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81-tiny_tobacco3482
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:
- Loss: 0.1635
- Accuracy: 0.815
- Brier Loss: 0.3719
- Nll: 0.7360
- F1 Micro: 0.815
- F1 Macro: 0.7973
- Ece: 0.3345
- Aurc: 0.0542
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:
- learning_rate: 0.0001
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
---|---|---|---|---|---|---|---|---|---|---|
No log | 1.0 | 7 | 2.2942 | 0.16 | 1.0273 | 8.4554 | 0.16 | 0.0543 | 0.3538 | 0.8479 |
No log | 2.0 | 14 | 1.1952 | 0.095 | 0.9063 | 7.1126 | 0.095 | 0.0819 | 0.2327 | 0.8615 |
No log | 3.0 | 21 | 0.7862 | 0.305 | 0.8434 | 5.1628 | 0.305 | 0.1452 | 0.2965 | 0.6092 |
No log | 4.0 | 28 | 0.6144 | 0.385 | 0.7594 | 3.3452 | 0.3850 | 0.2942 | 0.2978 | 0.4140 |
No log | 5.0 | 35 | 0.5040 | 0.54 | 0.6858 | 2.6433 | 0.54 | 0.4419 | 0.3200 | 0.2853 |
No log | 6.0 | 42 | 0.4291 | 0.625 | 0.6353 | 2.2564 | 0.625 | 0.5273 | 0.3772 | 0.1975 |
No log | 7.0 | 49 | 0.3839 | 0.63 | 0.5815 | 2.0215 | 0.63 | 0.4970 | 0.3469 | 0.1795 |
No log | 8.0 | 56 | 0.3390 | 0.705 | 0.5512 | 1.5239 | 0.705 | 0.5983 | 0.3704 | 0.1337 |
No log | 9.0 | 63 | 0.3128 | 0.705 | 0.5015 | 1.7837 | 0.705 | 0.6172 | 0.3403 | 0.1138 |
No log | 10.0 | 70 | 0.2868 | 0.705 | 0.4532 | 1.1243 | 0.705 | 0.6108 | 0.3221 | 0.1015 |
No log | 11.0 | 77 | 0.2866 | 0.74 | 0.4532 | 1.0967 | 0.74 | 0.6909 | 0.3422 | 0.0932 |
No log | 12.0 | 84 | 0.2625 | 0.765 | 0.4235 | 1.4646 | 0.765 | 0.6788 | 0.3240 | 0.0805 |
No log | 13.0 | 91 | 0.2516 | 0.73 | 0.4141 | 1.4855 | 0.7300 | 0.6495 | 0.2770 | 0.0869 |
No log | 14.0 | 98 | 0.2487 | 0.805 | 0.4440 | 1.2873 | 0.805 | 0.7319 | 0.3699 | 0.0670 |
No log | 15.0 | 105 | 0.2103 | 0.77 | 0.4029 | 1.0834 | 0.7700 | 0.6891 | 0.3007 | 0.0677 |
No log | 16.0 | 112 | 0.2216 | 0.8 | 0.4029 | 1.0621 | 0.8000 | 0.7247 | 0.3513 | 0.0611 |
No log | 17.0 | 119 | 0.2230 | 0.745 | 0.3999 | 1.0367 | 0.745 | 0.6710 | 0.2992 | 0.0809 |
No log | 18.0 | 126 | 0.2301 | 0.805 | 0.4044 | 0.9650 | 0.805 | 0.7473 | 0.3406 | 0.0538 |
No log | 19.0 | 133 | 0.2024 | 0.795 | 0.3935 | 0.9429 | 0.795 | 0.7562 | 0.3327 | 0.0669 |
No log | 20.0 | 140 | 0.1959 | 0.82 | 0.3935 | 0.8984 | 0.82 | 0.8000 | 0.3453 | 0.0571 |
No log | 21.0 | 147 | 0.2020 | 0.815 | 0.3946 | 0.8507 | 0.815 | 0.7949 | 0.3669 | 0.0588 |
No log | 22.0 | 154 | 0.2100 | 0.805 | 0.3758 | 0.8083 | 0.805 | 0.7743 | 0.3087 | 0.0625 |
