generated_from_trainer

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vit-tiny_tobacco3482_simkd_CEKD_tNone_aNone_tNone_gNone

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 25 0.0553 0.085 0.8991 5.2518 0.085 0.0595 0.1614 0.8792
No log 2.0 50 0.0488 0.035 0.9007 7.4288 0.035 0.0069 0.1218 0.9410
No log 3.0 75 0.0481 0.045 0.8999 6.0525 0.045 0.0087 0.1349 0.9308
No log 4.0 100 0.0478 0.05 0.8991 5.6444 0.0500 0.0149 0.1378 0.9211
No log 5.0 125 0.0475 0.14 0.8981 5.8239 0.14 0.0863 0.1987 0.8452
No log 6.0 150 0.0471 0.305 0.8964 5.7469 0.305 0.1652 0.3097 0.5016
No log 7.0 175 0.0466 0.305 0.8950 4.9568 0.305 0.1899 0.3035 0.5165
No log 8.0 200 0.0461 0.315 0.8931 4.8214 0.315 0.1811 0.3152 0.4687
No log 9.0 225 0.0455 0.315 0.8907 4.7406 0.315 0.2028 0.3225 0.4671
No log 10.0 250 0.0449 0.35 0.8862 4.7538 0.35 0.1972 0.3364 0.4263
No log 11.0 275 0.0443 0.37 0.8793 4.6283 0.37 0.2106 0.3455 0.4084
No log 12.0 300 0.0438 0.4 0.8731 3.9664 0.4000 0.2443 0.3685 0.3731
No log 13.0 325 0.0434 0.425 0.8628 3.9702 0.425 0.2574 0.3842 0.3601
No log 14.0 350 0.0430 0.465 0.8586 3.8630 0.465 0.3226 0.4112 0.3024
No log 15.0 375 0.0428 0.46 0.8488 3.9046 0.46 0.2693 0.4082 0.2854
No log 16.0 400 0.0424 0.475 0.8430 3.2916 0.4750 0.2802 0.4183 0.2626
No log 17.0 425 0.0421 0.555 0.8439 2.7780 0.555 0.4109 0.4760 0.2123
No log 18.0 450 0.0418 0.575 0.8317 2.8629 0.575 0.4399 0.4869 0.2123
No log 19.0 475 0.0415 0.665 0.8329 2.5145 0.665 0.5077 0.5655 0.1361
0.0491 20.0 500 0.0412 0.635 0.8121 2.7489 0.635 0.5155 0.5235 0.1686
0.0491 21.0 525 0.0410 0.655 0.8221 1.7853 0.655 0.5182 0.5509 0.1545
0.0491 22.0 550 0.0406 0.685 0.8045 1.5894 0.685 0.5486 0.5627 0.1305
0.0491 23.0 575 0.0405 0.68 0.7984 1.7241 0.68 0.5489 0.5545 0.1296
0.0491 24.0 600 0.0402 0.725 0.7959 1.5667 0.7250 0.6156 0.5926 0.1055
0.0491 25.0 625 0.0402 0.68 0.7927 1.4334 0.68 0.5853 0.5453 0.1239
0.0491 26.0 650 0.0401 0.705 0.7808 1.8114 0.705 0.5856 0.5735 0.1109
0.0491 27.0 675 0.0399 0.71 0.7859 1.6101 0.7100 0.6176 0.5679 0.1034
0.0491 28.0 700 0.0399 0.715 0.7808 1.3423 0.715 0.6612 0.5582 0.1130
0.0491 29.0 725 0.0398 0.705 0.7789 1.3921 0.705 0.6477 0.5615 0.1175
0.0491 30.0 750 0.0397 0.73 0.7767 1.5801 0.7300 0.6758 0.5741 0.1069
0.0491 31.0 775 0.0397 0.72 0.7774 1.3193 0.72 0.6653 0.5790 0.1004
0.0491 32.0 800 0.0396 0.745 0.7729 1.4864 0.745 0.6931 0.5941 0.0933
