generated_from_trainer

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vit-small_tobacco3482_simkd_CEKD_tNone_aNone_tNone_gNone

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 25 0.0506 0.09 0.8991 6.5155 0.09 0.0484 0.1622 0.8986
No log 2.0 50 0.0468 0.22 0.8982 4.6950 0.22 0.1025 0.2491 0.7656
No log 3.0 75 0.0463 0.29 0.8969 3.3099 0.29 0.1676 0.2924 0.6888
No log 4.0 100 0.0459 0.37 0.8954 3.2920 0.37 0.1891 0.3517 0.4208
No log 5.0 125 0.0455 0.395 0.8929 3.2550 0.395 0.2299 0.3759 0.3617
No log 6.0 150 0.0449 0.49 0.8885 2.9109 0.49 0.3135 0.4396 0.2804
No log 7.0 175 0.0441 0.495 0.8796 2.8950 0.495 0.3248 0.4360 0.2721
No log 8.0 200 0.0430 0.545 0.8619 2.5199 0.545 0.3771 0.4777 0.2129
No log 9.0 225 0.0418 0.62 0.8382 2.2126 0.62 0.4291 0.5298 0.1659
No log 10.0 250 0.0409 0.645 0.8137 2.2525 0.645 0.4947 0.5293 0.1552
No log 11.0 275 0.0401 0.68 0.7863 2.4423 0.68 0.5145 0.5433 0.1215
No log 12.0 300 0.0392 0.68 0.7628 1.9779 0.68 0.5373 0.5402 0.1172
No log 13.0 325 0.0385 0.745 0.7350 1.8986 0.745 0.6126 0.5806 0.0843
No log 14.0 350 0.0384 0.735 0.7268 1.9922 0.735 0.6451 0.5466 0.0997
No log 15.0 375 0.0381 0.745 0.7180 1.6965 0.745 0.6627 0.5586 0.0761
No log 16.0 400 0.0377 0.805 0.7031 1.2564 0.805 0.7353 0.6034 0.0713
No log 17.0 425 0.0389 0.745 0.7303 1.5063 0.745 0.7192 0.5779 0.0705
No log 18.0 450 0.0387 0.765 0.7219 1.5776 0.765 0.7703 0.5815 0.0923
No log 19.0 475 0.0383 0.805 0.7213 1.3953 0.805 0.7906 0.6159 0.0667
0.0432 20.0 500 0.0377 0.835 0.6952 1.3075 0.835 0.8271 0.6116 0.0799
0.0432 21.0 525 0.0381 0.795 0.7018 1.6184 0.795 0.7723 0.5851 0.0880
0.0432 22.0 550 0.0378 0.81 0.6984 1.4292 0.81 0.7950 0.6103 0.0673
0.0432 23.0 575 0.0380 0.805 0.6976 1.4852 0.805 0.7951 0.5942 0.0808
0.0432 24.0 600 0.0377 0.825 0.6907 1.4501 0.825 0.8103 0.6020 0.0774
0.0432 25.0 625 0.0377 0.83 0.6920 1.4509 0.83 0.8148 0.6038 0.0759
0.0432 26.0 650 0.0377 0.825 0.6927 1.4113 0.825 0.8114 0.6072 0.0765
0.0432 27.0 675 0.0377 0.825 0.6924 1.4044 0.825 0.8114 0.6057 0.0757
0.0432 28.0 700 0.0377 0.82 0.6932 1.4521 0.82 0.8061 0.6017 0.0815
0.0432 29.0 725 0.0377 0.82 0.6932 1.3593 0.82 0.8080 0.5983 0.0794
0.0432 30.0 750 0.0377 0.82 0.6926 1.3437 0.82 0.8069 0.6042 0.0819
0.0432 31.0 775 0.0377 0.815 0.6932 1.3453 0.815 0.8027 0.5988 0.0815
0.0432 32.0 800 0.0377 0.82 0.6930 1.3384 0.82 0.8029 0.6044 0.0855
