generated_from_trainer

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vit-base_rvl-cdip-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
No log 1.0 250 76.1878 0.5783 0.5528 2.6021 0.5783 0.5765 0.0527 0.2026
76.425 2.0 500 75.1954 0.6558 0.4561 2.2844 0.6558 0.6549 0.0488 0.1337
76.425 3.0 750 74.7574 0.716 0.3935 2.2465 0.7160 0.7170 0.0489 0.0983
74.5686 4.0 1000 74.5759 0.7265 0.3815 2.1845 0.7265 0.7306 0.0445 0.0951
74.5686 5.0 1250 74.4539 0.7245 0.3774 2.2022 0.7245 0.7264 0.0560 0.0919
73.9702 6.0 1500 74.4498 0.7468 0.3680 2.1854 0.7468 0.7555 0.0829 0.0826
73.9702 7.0 1750 74.2701 0.773 0.3350 2.1685 0.7730 0.7724 0.0855 0.0683
73.5091 8.0 2000 74.2610 0.7675 0.3548 2.1544 0.7675 0.7704 0.1155 0.0709
73.5091 9.0 2250 74.2621 0.772 0.3501 2.2087 0.772 0.7703 0.1242 0.0638
73.2311 10.0 2500 74.2978 0.7592 0.3768 2.1953 0.7592 0.7592 0.1462 0.0738
73.2311 11.0 2750 74.3242 0.7645 0.3803 2.1374 0.7645 0.7603 0.1528 0.0747
73.0554 12.0 3000 74.2177 0.7847 0.3545 2.1892 0.7847 0.7862 0.1411 0.0650
73.0554 13.0 3250 74.2360 0.779 0.3598 2.1518 0.779 0.7781 0.1513 0.0629
72.9294 14.0 3500 74.2339 0.7772 0.3684 2.1404 0.7773 0.7799 0.1583 0.0644
72.9294 15.0 3750 74.1185 0.7953 0.3416 2.1394 0.7953 0.7966 0.1436 0.0562
72.8246 16.0 4000 74.1754 0.7915 0.3498 2.1599 0.7915 0.7929 0.1525 0.0606
72.8246 17.0 4250 74.2033 0.7885 0.3559 2.2161 0.7885 0.7898 0.1558 0.0597
72.7339 18.0 4500 74.2018 0.7873 0.3640 2.1417 0.7873 0.7881 0.1590 0.0613
72.7339 19.0 4750 74.1204 0.7913 0.3517 2.1363 0.7913 0.7927 0.1553 0.0601
72.6572 20.0 5000 74.0625 0.7975 0.3431 2.1165 0.7975 0.7989 0.1530 0.0587
72.6572 21.0 5250 74.2249 0.7893 0.3609 2.1703 0.7893 0.7909 0.1663 0.0620
72.5815 22.0 5500 74.1181 0.8025 0.3400 2.1457 0.8025 0.8024 0.1531 0.0543
72.5815 23.0 5750 74.0536 0.8113 0.3293 2.1567 0.8113 0.8121 0.1489 0.0511
72.5166 24.0 6000 74.0110 0.8073 0.3345 2.1831 0.8073 0.8072 0.1487 0.0524
72.5166 25.0 6250 74.1061 0.8005 0.3424 2.1431 0.8005 0.8013 0.1573 0.0576
72.4615 26.0 6500 74.0349 0.8013 0.3399 2.1286 0.8013 0.7997 0.1565 0.0548
72.4615 27.0 6750 74.0363 0.805 0.3416 2.1198 0.805 0.8057 0.1551 0.0573
72.4072 28.0 7000 74.0054 0.8107 0.3322 2.1186 0.8108 0.8104 0.1495 0.0528
72.4072 29.0 7250 74.0448 0.8043 0.3429 2.0845 0.8043 0.8058 0.1560 0.0563
72.3615 30.0 7500 73.9915 0.805 0.3376 2.1142 0.805 0.8059 0.1571 0.0527
72.3615 31.0 7750 73.9340 0.81 0.3284 2.0976 0.81 0.8101 0.1516 0.0500
72.3206 32.0 8000 73.9701 0.814 0.3264 2.1364 0.8140 0.8139 0.1488 0.0534
72.3206 33.0 8250 73.8978 0.8115 0.3287 2.1375 0.8115 0.8110 0.1517 0.0487
72.289 34.0 8500 73.8993 0.8175 0.3185 2.0686 0.8175 0.8196 0.1443 0.0505
72.289 35.0 8750 73.8655 0.814 0.3231 2.0881 0.8140 0.8149 0.1504 0.0488
72.2572 36.0 9000 73.8631 0.8153 0.3190 2.0729 0.8153 0.8158 0.1479 0.0489
72.2572 37.0 9250 73.8671 0.8163 0.3200 2.1224 0.8163 0.8154 0.1504 0.0486
72.2292 38.0 9500 73.8828 0.8155 0.3259 2.0859 0.8155 0.8151 0.1502 0.0476
72.2292 39.0 9750 73.8538 0.8115 0.3296 2.0611 0.8115 0.8119 0.1541 0.0493
72.2054 40.0 10000 73.8624 0.8115 0.3260 2.0991 0.8115 0.8113 0.1547 0.0481
72.2054 41.0 10250 73.8335 0.819 0.3199 2.0802 0.819 0.8189 0.1468 0.0479
72.1861 42.0 10500 73.8582 0.8123 0.3314 2.0555 0.8123 0.8130 0.1548 0.0490
72.1861 43.0 10750 73.8290 0.8153 0.3235 2.0956 0.8153 0.8158 0.1514 0.0480
72.1705 44.0 11000 73.8210 0.8107 0.3291 2.0636 0.8108 0.8112 0.1570 0.0489
72.1705 45.0 11250 73.8179 0.8143 0.3260 2.0835 0.8143 0.8148 0.1534 0.0474
72.1588 46.0 11500 73.8054 0.8117 0.3239 2.0814 0.8117 0.8122 0.1553 0.0479
72.1588 47.0 11750 73.8085 0.8137 0.3251 2.0705 0.8137 0.8138 0.1536 0.0485
72.1506 48.0 12000 73.8144 0.814 0.3254 2.0702 0.8140 0.8142 0.1534 0.0483
72.1506 49.0 12250 73.8181 0.8137 0.3252 2.0666 0.8137 0.8141 0.1539 0.0483
72.146 50.0 12500 73.8137 0.8137 0.3252 2.0673 0.8137 0.8140 0.1539 0.0483

Framework versions