0.0491 33.0 825 0.0396 0.74 0.7736 1.5161 0.74 0.6901 0.5828 0.0934
0.0491 34.0 850 0.0396 0.745 0.7754 1.5432 0.745 0.6963 0.5911 0.0857
0.0491 35.0 875 0.0396 0.74 0.7744 1.4773 0.74 0.6936 0.5966 0.0896
0.0491 36.0 900 0.0397 0.715 0.7762 1.3769 0.715 0.6827 0.5675 0.1048
0.0491 37.0 925 0.0396 0.72 0.7744 1.3882 0.72 0.6780 0.5689 0.0970
0.0491 38.0 950 0.0396 0.72 0.7762 1.4098 0.72 0.6874 0.5701 0.1016
0.0491 39.0 975 0.0395 0.74 0.7728 1.3890 0.74 0.6894 0.5861 0.0902
0.0386 40.0 1000 0.0396 0.74 0.7724 1.5265 0.74 0.6936 0.5906 0.0881
0.0386 41.0 1025 0.0396 0.725 0.7730 1.3516 0.7250 0.6768 0.5784 0.0942
0.0386 42.0 1050 0.0396 0.73 0.7728 1.3633 0.7300 0.6847 0.5899 0.0945
0.0386 43.0 1075 0.0396 0.735 0.7730 1.3670 0.735 0.6874 0.5830 0.0940
0.0386 44.0 1100 0.0395 0.73 0.7727 1.4707 0.7300 0.6850 0.5914 0.0930
0.0386 45.0 1125 0.0396 0.725 0.7721 1.4269 0.7250 0.6810 0.5770 0.0934
0.0386 46.0 1150 0.0396 0.72 0.7730 1.3567 0.72 0.6793 0.5717 0.0976
0.0386 47.0 1175 0.0396 0.715 0.7731 1.3708 0.715 0.6757 0.5717 0.0974
0.0386 48.0 1200 0.0396 0.735 0.7724 1.4118 0.735 0.6874 0.5791 0.0923
0.0386 49.0 1225 0.0396 0.72 0.7729 1.3647 0.72 0.6837 0.5711 0.0965
0.0386 50.0 1250 0.0396 0.725 0.7727 1.3773 0.7250 0.6820 0.5740 0.0963
0.0386 51.0 1275 0.0396 0.73 0.7736 1.3286 0.7300 0.6847 0.5766 0.0939
0.0386 52.0 1300 0.0396 0.725 0.7732 1.3810 0.7250 0.6817 0.5830 0.0944
0.0386 53.0 1325 0.0396 0.725 0.7725 1.3568 0.7250 0.6820 0.5763 0.0948
0.0386 54.0 1350 0.0396 0.73 0.7731 1.3693 0.7300 0.6847 0.5768 0.0941
0.0386 55.0 1375 0.0396 0.745 0.7728 1.3631 0.745 0.7112 0.5842 0.0928
0.0386 56.0 1400 0.0396 0.715 0.7731 1.4175 0.715 0.6712 0.5600 0.0976
0.0386 57.0 1425 0.0396 0.725 0.7725 1.3668 0.7250 0.6929 0.5738 0.0962
0.0386 58.0 1450 0.0396 0.73 0.7734 1.3903 0.7300 0.6958 0.5868 0.0963
0.0386 59.0 1475 0.0396 0.725 0.7729 1.4120 0.7250 0.6765 0.5756 0.0945
0.0373 60.0 1500 0.0396 0.725 0.7732 1.3655 0.7250 0.6820 0.5754 0.0951
0.0373 61.0 1525 0.0396 0.745 0.7727 1.3676 0.745 0.7038 0.5913 0.0921
0.0373 62.0 1550 0.0396 0.72 0.7729 1.3629 0.72 0.6797 0.5762 0.0969
0.0373 63.0 1575 0.0396 0.725 0.7730 1.4242 0.7250 0.6865 0.5811 0.0950
0.0373 64.0 1600 0.0396 0.725 0.7735 1.3658 0.7250 0.6923 0.5750 0.0959
0.0373 65.0 1625 0.0396 0.73 0.7731 1.4296 0.7300 0.6958 0.5769 0.0954
0.0373 66.0 1650 0.0396 0.735 0.7727 1.4780 0.735 0.6980 0.5851 0.0938