0.0432 33.0 825 0.0377 0.81 0.6928 1.3969 0.81 0.7927 0.5929 0.0835
0.0432 34.0 850 0.0378 0.805 0.6927 1.3995 0.805 0.7886 0.5961 0.0855
0.0432 35.0 875 0.0377 0.81 0.6927 1.3705 0.81 0.7979 0.5910 0.0887
0.0432 36.0 900 0.0378 0.805 0.6930 1.3566 0.805 0.7886 0.5850 0.0817
0.0432 37.0 925 0.0377 0.82 0.6927 1.3537 0.82 0.8022 0.5936 0.0847
0.0432 38.0 950 0.0377 0.815 0.6930 1.3574 0.815 0.7978 0.5976 0.0854
0.0432 39.0 975 0.0377 0.815 0.6932 1.4599 0.815 0.7978 0.5955 0.0864
0.035 40.0 1000 0.0377 0.815 0.6926 1.4147 0.815 0.7978 0.5990 0.0869
0.035 41.0 1025 0.0377 0.81 0.6931 1.4065 0.81 0.7943 0.5966 0.0844
0.035 42.0 1050 0.0378 0.81 0.6929 1.4678 0.81 0.7961 0.5902 0.0891
0.035 43.0 1075 0.0378 0.81 0.6927 1.4164 0.81 0.7971 0.5951 0.0897
0.035 44.0 1100 0.0378 0.81 0.6930 1.4646 0.81 0.7961 0.5948 0.0875
0.035 45.0 1125 0.0378 0.815 0.6921 1.4660 0.815 0.8004 0.6024 0.0895
0.035 46.0 1150 0.0378 0.81 0.6929 1.4098 0.81 0.7961 0.5987 0.0831
0.035 47.0 1175 0.0378 0.815 0.6928 1.4634 0.815 0.8004 0.5963 0.0911
0.035 48.0 1200 0.0378 0.81 0.6932 1.4648 0.81 0.7961 0.5841 0.0877
0.035 49.0 1225 0.0378 0.81 0.6928 1.4635 0.81 0.7961 0.5955 0.0898
0.035 50.0 1250 0.0378 0.805 0.6935 1.4688 0.805 0.7882 0.5795 0.0902
0.035 51.0 1275 0.0378 0.805 0.6928 1.4665 0.805 0.7882 0.5848 0.0916
0.035 52.0 1300 0.0378 0.81 0.6925 1.4249 0.81 0.7961 0.5869 0.0926
0.035 53.0 1325 0.0378 0.815 0.6926 1.4150 0.815 0.8021 0.5934 0.0913
0.035 54.0 1350 0.0378 0.81 0.6929 1.4155 0.81 0.7961 0.5943 0.0913
0.035 55.0 1375 0.0378 0.805 0.6928 1.4141 0.805 0.7882 0.5934 0.0964
0.035 56.0 1400 0.0378 0.805 0.6930 1.4124 0.805 0.7882 0.5926 0.0958
0.035 57.0 1425 0.0378 0.81 0.6935 1.4116 0.81 0.7934 0.6002 0.0895
0.035 58.0 1450 0.0378 0.805 0.6928 1.4059 0.805 0.7882 0.5890 0.0937
0.035 59.0 1475 0.0378 0.805 0.6929 1.4141 0.805 0.7882 0.5918 0.0967
0.0348 60.0 1500 0.0378 0.81 0.6935 1.4086 0.81 0.7934 0.5915 0.0934
0.0348 61.0 1525 0.0378 0.81 0.6930 1.4105 0.81 0.7941 0.5954 0.0961
0.0348 62.0 1550 0.0378 0.81 0.6933 1.4166 0.81 0.7941 0.5889 0.0954
0.0348 63.0 1575 0.0378 0.81 0.6933 1.4109 0.81 0.7934 0.5963 0.0975
0.0348 64.0 1600 0.0378 0.81 0.6932 1.4131 0.81 0.7934 0.5980 0.0953
0.0348 65.0 1625 0.0378 0.81 0.6937 1.4182 0.81 0.7934 0.5956 0.0970
0.0348 66.0 1650 0.0378 0.805 0.6933 1.4125 0.805 0.7893 0.5905 0.0966
