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vit-small_tobacco3482_kd_CEKD_t1.5_a0.9
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:
- Loss: 0.5492
- Accuracy: 0.84
- Brier Loss: 0.2438
- Nll: 1.0175
- F1 Micro: 0.8400
- F1 Macro: 0.8329
- Ece: 0.1581
- Aurc: 0.0460
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 | 2.1371 | 0.215 | 0.8750 | 5.2661 | 0.2150 | 0.1261 | 0.2616 | 0.6901 |
No log | 2.0 | 14 | 1.7146 | 0.4 | 0.7405 | 3.6392 | 0.4000 | 0.2249 | 0.2801 | 0.4047 |
No log | 3.0 | 21 | 1.1877 | 0.625 | 0.5608 | 2.0254 | 0.625 | 0.5681 | 0.3161 | 0.2040 |
No log | 4.0 | 28 | 0.8633 | 0.715 | 0.4058 | 1.6421 | 0.715 | 0.6656 | 0.2020 | 0.1142 |
No log | 5.0 | 35 | 0.8597 | 0.72 | 0.3947 | 1.6962 | 0.72 | 0.7299 | 0.2181 | 0.1133 |
No log | 6.0 | 42 | 0.7266 | 0.785 | 0.3157 | 1.6428 | 0.785 | 0.7648 | 0.2063 | 0.0758 |
No log | 7.0 | 49 | 0.7662 | 0.77 | 0.3428 | 1.4695 | 0.7700 | 0.7666 | 0.1871 | 0.0998 |
No log | 8.0 | 56 | 0.7824 | 0.77 | 0.3365 | 1.5995 | 0.7700 | 0.7346 | 0.1840 | 0.0980 |
No log | 9.0 | 63 | 0.7245 | 0.805 | 0.3102 | 1.2669 | 0.805 | 0.8012 | 0.1789 | 0.0855 |
No log | 10.0 | 70 | 0.6787 | 0.8 | 0.2944 | 1.3351 | 0.8000 | 0.7754 | 0.1578 | 0.0665 |
No log | 11.0 | 77 | 0.6497 | 0.805 | 0.2870 | 1.3980 | 0.805 | 0.8029 | 0.1743 | 0.0709 |
No log | 12.0 | 84 | 0.6353 | 0.82 | 0.2747 | 1.3397 | 0.82 | 0.8085 | 0.1670 | 0.0687 |
No log | 13.0 | 91 | 0.7204 | 0.79 | 0.3163 | 1.4500 | 0.79 | 0.7945 | 0.1660 | 0.0782 |
No log | 14.0 | 98 | 0.6632 | 0.825 | 0.2714 | 1.5658 | 0.825 | 0.8110 | 0.1827 | 0.0726 |
No log | 15.0 | 105 | 0.6417 | 0.8 | 0.2840 | 1.3774 | 0.8000 | 0.7984 | 0.1618 | 0.0703 |
No log | 16.0 | 112 | 0.5899 | 0.825 | 0.2687 | 1.0331 | 0.825 | 0.8220 | 0.1569 | 0.0603 |
No log | 17.0 | 119 | 0.5924 | 0.83 | 0.2508 | 1.4167 | 0.83 | 0.8182 | 0.1414 | 0.0549 |
No log | 18.0 | 126 | 0.5885 | 0.825 | 0.2608 | 1.1991 | 0.825 | 0.8174 | 0.1677 | 0.0607 |
No log | 19.0 | 133 | 0.5898 | 0.82 | 0.2634 | 1.2879 | 0.82 | 0.8145 | 0.1563 | 0.0610 |
No log | 20.0 | 140 | 0.5509 | 0.825 | 0.2439 | 1.1130 | 0.825 | 0.8127 | 0.1532 | 0.0475 |
No log | 21.0 | 147 | 0.5719 | 0.82 | 0.2585 | 1.1331 | 0.82 | 0.8101 | 0.1640 | 0.0490 |
No log | 22.0 | 154 | 0.5650 | 0.85 | 0.2449 | 1.2095 | 0.85 | 0.8429 | 0.1622 | 0.0595 |
No log | 23.0 | 161 | 0.5538 | 0.83 | 0.2492 | 1.0979 | 0.83 | 0.8227 | 0.1759 | 0.0515 |