0.0373 67.0 1675 0.0396 0.725 0.7725 1.3669 0.7250 0.6824 0.5715 0.0938
0.0373 68.0 1700 0.0396 0.725 0.7730 1.4327 0.7250 0.6804 0.5741 0.0940
0.0373 69.0 1725 0.0396 0.73 0.7728 1.3811 0.7300 0.6961 0.5806 0.0963
0.0373 70.0 1750 0.0396 0.735 0.7727 1.3812 0.735 0.7081 0.5765 0.0952
0.0373 71.0 1775 0.0396 0.73 0.7730 1.4263 0.7300 0.6961 0.5739 0.0953
0.0373 72.0 1800 0.0396 0.73 0.7731 1.4280 0.7300 0.6953 0.5803 0.0956
0.0373 73.0 1825 0.0396 0.735 0.7729 1.3676 0.735 0.6988 0.5889 0.0953
0.0373 74.0 1850 0.0396 0.735 0.7727 1.4358 0.735 0.6985 0.5828 0.0940
0.0373 75.0 1875 0.0396 0.735 0.7727 1.4306 0.735 0.6965 0.5786 0.0940
0.0373 76.0 1900 0.0396 0.73 0.7729 1.4343 0.7300 0.6957 0.5802 0.0958
0.0373 77.0 1925 0.0396 0.73 0.7726 1.4259 0.7300 0.6961 0.5795 0.0962
0.0373 78.0 1950 0.0396 0.74 0.7731 1.4246 0.74 0.7080 0.5879 0.0941
0.0373 79.0 1975 0.0396 0.735 0.7730 1.4414 0.735 0.6980 0.5914 0.0945
0.0372 80.0 2000 0.0396 0.74 0.7727 1.4285 0.74 0.7103 0.5915 0.0939
0.0372 81.0 2025 0.0396 0.735 0.7731 1.4379 0.735 0.6980 0.5826 0.0942
0.0372 82.0 2050 0.0396 0.735 0.7729 1.4308 0.735 0.6963 0.5827 0.0942
0.0372 83.0 2075 0.0396 0.735 0.7728 1.4329 0.735 0.6968 0.5896 0.0946
0.0372 84.0 2100 0.0396 0.735 0.7728 1.4343 0.735 0.6948 0.5889 0.0947
0.0372 85.0 2125 0.0396 0.735 0.7727 1.4320 0.735 0.6948 0.5988 0.0945
0.0372 86.0 2150 0.0396 0.735 0.7730 1.4366 0.735 0.6963 0.5883 0.0949
0.0372 87.0 2175 0.0396 0.73 0.7728 1.4825 0.7300 0.6888 0.5878 0.0945
0.0372 88.0 2200 0.0396 0.735 0.7731 1.4339 0.735 0.6945 0.5828 0.0948
0.0372 89.0 2225 0.0396 0.735 0.7729 1.4383 0.735 0.6948 0.5917 0.0946
0.0372 90.0 2250 0.0396 0.735 0.7729 1.4471 0.735 0.6948 0.5867 0.0944
0.0372 91.0 2275 0.0396 0.735 0.7728 1.4402 0.735 0.6948 0.5892 0.0946
0.0372 92.0 2300 0.0396 0.735 0.7729 1.4412 0.735 0.6948 0.5952 0.0948
0.0372 93.0 2325 0.0396 0.735 0.7729 1.4709 0.735 0.6948 0.5917 0.0948
0.0372 94.0 2350 0.0396 0.735 0.7728 1.4413 0.735 0.6948 0.5858 0.0947
0.0372 95.0 2375 0.0396 0.735 0.7729 1.4422 0.735 0.6948 0.5917 0.0946
0.0372 96.0 2400 0.0396 0.735 0.7729 1.4527 0.735 0.6948 0.5917 0.0946
0.0372 97.0 2425 0.0396 0.735 0.7729 1.4441 0.735 0.6948 0.5917 0.0946
0.0372 98.0 2450 0.0396 0.735 0.7729 1.4423 0.735 0.6948 0.5917 0.0946
0.0372 99.0 2475 0.0396 0.735 0.7729 1.4457 0.735 0.6948 0.5886 0.0948
0.0372 100.0 2500 0.0396 0.735 0.7729 1.4473 0.735 0.6948 0.5886 0.0947

Framework versions