No log | 23.0 | 161 | 0.2002 | 0.81 | 0.3881 | 1.0046 | 0.81 | 0.7953 | 0.3276 | 0.0617 |
No log | 24.0 | 168 | 0.2004 | 0.83 | 0.3987 | 0.8922 | 0.83 | 0.8034 | 0.3565 | 0.0531 |
No log | 25.0 | 175 | 0.1914 | 0.785 | 0.3699 | 0.8571 | 0.785 | 0.7484 | 0.3117 | 0.0601 |
No log | 26.0 | 182 | 0.1845 | 0.815 | 0.3764 | 0.8153 | 0.815 | 0.7970 | 0.3215 | 0.0539 |
No log | 27.0 | 189 | 0.1821 | 0.835 | 0.3815 | 0.8488 | 0.835 | 0.8175 | 0.3441 | 0.0497 |
No log | 28.0 | 196 | 0.1869 | 0.84 | 0.3808 | 0.8654 | 0.8400 | 0.8236 | 0.3639 | 0.0504 |
No log | 29.0 | 203 | 0.1859 | 0.79 | 0.3752 | 0.7067 | 0.79 | 0.7661 | 0.3019 | 0.0618 |
No log | 30.0 | 210 | 0.1842 | 0.795 | 0.3826 | 0.9031 | 0.795 | 0.7646 | 0.3170 | 0.0599 |
No log | 31.0 | 217 | 0.1797 | 0.815 | 0.3714 | 0.8572 | 0.815 | 0.8038 | 0.3214 | 0.0588 |
No log | 32.0 | 224 | 0.1754 | 0.805 | 0.3679 | 0.7412 | 0.805 | 0.7883 | 0.3063 | 0.0563 |
No log | 33.0 | 231 | 0.1790 | 0.835 | 0.3736 | 0.8357 | 0.835 | 0.8128 | 0.3431 | 0.0497 |
No log | 34.0 | 238 | 0.1761 | 0.81 | 0.3744 | 0.7360 | 0.81 | 0.7907 | 0.3292 | 0.0526 |
No log | 35.0 | 245 | 0.1731 | 0.83 | 0.3744 | 0.7494 | 0.83 | 0.8202 | 0.3351 | 0.0540 |
No log | 36.0 | 252 | 0.1758 | 0.79 | 0.3738 | 0.9441 | 0.79 | 0.7710 | 0.3099 | 0.0625 |
No log | 37.0 | 259 | 0.1730 | 0.81 | 0.3785 | 0.9418 | 0.81 | 0.7939 | 0.3425 | 0.0577 |
No log | 38.0 | 266 | 0.1727 | 0.815 | 0.3752 | 0.7539 | 0.815 | 0.8001 | 0.3290 | 0.0540 |
No log | 39.0 | 273 | 0.1769 | 0.81 | 0.3754 | 0.9013 | 0.81 | 0.7888 | 0.3296 | 0.0563 |
No log | 40.0 | 280 | 0.1770 | 0.805 | 0.3809 | 0.7637 | 0.805 | 0.7809 | 0.3291 | 0.0519 |
No log | 41.0 | 287 | 0.1771 | 0.815 | 0.3814 | 0.7495 | 0.815 | 0.7993 | 0.3352 | 0.0565 |
No log | 42.0 | 294 | 0.1745 | 0.8 | 0.3807 | 0.8386 | 0.8000 | 0.7865 | 0.3212 | 0.0601 |
No log | 43.0 | 301 | 0.1700 | 0.825 | 0.3762 | 0.8216 | 0.825 | 0.8067 | 0.3420 | 0.0517 |
No log | 44.0 | 308 | 0.1686 | 0.825 | 0.3706 | 0.7310 | 0.825 | 0.8024 | 0.3448 | 0.0509 |
No log | 45.0 | 315 | 0.1692 | 0.81 | 0.3739 | 0.6805 | 0.81 | 0.7914 | 0.3397 | 0.0508 |
No log | 46.0 | 322 | 0.1697 | 0.825 | 0.3709 | 0.7482 | 0.825 | 0.7995 | 0.3364 | 0.0531 |
No log | 47.0 | 329 | 0.1681 | 0.805 | 0.3735 | 0.8810 | 0.805 | 0.7872 | 0.3388 | 0.0585 |
No log | 48.0 | 336 | 0.1667 | 0.815 | 0.3731 | 0.6788 | 0.815 | 0.7968 | 0.3427 | 0.0567 |
