generated_from_trainer

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vit-base_rvl-cdip-small_rvl_cdip-NK1000_hint

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
75.292 1.0 1000 74.9267 0.669 0.4449 2.2033 0.669 0.6688 0.0509 0.1261
74.3102 2.0 2000 74.4753 0.7228 0.3815 2.1695 0.7228 0.7241 0.0443 0.0931
73.8711 3.0 3000 74.1051 0.7718 0.3245 1.9516 0.7717 0.7726 0.0520 0.0687
73.8038 4.0 4000 74.0142 0.764 0.3412 1.9168 0.764 0.7656 0.0862 0.0705
73.5111 5.0 5000 73.9154 0.762 0.3443 1.9320 0.762 0.7684 0.0774 0.0734
73.2405 6.0 6000 73.6544 0.7923 0.3034 1.9497 0.7923 0.7877 0.0780 0.0555
72.892 7.0 7000 73.6057 0.8073 0.3042 1.9087 0.8073 0.8074 0.1072 0.0549
72.6235 8.0 8000 73.6740 0.8007 0.3175 1.9674 0.8007 0.8002 0.1221 0.0566
72.6609 9.0 9000 73.6103 0.7977 0.3290 2.0128 0.7977 0.7992 0.1345 0.0595
72.4643 10.0 10000 73.5996 0.8143 0.3166 1.9412 0.8143 0.8148 0.1372 0.0498
72.2435 11.0 11000 73.5005 0.8057 0.3268 1.9323 0.8057 0.8070 0.1469 0.0530
72.1757 12.0 12000 73.5284 0.8165 0.3197 1.9353 0.8165 0.8175 0.1442 0.0511
72.0746 13.0 13000 73.5250 0.8023 0.3434 2.0172 0.8023 0.8047 0.1577 0.0584
72.0251 14.0 14000 73.3937 0.817 0.3176 1.9784 0.817 0.8172 0.1471 0.0510
71.8588 15.0 15000 73.3792 0.814 0.3249 1.9812 0.8140 0.8148 0.1506 0.0514
71.8093 16.0 16000 73.2188 0.825 0.3084 1.9155 0.825 0.8259 0.1446 0.0449
71.5835 17.0 17000 73.2452 0.8307 0.3019 1.9447 0.8308 0.8308 0.1386 0.0437
71.6995 18.0 18000 73.2568 0.83 0.3042 2.0599 0.83 0.8304 0.1444 0.0459
71.4455 19.0 19000 73.2655 0.8203 0.3204 2.0173 0.8203 0.8205 0.1524 0.0484
71.4047 20.0 20000 73.1947 0.8203 0.3218 1.9651 0.8203 0.8218 0.1524 0.0484
71.5116 21.0 21000 73.1398 0.8217 0.3197 1.9803 0.8217 0.8209 0.1521 0.0453
71.3315 22.0 22000 73.0498 0.832 0.3033 1.9617 0.832 0.8327 0.1445 0.0478
71.329 23.0 23000 73.0645 0.8277 0.3129 1.9073 0.8277 0.8293 0.1502 0.0486
71.2208 24.0 24000 73.1448 0.8257 0.3188 2.0056 0.8257 0.8252 0.1511 0.0520
71.22 25.0 25000 72.9177 0.827 0.3103 1.9543 0.827 0.8261 0.1493 0.0500
71.1268 26.0 26000 72.9064 0.8323 0.3022 1.9069 0.8323 0.8311 0.1458 0.0463
70.8954 27.0 27000 72.8821 0.8403 0.2930 1.9264 0.8403 0.8405 0.1399 0.0454
70.7553 28.0 28000 72.8230 0.8347 0.3012 1.9356 0.8347 0.8347 0.1434 0.0487
70.8785 29.0 29000 72.8549 0.8347 0.3023 1.9905 0.8347 0.8339 0.1464 0.0477
70.8139 30.0 30000 72.8073 0.8405 0.2933 1.9148 0.8405 0.8400 0.1399 0.0482
70.9162 31.0 31000 72.7751 0.8367 0.3005 1.9466 0.8367 0.8364 0.1441 0.0473
70.8988 32.0 32000 72.7235 0.8365 0.2990 1.9178 0.8365 0.8362 0.1432 0.0453
70.7529 33.0 33000 72.6744 0.8415 0.2937 1.9929 0.8415 0.8424 0.1391 0.0491
70.6705 34.0 34000 72.6624 0.8407 0.2927 1.9562 0.8407 0.8423 0.1417 0.0490
70.6404 35.0 35000 72.7689 0.8317 0.3071 2.0079 0.8317 0.8311 0.1479 0.0551
70.5201 36.0 36000 72.6579 0.8425 0.2928 1.9879 0.8425 0.8431 0.1404 0.0519
70.6383 37.0 37000 72.5850 0.8458 0.2839 1.9513 0.8458 0.8470 0.1362 0.0500
70.5781 38.0 38000 72.5590 0.8423 0.2894 1.9416 0.8423 0.8427 0.1407 0.0496
70.4386 39.0 39000 72.5131 0.8435 0.2855 1.9475 0.8435 0.8443 0.1397 0.0492
70.4275 40.0 40000 72.5441 0.8455 0.2896 1.9216 0.8455 0.8460 0.1387 0.0481
70.5018 41.0 41000 72.5071 0.8452 0.2866 1.9148 0.8452 0.8465 0.1384 0.0486
70.5176 42.0 42000 72.5110 0.8445 0.2862 1.9591 0.8445 0.8447 0.1384 0.0523
70.3675 43.0 43000 72.4968 0.844 0.2845 1.9376 0.844 0.8445 0.1398 0.0507
70.4295 44.0 44000 72.4633 0.846 0.2838 1.9475 0.8460 0.8464 0.1381 0.0506
70.5198 45.0 45000 72.4940 0.8468 0.2828 1.9559 0.8468 0.8471 0.1374 0.0501
70.3838 46.0 46000 72.4658 0.8462 0.2823 1.9468 0.8462 0.8465 0.1366 0.0500
70.5383 47.0 47000 72.4590 0.8475 0.2817 1.9537 0.8475 0.8480 0.1362 0.0502
70.3619 48.0 48000 72.4522 0.8458 0.2824 1.9632 0.8458 0.8461 0.1377 0.0495
70.4764 49.0 49000 72.4573 0.8455 0.2825 1.9689 0.8455 0.8457 0.1378 0.0499
70.3705 50.0 50000 72.4419 0.8455 0.2823 1.9713 0.8455 0.8458 0.1379 0.0501

Framework versions