No log | 24.0 | 168 | 0.5514 | 0.84 | 0.2396 | 1.1748 | 0.8400 | 0.8360 | 0.1449 | 0.0479 |
No log | 25.0 | 175 | 0.5549 | 0.815 | 0.2497 | 1.0876 | 0.815 | 0.8080 | 0.1668 | 0.0502 |
No log | 26.0 | 182 | 0.5469 | 0.84 | 0.2397 | 1.1651 | 0.8400 | 0.8317 | 0.1560 | 0.0471 |
No log | 27.0 | 189 | 0.5584 | 0.84 | 0.2508 | 1.0605 | 0.8400 | 0.8253 | 0.1801 | 0.0486 |
No log | 28.0 | 196 | 0.5395 | 0.845 | 0.2371 | 1.0749 | 0.845 | 0.8302 | 0.1448 | 0.0438 |
No log | 29.0 | 203 | 0.5478 | 0.84 | 0.2436 | 1.0599 | 0.8400 | 0.8271 | 0.1556 | 0.0470 |
No log | 30.0 | 210 | 0.5432 | 0.835 | 0.2402 | 1.0595 | 0.835 | 0.8206 | 0.1613 | 0.0457 |
No log | 31.0 | 217 | 0.5454 | 0.83 | 0.2422 | 1.0518 | 0.83 | 0.8176 | 0.1556 | 0.0462 |
No log | 32.0 | 224 | 0.5456 | 0.83 | 0.2415 | 1.0500 | 0.83 | 0.8176 | 0.1555 | 0.0461 |
No log | 33.0 | 231 | 0.5471 | 0.835 | 0.2430 | 1.0492 | 0.835 | 0.8233 | 0.1616 | 0.0466 |
No log | 34.0 | 238 | 0.5456 | 0.83 | 0.2424 | 1.0495 | 0.83 | 0.8176 | 0.1636 | 0.0467 |
No log | 35.0 | 245 | 0.5482 | 0.835 | 0.2434 | 1.0438 | 0.835 | 0.8239 | 0.1717 | 0.0474 |
No log | 36.0 | 252 | 0.5462 | 0.835 | 0.2425 | 1.0461 | 0.835 | 0.8239 | 0.1507 | 0.0462 |
No log | 37.0 | 259 | 0.5488 | 0.83 | 0.2435 | 1.0468 | 0.83 | 0.8176 | 0.1377 | 0.0471 |
No log | 38.0 | 266 | 0.5461 | 0.84 | 0.2420 | 1.0389 | 0.8400 | 0.8296 | 0.1379 | 0.0458 |
No log | 39.0 | 273 | 0.5458 | 0.84 | 0.2423 | 1.0387 | 0.8400 | 0.8296 | 0.1545 | 0.0457 |
No log | 40.0 | 280 | 0.5483 | 0.835 | 0.2435 | 1.0382 | 0.835 | 0.8233 | 0.1343 | 0.0466 |
No log | 41.0 | 287 | 0.5475 | 0.835 | 0.2430 | 1.0378 | 0.835 | 0.8233 | 0.1408 | 0.0454 |
No log | 42.0 | 294 | 0.5463 | 0.835 | 0.2424 | 1.0368 | 0.835 | 0.8233 | 0.1463 | 0.0454 |
No log | 43.0 | 301 | 0.5467 | 0.835 | 0.2428 | 1.0335 | 0.835 | 0.8233 | 0.1453 | 0.0458 |
No log | 44.0 | 308 | 0.5470 | 0.835 | 0.2429 | 1.0331 | 0.835 | 0.8233 | 0.1597 | 0.0459 |
No log | 45.0 | 315 | 0.5469 | 0.835 | 0.2426 | 1.0336 | 0.835 | 0.8233 | 0.1487 | 0.0459 |
No log | 46.0 | 322 | 0.5473 | 0.835 | 0.2431 | 1.0322 | 0.835 | 0.8233 | 0.1486 | 0.0465 |
No log | 47.0 | 329 | 0.5464 | 0.84 | 0.2425 | 1.0324 | 0.8400 | 0.8329 | 0.1443 | 0.0454 |
No log | 48.0 | 336 | 0.5462 | 0.835 | 0.2426 | 1.0298 | 0.835 | 0.8233 | 0.1527 | 0.0454 |
No log | 49.0 | 343 | 0.5471 | 0.835 | 0.2427 | 1.0305 | 0.835 | 0.8233 | 0.1619 | 0.0456 |