0.0348 67.0 1675 0.0378 0.81 0.6937 1.4136 0.81 0.7934 0.5965 0.0975
0.0348 68.0 1700 0.0379 0.81 0.6935 1.4137 0.81 0.7934 0.5994 0.0971
0.0348 69.0 1725 0.0378 0.805 0.6935 1.4196 0.805 0.7893 0.5913 0.0946
0.0348 70.0 1750 0.0379 0.805 0.6933 1.4129 0.805 0.7893 0.5877 0.0945
0.0348 71.0 1775 0.0379 0.805 0.6933 1.4172 0.805 0.7893 0.5921 0.0951
0.0348 72.0 1800 0.0379 0.805 0.6931 1.4136 0.805 0.7893 0.5851 0.0953
0.0348 73.0 1825 0.0379 0.805 0.6929 1.4168 0.805 0.7893 0.5846 0.0971
0.0348 74.0 1850 0.0379 0.805 0.6939 1.4185 0.805 0.7893 0.5892 0.0950
0.0348 75.0 1875 0.0379 0.805 0.6935 1.4171 0.805 0.7893 0.5946 0.0938
0.0348 76.0 1900 0.0379 0.805 0.6934 1.4217 0.805 0.7893 0.5939 0.0959
0.0348 77.0 1925 0.0379 0.8 0.6932 1.4162 0.8000 0.7859 0.5826 0.0954
0.0348 78.0 1950 0.0379 0.8 0.6935 1.4172 0.8000 0.7859 0.5912 0.0950
0.0348 79.0 1975 0.0379 0.8 0.6933 1.4169 0.8000 0.7859 0.5885 0.0964
0.0348 80.0 2000 0.0379 0.8 0.6935 1.4196 0.8000 0.7859 0.5865 0.0957
0.0348 81.0 2025 0.0379 0.8 0.6937 1.4213 0.8000 0.7859 0.5880 0.0962
0.0348 82.0 2050 0.0379 0.8 0.6939 1.4201 0.8000 0.7859 0.5910 0.0962
0.0348 83.0 2075 0.0379 0.8 0.6938 1.3762 0.8000 0.7859 0.5883 0.0945
0.0348 84.0 2100 0.0379 0.8 0.6938 1.4218 0.8000 0.7859 0.5947 0.0950
0.0348 85.0 2125 0.0379 0.8 0.6935 1.3657 0.8000 0.7859 0.5857 0.0912
0.0348 86.0 2150 0.0379 0.8 0.6938 1.3278 0.8000 0.7859 0.5892 0.0929
0.0348 87.0 2175 0.0379 0.8 0.6938 1.3831 0.8000 0.7859 0.5856 0.0946
0.0348 88.0 2200 0.0379 0.8 0.6938 1.3761 0.8000 0.7859 0.5892 0.0955
0.0348 89.0 2225 0.0379 0.8 0.6938 1.3296 0.8000 0.7859 0.5870 0.0947
0.0348 90.0 2250 0.0379 0.8 0.6939 1.3667 0.8000 0.7859 0.5909 0.0926
0.0348 91.0 2275 0.0379 0.8 0.6940 1.3346 0.8000 0.7859 0.5906 0.0930
0.0348 92.0 2300 0.0379 0.8 0.6938 1.3268 0.8000 0.7859 0.5870 0.0936
0.0348 93.0 2325 0.0379 0.8 0.6937 1.3320 0.8000 0.7859 0.5919 0.0939
0.0348 94.0 2350 0.0379 0.8 0.6939 1.3324 0.8000 0.7859 0.5870 0.0928
0.0348 95.0 2375 0.0379 0.8 0.6937 1.3289 0.8000 0.7859 0.5869 0.0932
0.0348 96.0 2400 0.0379 0.8 0.6938 1.3264 0.8000 0.7859 0.5869 0.0931
0.0348 97.0 2425 0.0379 0.8 0.6938 1.3280 0.8000 0.7859 0.5870 0.0932
0.0348 98.0 2450 0.0379 0.8 0.6938 1.3297 0.8000 0.7859 0.5869 0.0930
0.0348 99.0 2475 0.0379 0.8 0.6938 1.3304 0.8000 0.7859 0.5869 0.0929
0.0347 100.0 2500 0.0379 0.8 0.6938 1.3290 0.8000 0.7859 0.5869 0.0931

Framework versions