6209 0.793 0.3098 1.6026 0.793 0.7943 0.0863 0.0630
0.0733 36.0 4500 0.6187 0.7947 0.3076 1.6282 0.7947 0.7967 0.0880 0.0666
0.0733 37.0 4625 0.6146 0.7957 0.3051 1.6186 0.7957 0.7971 0.0885 0.0623
0.0733 38.0 4750 0.6169 0.7983 0.3062 1.6182 0.7983 0.7996 0.0835 0.0650
0.0733 39.0 4875 0.6180 0.7953 0.3074 1.6241 0.7953 0.7975 0.0889 0.0655
0.0693 40.0 5000 0.6204 0.7977 0.3069 1.6048 0.7977 0.7987 0.0824 0.0659
0.0693 41.0 5125 0.6140 0.7967 0.3055 1.6065 0.7967 0.7986 0.0911 0.0662
0.0693 42.0 5250 0.6162 0.7957 0.3062 1.6182 0.7957 0.7971 0.0883 0.0655
0.0693 43.0 5375 0.6169 0.796 0.3058 1.6212 0.796 0.7976 0.0879 0.0662
0.0673 44.0 5500 0.6173 0.7973 0.3063 1.6161 0.7973 0.7990 0.0877 0.0666
0.0673 45.0 5625 0.6193 0.797 0.3070 1.6151 0.797 0.7986 0.0881 0.0678
0.0673 46.0 5750 0.6209 0.7963 0.3076 1.6211 0.7963 0.7979 0.0894 0.0678
0.0673 47.0 5875 0.6211 0.7977 0.3075 1.6284 0.7977 0.7993 0.0905 0.0691
0.0662 48.0 6000 0.6206 0.7967 0.3072 1.6289 0.7967 0.7983 0.0892 0.0673
0.0662 49.0 6125 0.6213 0.7965 0.3075 1.6262 0.7965 0.7980 0.0886 0.0684
0.0662 50.0 6250 0.6215 0.7963 0.3076 1.6291 0.7963 0.7978 0.0919 0.0682

Framework versions