No log | 50.0 | 350 | 0.5479 | 0.84 | 0.2433 | 1.0304 | 0.8400 | 0.8329 | 0.1549 | 0.0457 |
No log | 51.0 | 357 | 0.5471 | 0.835 | 0.2427 | 1.0296 | 0.835 | 0.8233 | 0.1607 | 0.0458 |
No log | 52.0 | 364 | 0.5475 | 0.835 | 0.2431 | 1.0282 | 0.835 | 0.8233 | 0.1596 | 0.0458 |
No log | 53.0 | 371 | 0.5474 | 0.84 | 0.2428 | 1.0294 | 0.8400 | 0.8329 | 0.1603 | 0.0457 |
No log | 54.0 | 378 | 0.5482 | 0.835 | 0.2436 | 1.0263 | 0.835 | 0.8233 | 0.1460 | 0.0461 |
No log | 55.0 | 385 | 0.5468 | 0.84 | 0.2424 | 1.0264 | 0.8400 | 0.8329 | 0.1491 | 0.0454 |
No log | 56.0 | 392 | 0.5479 | 0.84 | 0.2432 | 1.0263 | 0.8400 | 0.8329 | 0.1594 | 0.0452 |
No log | 57.0 | 399 | 0.5467 | 0.84 | 0.2426 | 1.0259 | 0.8400 | 0.8329 | 0.1476 | 0.0454 |
No log | 58.0 | 406 | 0.5484 | 0.835 | 0.2434 | 1.0237 | 0.835 | 0.8233 | 0.1379 | 0.0463 |
No log | 59.0 | 413 | 0.5473 | 0.835 | 0.2429 | 1.0245 | 0.835 | 0.8233 | 0.1521 | 0.0458 |
No log | 60.0 | 420 | 0.5475 | 0.835 | 0.2430 | 1.0240 | 0.835 | 0.8233 | 0.1523 | 0.0458 |
No log | 61.0 | 427 | 0.5475 | 0.835 | 0.2430 | 1.0239 | 0.835 | 0.8233 | 0.1438 | 0.0461 |
No log | 62.0 | 434 | 0.5476 | 0.835 | 0.2430 | 1.0227 | 0.835 | 0.8233 | 0.1522 | 0.0461 |
No log | 63.0 | 441 | 0.5478 | 0.835 | 0.2430 | 1.0235 | 0.835 | 0.8233 | 0.1520 | 0.0460 |
No log | 64.0 | 448 | 0.5478 | 0.84 | 0.2432 | 1.0215 | 0.8400 | 0.8329 | 0.1576 | 0.0458 |
No log | 65.0 | 455 | 0.5478 | 0.835 | 0.2430 | 1.0229 | 0.835 | 0.8233 | 0.1592 | 0.0461 |
No log | 66.0 | 462 | 0.5481 | 0.84 | 0.2433 | 1.0219 | 0.8400 | 0.8329 | 0.1582 | 0.0459 |
No log | 67.0 | 469 | 0.5482 | 0.84 | 0.2434 | 1.0214 | 0.8400 | 0.8329 | 0.1665 | 0.0456 |
No log | 68.0 | 476 | 0.5482 | 0.835 | 0.2433 | 1.0209 | 0.835 | 0.8233 | 0.1445 | 0.0463 |
No log | 69.0 | 483 | 0.5484 | 0.84 | 0.2435 | 1.0210 | 0.8400 | 0.8329 | 0.1578 | 0.0458 |
No log | 70.0 | 490 | 0.5479 | 0.84 | 0.2433 | 1.0206 | 0.8400 | 0.8329 | 0.1662 | 0.0457 |
No log | 71.0 | 497 | 0.5486 | 0.84 | 0.2435 | 1.0210 | 0.8400 | 0.8329 | 0.1401 | 0.0460 |
0.1783 | 72.0 | 504 | 0.5489 | 0.84 | 0.2437 | 1.0204 | 0.8400 | 0.8329 | 0.1581 | 0.0460 |
0.1783 | 73.0 | 511 | 0.5483 | 0.835 | 0.2435 | 1.0194 | 0.835 | 0.8233 | 0.1712 | 0.0460 |
0.1783 | 74.0 | 518 | 0.5489 | 0.84 | 0.2437 | 1.0198 | 0.8400 | 0.8329 | 0.1668 | 0.0461 |
0.1783 | 75.0 | 525 | 0.5486 | 0.84 | 0.2435 | 1.0194 | 0.8400 | 0.8329 | 0.1666 | 0.0458 |
