generated_from_trainer

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vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_simkd

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 1.0 250 56.2115 0.3142 0.8385 3.5992 0.3142 0.2499 0.1012 0.5692
56.615 2.0 500 54.0327 0.4025 0.9176 3.1629 0.4025 0.3116 0.4002 0.3781
56.615 3.0 750 49.9569 0.4728 0.8906 2.8997 0.4728 0.4076 0.4129 0.2864
50.7474 4.0 1000 47.4945 0.5685 0.7670 2.6755 0.5685 0.5350 0.3561 0.2844
50.7474 5.0 1250 45.5054 0.6378 0.6629 2.5408 0.6378 0.6030 0.3212 0.1851
45.4907 6.0 1500 43.9471 0.679 0.5949 2.6322 0.679 0.6636 0.2925 0.1474
45.4907 7.0 1750 42.9273 0.7342 0.4843 2.4382 0.7342 0.7365 0.2245 0.1436
42.5191 8.0 2000 41.9715 0.7548 0.4560 2.3596 0.7548 0.7533 0.2231 0.1400
42.5191 9.0 2250 41.4349 0.7722 0.4310 2.3144 0.7722 0.7718 0.2103 0.1304
40.8849 10.0 2500 41.0961 0.7805 0.4187 2.2268 0.7805 0.7826 0.2047 0.1305
40.8849 11.0 2750 40.5831 0.7893 0.4030 2.1663 0.7893 0.7930 0.2001 0.1246
39.8394 12.0 3000 40.1596 0.7987 0.3877 2.1719 0.7987 0.8015 0.1929 0.1162
39.8394 13.0 3250 39.8469 0.8033 0.3821 2.1455 0.8033 0.8077 0.1889 0.1183
38.9442 14.0 3500 39.5865 0.8055 0.3761 2.1121 0.8055 0.8096 0.1864 0.1110
38.9442 15.0 3750 39.4686 0.81 0.3693 2.0948 0.81 0.8125 0.1831 0.1114
38.3612 16.0 4000 39.1387 0.8207 0.3446 1.9957 0.8207 0.8219 0.1716 0.1038
38.3612 17.0 4250 38.8950 0.8143 0.3575 2.0339 0.8143 0.8152 0.1781 0.1034
37.7855 18.0 4500 38.6442 0.8215 0.3442 1.9658 0.8215 0.8236 0.1718 0.1036
37.7855 19.0 4750 38.5218 0.8197 0.3477 1.9627 0.8197 0.8220 0.1735 0.1070
37.3649 20.0 5000 38.3474 0.8225 0.3413 1.9886 0.8225 0.8239 0.1710 0.1028
37.3649 21.0 5250 38.2377 0.8257 0.3358 1.9864 0.8257 0.8269 0.1674 0.0957
37.0326 22.0 5500 38.1089 0.824 0.3418 1.9404 0.824 0.8257 0.1678 0.0980
37.0326 23.0 5750 37.9861 0.8273 0.3339 1.9540 0.8273 0.8285 0.1664 0.0985
36.7372 24.0 6000 37.8397 0.8255 0.3376 1.9492 0.8255 0.8268 0.1685 0.0944
36.7372 25.0 6250 37.7772 0.8253 0.3370 1.9078 0.8253 0.8255 0.1669 0.0997
36.4341 26.0 6500 37.6550 0.828 0.3325 1.9388 0.828 0.8284 0.1647 0.0943
36.4341 27.0 6750 37.5873 0.8255 0.3364 1.9319 0.8255 0.8261 0.1680 0.0920
36.2152 28.0 7000 37.5052 0.825 0.3379 1.8945 0.825 0.8268 0.1681 0.0981
36.2152 29.0 7250 37.4586 0.8243 0.3361 1.9094 0.8243 0.8251 0.1692 0.0945
36.0128 30.0 7500 37.3730 0.8277 0.3304 1.9062 0.8277 0.8288 0.1657 0.0946
36.0128 31.0 7750 37.3309 0.8277 0.3309 1.9045 0.8277 0.8291 0.1660 0.0947
35.8486 32.0 8000 37.2620 0.8267 0.3323 1.8884 0.8267 0.8279 0.1652 0.0950
35.8486 33.0 8250 37.2147 0.8275 0.3308 1.9079 0.8275 0.8290 0.1654 0.0960
35.6854 34.0 8500 37.1911 0.831 0.3252 1.8935 0.831 0.8323 0.1613 0.0939
35.6854 35.0 8750 37.1523 0.8283 0.3301 1.8847 0.8283 0.8293 0.1644 0.0972
35.5758 36.0 9000 37.1315 0.8305 0.3252 1.8941 0.8305 0.8317 0.1627 0.0934
35.5758 37.0 9250 37.1184 0.8275 0.3320 1.8844 0.8275 0.8285 0.1654 0.0923
35.4911 38.0 9500 37.1149 0.827 0.3327 1.8885 0.827 0.8288 0.1668 0.0953
35.4911 39.0 9750 37.1067 0.8267 0.3323 1.8846 0.8267 0.8281 0.1659 0.0932
35.4248 40.0 10000 37.0792 0.8293 0.3294 1.8840 0.8293 0.8305 0.1633 0.0937
35.4248 41.0 10250 37.0798 0.8297 0.3288 1.8718 0.8297 0.8309 0.1639 0.0929
35.3648 42.0 10500 37.0635 0.8265 0.3351 1.8883 0.8265 0.8279 0.1680 0.0951
35.3648 43.0 10750 37.0470 0.828 0.3308 1.8746 0.828 0.8294 0.1656 0.0939
35.2961 44.0 11000 37.0305 0.8273 0.3321 1.8901 0.8273 0.8286 0.1657 0.0932
35.2961 45.0 11250 37.0261 0.8275 0.3315 1.8823 0.8275 0.8287 0.1650 0.0949
35.241 46.0 11500 37.0253 0.827 0.3311 1.8751 0.827 0.8283 0.1662 0.0940
35.241 47.0 11750 37.0200 0.8277 0.3321 1.8708 0.8277 0.8289 0.1653 0.0949
35.2059 48.0 12000 37.0165 0.8277 0.3305 1.8745 0.8277 0.8289 0.1650 0.0934
35.2059 49.0 12250 37.0130 0.8275 0.3312 1.8743 0.8275 0.8287 0.1655 0.0942
35.18 50.0 12500 37.0129 0.8277 0.3307 1.8775 0.8277 0.8289 0.1649 0.0944

Framework versions