generated_from_trainer

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dit-base-finetuned-rvlcdip-tiny_rvl_cdip-NK1000_hint

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
3.111 1.0 1000 2.9416 0.5917 0.5262 2.5737 0.5917 0.5835 0.0528 0.1821
2.5832 2.0 2000 2.4518 0.6917 0.4147 2.1569 0.6917 0.6919 0.0508 0.1107
2.2618 3.0 3000 2.2194 0.7418 0.3548 2.0905 0.7418 0.7417 0.0384 0.0775
2.0277 4.0 4000 2.1469 0.7575 0.3418 2.0638 0.7575 0.7547 0.0661 0.0729
1.9024 5.0 5000 2.1380 0.7355 0.3703 2.0583 0.7355 0.7365 0.0619 0.0880
1.7315 6.0 6000 2.0423 0.7508 0.3495 2.0467 0.7508 0.7566 0.0631 0.0752
1.5844 7.0 7000 2.0832 0.7628 0.3382 2.1301 0.7628 0.7651 0.0953 0.0689
1.4761 8.0 8000 2.2224 0.773 0.3548 2.1347 0.7730 0.7734 0.1284 0.0708
1.3852 9.0 9000 2.2341 0.7853 0.3452 2.0905 0.7853 0.7874 0.1349 0.0614
1.3234 10.0 10000 2.3403 0.778 0.3614 2.1125 0.778 0.7797 0.1530 0.0649
1.2546 11.0 11000 2.4153 0.7768 0.3675 2.1438 0.7768 0.7772 0.1601 0.0649
1.2161 12.0 12000 2.5661 0.7742 0.3810 2.1581 0.7742 0.7752 0.1715 0.0669
1.1611 13.0 13000 2.5638 0.789 0.3616 2.0957 0.7890 0.7888 0.1648 0.0595
1.1349 14.0 14000 2.6037 0.7957 0.3569 2.1299 0.7957 0.7963 0.1641 0.0578
1.1043 15.0 15000 2.6763 0.7817 0.3786 2.1078 0.7817 0.7855 0.1755 0.0680
1.0768 16.0 16000 2.6931 0.792 0.3636 2.1056 0.792 0.7942 0.1679 0.0601
1.0675 17.0 17000 2.6384 0.7957 0.3549 2.1658 0.7957 0.7941 0.1651 0.0570
1.0387 18.0 18000 2.8320 0.7825 0.3899 2.1964 0.7825 0.7804 0.1804 0.0706
1.035 19.0 19000 2.7127 0.7947 0.3641 2.0771 0.7947 0.7981 0.1741 0.0607
1.0053 20.0 20000 2.7164 0.8035 0.3508 2.0693 0.8035 0.8017 0.1638 0.0594
0.9783 21.0 21000 2.7162 0.8085 0.3475 2.0165 0.8085 0.8080 0.1622 0.0601
0.9606 22.0 22000 2.7740 0.804 0.3505 2.0738 0.804 0.8057 0.1678 0.0585
0.9579 23.0 23000 2.7597 0.803 0.3544 2.0507 0.803 0.8038 0.1668 0.0600
0.9439 24.0 24000 2.7108 0.809 0.3407 2.0218 0.809 0.8099 0.1626 0.0574
0.9247 25.0 25000 2.6918 0.8125 0.3355 2.0449 0.8125 0.8114 0.1580 0.0549
0.9275 26.0 26000 2.6996 0.8163 0.3316 2.0140 0.8163 0.8159 0.1585 0.0582
0.914 27.0 27000 2.7846 0.8113 0.3389 2.0190 0.8113 0.8110 0.1626 0.0598
0.9036 28.0 28000 2.7436 0.817 0.3341 2.0702 0.817 0.8166 0.1587 0.0564
0.893 29.0 29000 2.7354 0.8197 0.3272 2.0581 0.8197 0.8207 0.1551 0.0588
0.8815 30.0 30000 2.8377 0.813 0.3414 2.1163 0.813 0.8149 0.1630 0.0614
0.8688 31.0 31000 2.7815 0.8207 0.3310 2.0502 0.8207 0.8205 0.1576 0.0554
0.8727 32.0 32000 2.7370 0.82 0.3292 2.1149 0.82 0.8193 0.1563 0.0545
0.8581 33.0 33000 2.8168 0.812 0.3443 2.0026 0.8120 0.8146 0.1658 0.0594
0.8504 34.0 34000 2.7660 0.8173 0.3321 2.0497 0.8173 0.8181 0.1597 0.0556
0.8563 35.0 35000 2.8457 0.8097 0.3442 2.0815 0.8097 0.8107 0.1669 0.0592
0.8415 36.0 36000 2.7366 0.8245 0.3179 2.0282 0.8245 0.8251 0.1511 0.0566
0.8372 37.0 37000 2.7731 0.821 0.3249 2.1084 0.821 0.8198 0.1563 0.0546
0.8406 38.0 38000 2.6948 0.8283 0.3131 2.0343 0.8283 0.8281 0.1493 0.0533
0.831 39.0 39000 2.7781 0.827 0.3192 2.0592 0.827 0.8270 0.1534 0.0544
0.8223 40.0 40000 2.7811 0.8267 0.3161 2.0946 0.8267 0.8271 0.1512 0.0570
0.8258 41.0 41000 2.6993 0.827 0.3138 2.0347 0.827 0.8271 0.1507 0.0531
0.8209 42.0 42000 2.7467 0.828 0.3197 2.0159 0.828 0.8279 0.1530 0.0541
0.8146 43.0 43000 2.7050 0.8257 0.3159 2.0518 0.8257 0.8249 0.1526 0.0523
0.8161 44.0 44000 2.6919 0.8257 0.3160 1.9889 0.8257 0.8255 0.1515 0.0530
0.8121 45.0 45000 2.7314 0.8235 0.3210 2.0259 0.8235 0.8244 0.1542 0.0537
0.809 46.0 46000 2.7203 0.8275 0.3146 2.0431 0.8275 0.8272 0.1526 0.0514
0.8091 47.0 47000 2.7174 0.826 0.3176 2.0313 0.826 0.8253 0.1534 0.0527
0.8073 48.0 48000 2.7058 0.8277 0.3130 2.0258 0.8277 0.8272 0.1515 0.0519
0.8073 49.0 49000 2.7065 0.827 0.3146 2.0301 0.827 0.8266 0.1528 0.0523
0.8069 50.0 50000 2.7080 0.8275 0.3142 2.0399 0.8275 0.8270 0.1526 0.0520

Framework versions