No log | 49.0 | 343 | 0.1690 | 0.82 | 0.3786 | 0.8693 | 0.82 | 0.7967 | 0.3507 | 0.0502 |
No log | 50.0 | 350 | 0.1668 | 0.82 | 0.3761 | 0.7854 | 0.82 | 0.8032 | 0.3509 | 0.0526 |
No log | 51.0 | 357 | 0.1659 | 0.82 | 0.3760 | 0.7437 | 0.82 | 0.8045 | 0.3374 | 0.0557 |
No log | 52.0 | 364 | 0.1663 | 0.805 | 0.3682 | 0.7932 | 0.805 | 0.7863 | 0.3171 | 0.0560 |
No log | 53.0 | 371 | 0.1649 | 0.805 | 0.3649 | 0.7249 | 0.805 | 0.7863 | 0.3139 | 0.0562 |
No log | 54.0 | 378 | 0.1654 | 0.815 | 0.3725 | 0.7390 | 0.815 | 0.7935 | 0.3273 | 0.0560 |
No log | 55.0 | 385 | 0.1667 | 0.84 | 0.3741 | 0.7324 | 0.8400 | 0.8279 | 0.3501 | 0.0513 |
No log | 56.0 | 392 | 0.1659 | 0.825 | 0.3718 | 0.7278 | 0.825 | 0.8058 | 0.3288 | 0.0522 |
No log | 57.0 | 399 | 0.1659 | 0.825 | 0.3731 | 0.7351 | 0.825 | 0.8084 | 0.3204 | 0.0537 |
No log | 58.0 | 406 | 0.1645 | 0.825 | 0.3730 | 0.7356 | 0.825 | 0.8068 | 0.3468 | 0.0524 |
No log | 59.0 | 413 | 0.1626 | 0.825 | 0.3715 | 0.7282 | 0.825 | 0.8040 | 0.3391 | 0.0527 |
No log | 60.0 | 420 | 0.1628 | 0.825 | 0.3698 | 0.7297 | 0.825 | 0.8056 | 0.3212 | 0.0534 |
No log | 61.0 | 427 | 0.1630 | 0.825 | 0.3719 | 0.7312 | 0.825 | 0.8065 | 0.3260 | 0.0533 |
No log | 62.0 | 434 | 0.1642 | 0.82 | 0.3714 | 0.7335 | 0.82 | 0.8040 | 0.3388 | 0.0549 |
No log | 63.0 | 441 | 0.1634 | 0.82 | 0.3733 | 0.7349 | 0.82 | 0.8011 | 0.3399 | 0.0553 |
No log | 64.0 | 448 | 0.1628 | 0.815 | 0.3710 | 0.7301 | 0.815 | 0.7974 | 0.3249 | 0.0532 |
No log | 65.0 | 455 | 0.1629 | 0.815 | 0.3704 | 0.7362 | 0.815 | 0.7981 | 0.3362 | 0.0541 |
No log | 66.0 | 462 | 0.1630 | 0.82 | 0.3727 | 0.7354 | 0.82 | 0.8024 | 0.3559 | 0.0526 |
No log | 67.0 | 469 | 0.1633 | 0.825 | 0.3733 | 0.7355 | 0.825 | 0.8038 | 0.3492 | 0.0526 |
No log | 68.0 | 476 | 0.1638 | 0.82 | 0.3724 | 0.7369 | 0.82 | 0.8040 | 0.3468 | 0.0550 |
No log | 69.0 | 483 | 0.1629 | 0.82 | 0.3717 | 0.7329 | 0.82 | 0.8040 | 0.3251 | 0.0536 |
No log | 70.0 | 490 | 0.1629 | 0.815 | 0.3709 | 0.7312 | 0.815 | 0.7947 | 0.3221 | 0.0541 |
No log | 71.0 | 497 | 0.1630 | 0.815 | 0.3718 | 0.7371 | 0.815 | 0.7973 | 0.3436 | 0.0545 |
0.1743 | 72.0 | 504 | 0.1631 | 0.815 | 0.3712 | 0.7350 | 0.815 | 0.7973 | 0.3264 | 0.0536 |
0.1743 | 73.0 | 511 | 0.1634 | 0.815 | 0.3721 | 0.7348 | 0.815 | 0.7981 | 0.3340 | 0.0543 |
0.1743 | 74.0 | 518 | 0.1631 | 0.815 | 0.3716 | 0.7332 | 0.815 | 0.7973 | 0.3279 | 0.0541 |
