generated_from_trainer

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cdip-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 2.1936 0.55 0.5790 2.6092 0.55 0.5380 0.0723 0.2136
No log 2.0 250 1.5136 0.666 0.4551 2.2832 0.666 0.6632 0.0686 0.1282
No log 3.0 375 1.2758 0.6993 0.4127 2.2483 0.6993 0.6928 0.0836 0.1026
1.9726 4.0 500 1.1382 0.726 0.3794 2.0849 0.726 0.7288 0.0725 0.0878
1.9726 5.0 625 1.0625 0.7542 0.3523 2.1178 0.7542 0.7426 0.0755 0.0758
1.9726 6.0 750 0.8657 0.7775 0.3184 1.9516 0.7775 0.7783 0.0736 0.0618
1.9726 7.0 875 0.8285 0.7887 0.3089 2.0439 0.7887 0.7867 0.0724 0.0586
0.5092 8.0 1000 0.7988 0.7925 0.3056 2.0106 0.7925 0.7953 0.0700 0.0572
0.5092 9.0 1125 0.7783 0.7925 0.3060 1.9710 0.7925 0.7914 0.0822 0.0573
0.5092 10.0 1250 0.7640 0.796 0.3007 1.9819 0.796 0.7996 0.0801 0.0536
0.5092 11.0 1375 0.7705 0.7913 0.3048 1.9588 0.7913 0.7952 0.0857 0.0559
0.2071 12.0 1500 0.7328 0.8015 0.2937 1.9484 0.8015 0.8017 0.0760 0.0537
0.2071 13.0 1625 0.6946 0.811 0.2881 1.9173 0.811 0.8125 0.0824 0.0507
0.2071 14.0 1750 0.6902 0.8053 0.2880 1.9154 0.8053 0.8068 0.0738 0.0502
0.2071 15.0 1875 0.6756 0.8083 0.2840 1.9317 0.8083 0.8078 0.0761 0.0487
0.1424 16.0 2000 0.6684 0.8067 0.2852 1.9192 0.8067 0.8073 0.0765 0.0507
0.1424 17.0 2125 0.6548 0.8095 0.2816 1.9398 0.8095 0.8110 0.0758 0.0472
0.1424 18.0 2250 0.6477 0.8117 0.2762 1.9054 0.8117 0.8140 0.0759 0.0464
0.1424 19.0 2375 0.6423 0.8145 0.2794 1.9081 0.8145 0.8148 0.0774 0.0478
0.1102 20.0 2500 0.6312 0.8103 0.2771 1.9581 0.8103 0.8125 0.0746 0.0454
0.1102 21.0 2625 0.6299 0.8133 0.2720 1.9275 0.8133 0.8132 0.0758 0.0466
0.1102 22.0 2750 0.6148 0.8197 0.2691 1.9463 0.8197 0.8223 0.0681 0.0447
0.1102 23.0 2875 0.6132 0.8187 0.2700 1.9301 0.8187 0.8200 0.0691 0.0451
0.0931 24.0 3000 0.5995 0.8245 0.2649 1.9173 0.8245 0.8251 0.0640 0.0444
0.0931 25.0 3125 0.6020 0.8177 0.2697 1.9205 0.8178 0.8201 0.0723 0.0440
0.0931 26.0 3250 0.5914 0.8247 0.2617 1.9385 0.8247 0.8264 0.0667 0.0428
0.0931 27.0 3375 0.5833 0.822 0.2621 1.9390 0.822 0.8228 0.0658 0.0429
0.0789 28.0 3500 0.5884 0.8247 0.2626 1.9400 0.8247 0.8259 0.0619 0.0435
0.0789 29.0 3625 0.5771 0.8285 0.2568 1.9252 0.8285 0.8313 0.0612 0.0413
0.0789 30.0 3750 0.5815 0.823 0.2628 1.9413 0.823 0.8236 0.0676 0.0433
0.0789 31.0 3875 0.5789 0.8205 0.2617 1.9209 0.8205 0.8219 0.0667 0.0431
0.0686 32.0 4000 0.5775 0.8247 0.2616 1.9045 0.8247 0.8265 0.0674 0.0428
0.0686 33.0 4125 0.5744 0.827 0.2603 1.9088 0.827 0.8275 0.0656 0.0420
0.0686 34.0 4250 0.5685 0.824 0.2607 1.9372 0.824 0.8264 0.0647 0.0421
0.0686 35.0 4375 0.5649 0.8255 0.2584 1.9375 0.8255 0.8274 0.0694 0.0419
0.0596 36.0 4500 0.5629 0.8263 0.2574 1.9304 0.8263 0.8283 0.0651 0.0415
0.0596 37.0 4625 0.5622 0.8237 0.2579 1.9228 0.8237 0.8254 0.0644 0.0419
0.0596 38.0 4750 0.5623 0.8257 0.2579 1.9310 0.8257 0.8277 0.0650 0.0418
0.0596 39.0 4875 0.5625 0.827 0.2579 1.9311 0.827 0.8286 0.0668 0.0418
0.0538 40.0 5000 0.5633 0.8247 0.2590 1.9264 0.8247 0.8264 0.0671 0.0424
0.0538 41.0 5125 0.5607 0.8257 0.2575 1.9239 0.8257 0.8275 0.0621 0.0417
0.0538 42.0 5250 0.5605 0.8263 0.2569 1.9305 0.8263 0.8279 0.0620 0.0418
0.0538 43.0 5375 0.5613 0.8255 0.2581 1.9295 0.8255 0.8272 0.0672 0.0418
0.0512 44.0 5500 0.5616 0.8255 0.2581 1.9235 0.8255 0.8273 0.0636 0.0419
0.0512 45.0 5625 0.5624 0.8253 0.2585 1.9206 0.8253 0.8270 0.0646 0.0422
0.0512 46.0 5750 0.5622 0.8257 0.2582 1.9283 0.8257 0.8273 0.0647 0.0420
0.0512 47.0 5875 0.5616 0.825 0.2581 1.9283 0.825 0.8267 0.0640 0.0420
0.05 48.0 6000 0.5620 0.8257 0.2584 1.9262 0.8257 0.8275 0.0644 0.0421
0.05 49.0 6125 0.5622 0.8253 0.2585 1.9257 0.8253 0.8270 0.0630 0.0421
0.05 50.0 6250 0.5622 0.8255 0.2585 1.9229 0.8255 0.8273 0.0661 0.0421

Framework versions