0.1783 | 76.0 | 532 | 0.5487 | 0.84 | 0.2436 | 1.0194 | 0.8400 | 0.8329 | 0.1710 | 0.0458 |
0.1783 | 77.0 | 539 | 0.5485 | 0.84 | 0.2434 | 1.0191 | 0.8400 | 0.8329 | 0.1392 | 0.0459 |
0.1783 | 78.0 | 546 | 0.5486 | 0.84 | 0.2435 | 1.0191 | 0.8400 | 0.8329 | 0.1579 | 0.0458 |
0.1783 | 79.0 | 553 | 0.5486 | 0.84 | 0.2436 | 1.0190 | 0.8400 | 0.8329 | 0.1582 | 0.0459 |
0.1783 | 80.0 | 560 | 0.5492 | 0.84 | 0.2438 | 1.0194 | 0.8400 | 0.8329 | 0.1581 | 0.0461 |
0.1783 | 81.0 | 567 | 0.5486 | 0.84 | 0.2435 | 1.0189 | 0.8400 | 0.8329 | 0.1581 | 0.0460 |
0.1783 | 82.0 | 574 | 0.5489 | 0.84 | 0.2437 | 1.0185 | 0.8400 | 0.8329 | 0.1581 | 0.0460 |
0.1783 | 83.0 | 581 | 0.5491 | 0.84 | 0.2438 | 1.0188 | 0.8400 | 0.8329 | 0.1574 | 0.0460 |
0.1783 | 84.0 | 588 | 0.5490 | 0.84 | 0.2438 | 1.0183 | 0.8400 | 0.8329 | 0.1581 | 0.0461 |
0.1783 | 85.0 | 595 | 0.5491 | 0.84 | 0.2438 | 1.0184 | 0.8400 | 0.8329 | 0.1485 | 0.0461 |
0.1783 | 86.0 | 602 | 0.5492 | 0.84 | 0.2439 | 1.0177 | 0.8400 | 0.8329 | 0.1584 | 0.0461 |
0.1783 | 87.0 | 609 | 0.5491 | 0.84 | 0.2438 | 1.0180 | 0.8400 | 0.8329 | 0.1582 | 0.0461 |
0.1783 | 88.0 | 616 | 0.5493 | 0.84 | 0.2438 | 1.0180 | 0.8400 | 0.8329 | 0.1584 | 0.0462 |
0.1783 | 89.0 | 623 | 0.5493 | 0.84 | 0.2438 | 1.0178 | 0.8400 | 0.8329 | 0.1584 | 0.0462 |
0.1783 | 90.0 | 630 | 0.5490 | 0.84 | 0.2437 | 1.0180 | 0.8400 | 0.8329 | 0.1584 | 0.0461 |
0.1783 | 91.0 | 637 | 0.5491 | 0.84 | 0.2438 | 1.0177 | 0.8400 | 0.8329 | 0.1581 | 0.0459 |
0.1783 | 92.0 | 644 | 0.5492 | 0.84 | 0.2438 | 1.0177 | 0.8400 | 0.8329 | 0.1582 | 0.0461 |
0.1783 | 93.0 | 651 | 0.5491 | 0.84 | 0.2437 | 1.0180 | 0.8400 | 0.8329 | 0.1581 | 0.0460 |
0.1783 | 94.0 | 658 | 0.5491 | 0.84 | 0.2438 | 1.0180 | 0.8400 | 0.8329 | 0.1584 | 0.0461 |
0.1783 | 95.0 | 665 | 0.5492 | 0.84 | 0.2438 | 1.0177 | 0.8400 | 0.8329 | 0.1582 | 0.0461 |
0.1783 | 96.0 | 672 | 0.5492 | 0.84 | 0.2438 | 1.0176 | 0.8400 | 0.8329 | 0.1582 | 0.0461 |
0.1783 | 97.0 | 679 | 0.5492 | 0.84 | 0.2438 | 1.0175 | 0.8400 | 0.8329 | 0.1581 | 0.0460 |
0.1783 | 98.0 | 686 | 0.5491 | 0.84 | 0.2438 | 1.0175 | 0.8400 | 0.8329 | 0.1582 | 0.0461 |
0.1783 | 99.0 | 693 | 0.5491 | 0.84 | 0.2438 | 1.0175 | 0.8400 | 0.8329 | 0.1580 | 0.0460 |
0.1783 | 100.0 | 700 | 0.5492 | 0.84 | 0.2438 | 1.0175 | 0.8400 | 0.8329 | 0.1581 | 0.0460 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2