0.1743 | 75.0 | 525 | 0.1633 | 0.82 | 0.3720 | 0.7346 | 0.82 | 0.8008 | 0.3186 | 0.0542 |
0.1743 | 76.0 | 532 | 0.1631 | 0.815 | 0.3716 | 0.7342 | 0.815 | 0.7973 | 0.3189 | 0.0542 |
0.1743 | 77.0 | 539 | 0.1633 | 0.815 | 0.3718 | 0.7358 | 0.815 | 0.7973 | 0.3344 | 0.0542 |
0.1743 | 78.0 | 546 | 0.1633 | 0.815 | 0.3718 | 0.7363 | 0.815 | 0.7991 | 0.3342 | 0.0542 |
0.1743 | 79.0 | 553 | 0.1633 | 0.815 | 0.3718 | 0.7350 | 0.815 | 0.7973 | 0.3344 | 0.0543 |
0.1743 | 80.0 | 560 | 0.1634 | 0.815 | 0.3718 | 0.7354 | 0.815 | 0.7973 | 0.3136 | 0.0542 |
0.1743 | 81.0 | 567 | 0.1633 | 0.815 | 0.3721 | 0.7360 | 0.815 | 0.7973 | 0.3279 | 0.0543 |
0.1743 | 82.0 | 574 | 0.1634 | 0.815 | 0.3719 | 0.7358 | 0.815 | 0.7973 | 0.3276 | 0.0542 |
0.1743 | 83.0 | 581 | 0.1634 | 0.815 | 0.3720 | 0.7358 | 0.815 | 0.7973 | 0.3345 | 0.0542 |
0.1743 | 84.0 | 588 | 0.1633 | 0.815 | 0.3717 | 0.7352 | 0.815 | 0.7973 | 0.3275 | 0.0542 |
0.1743 | 85.0 | 595 | 0.1633 | 0.815 | 0.3720 | 0.7357 | 0.815 | 0.7973 | 0.3194 | 0.0541 |
0.1743 | 86.0 | 602 | 0.1634 | 0.815 | 0.3717 | 0.7361 | 0.815 | 0.7973 | 0.3276 | 0.0542 |
0.1743 | 87.0 | 609 | 0.1634 | 0.815 | 0.3719 | 0.7359 | 0.815 | 0.7973 | 0.3345 | 0.0542 |
0.1743 | 88.0 | 616 | 0.1634 | 0.815 | 0.3720 | 0.7359 | 0.815 | 0.7973 | 0.3278 | 0.0542 |
0.1743 | 89.0 | 623 | 0.1634 | 0.815 | 0.3718 | 0.7359 | 0.815 | 0.7973 | 0.3192 | 0.0543 |
0.1743 | 90.0 | 630 | 0.1635 | 0.815 | 0.3719 | 0.7359 | 0.815 | 0.7973 | 0.3278 | 0.0543 |
0.1743 | 91.0 | 637 | 0.1635 | 0.815 | 0.3719 | 0.7357 | 0.815 | 0.7973 | 0.3344 | 0.0541 |
0.1743 | 92.0 | 644 | 0.1635 | 0.815 | 0.3719 | 0.7361 | 0.815 | 0.7973 | 0.3278 | 0.0542 |
0.1743 | 93.0 | 651 | 0.1635 | 0.815 | 0.3719 | 0.7357 | 0.815 | 0.7973 | 0.3345 | 0.0542 |
0.1743 | 94.0 | 658 | 0.1635 | 0.815 | 0.3719 | 0.7356 | 0.815 | 0.7973 | 0.3261 | 0.0543 |
0.1743 | 95.0 | 665 | 0.1635 | 0.815 | 0.3719 | 0.7360 | 0.815 | 0.7973 | 0.3345 | 0.0542 |
0.1743 | 96.0 | 672 | 0.1635 | 0.815 | 0.3719 | 0.7357 | 0.815 | 0.7973 | 0.3278 | 0.0542 |
0.1743 | 97.0 | 679 | 0.1635 | 0.815 | 0.3719 | 0.7360 | 0.815 | 0.7973 | 0.3345 | 0.0542 |
0.1743 | 98.0 | 686 | 0.1635 | 0.815 | 0.3719 | 0.7360 | 0.815 | 0.7973 | 0.3278 | 0.0543 |
0.1743 | 99.0 | 693 | 0.1635 | 0.815 | 0.3719 | 0.7360 | 0.815 | 0.7973 | 0.3345 | 0.0542 |
0.1743 | 100.0 | 700 | 0.1635 | 0.815 | 0.3719 | 0.7360 | 0.815 | 0.7973 | 0.3345 | 0.0542 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2