<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
cdip-small_rvl_cdip-NK1000_kd_test
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: 0.3813
- Accuracy: 0.8558
- Brier Loss: 0.2176
- Nll: 1.4251
- F1 Micro: 0.8558
- F1 Macro: 0.8566
- Ece: 0.0597
- Aurc: 0.0299
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: 24
- eval_batch_size: 24
- 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: 50
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
---|---|---|---|---|---|---|---|---|---|---|
1.7536 | 1.0 | 667 | 0.9652 | 0.6695 | 0.4474 | 2.2965 | 0.6695 | 0.6595 | 0.0494 | 0.1257 |
0.8802 | 2.0 | 1334 | 0.7683 | 0.7195 | 0.3806 | 2.0303 | 0.7195 | 0.7116 | 0.0473 | 0.0920 |
0.5767 | 3.0 | 2001 | 0.6276 | 0.7698 | 0.3253 | 1.9446 | 0.7698 | 0.7711 | 0.0436 | 0.0684 |
0.4263 | 4.0 | 2668 | 0.6095 | 0.7785 | 0.3110 | 1.9810 | 0.7785 | 0.7810 | 0.0474 | 0.0624 |
0.3987 | 5.0 | 3335 | 0.5608 | 0.791 | 0.2939 | 1.8539 | 0.791 | 0.7918 | 0.0504 | 0.0557 |
0.3179 | 6.0 | 4002 | 0.6057 | 0.7935 | 0.3027 | 1.8778 | 0.7935 | 0.7940 | 0.0811 | 0.0548 |
0.2428 | 7.0 | 4669 | 0.5828 | 0.8043 | 0.2905 | 1.8616 | 0.8043 | 0.8050 | 0.0662 | 0.0520 |
0.2094 | 8.0 | 5336 | 0.5812 | 0.7957 | 0.2973 | 1.8459 | 0.7957 | 0.8019 | 0.0783 | 0.0532 |
0.1715 | 9.0 | 6003 | 0.6152 | 0.7987 | 0.2993 | 1.9533 | 0.7987 | 0.7998 | 0.0723 | 0.0539 |
0.1508 | 10.0 | 6670 | 0.5442 | 0.808 | 0.2820 | 1.8159 | 0.808 | 0.8097 | 0.0836 | 0.0476 |
0.1434 | 11.0 | 7337 | 0.4881 | 0.828 | 0.2549 | 1.6938 | 0.828 | 0.8286 | 0.0610 | 0.0410 |
0.1267 | 12.0 | 8004 | 0.4720 | 0.8365 | 0.2465 | 1.6878 | 0.8365 | 0.8360 | 0.0576 | 0.0400 |
0.115 | 13.0 | 8671 | 0.4648 | 0.8335 | 0.2482 | 1.6871 | 0.8335 | 0.8353 | 0.0630 | 0.0387 |
0.1112 | 14.0 | 9338 | 0.4777 | 0.8317 | 0.2509 | 1.6393 | 0.8317 | 0.8312 | 0.0614 | 0.0418 |
0.1002 | 15.0 | 10005 | 0.4684 | 0.8333 | 0.2484 | 1.6054 | 0.8333 | 0.8335 | 0.0657 | 0.0392 |
0.0944 | 16.0 | 10672 | 0.4693 | 0.8365 | 0.2480 | 1.6381 | 0.8365 | 0.8366 | 0.0658 | 0.0383 |
0.0934 | 17.0 | 11339 | 0.4534 | 0.8323 | 0.2465 | 1.6420 | 0.8323 | 0.8343 | 0.0561 | 0.0373 |
0.0835 | 18.0 | 12006 | 0.4512 | 0.8357 | 0.2435 | 1.6301 | 0.8357 | 0.8367 | 0.0575 | 0.0372 |
0.08 | 19.0 | 12673 | 0.4345 | 0.838 | 0.2394 | 1.6382 | 0.838 | 0.8398 | 0.0562 | 0.0366 |
0.0819 | 20.0 | 13340 | 0.4356 | 0.838 | 0.2374 | 1.5973 | 0.838 | 0.8384 | 0.0588 | 0.0364 |
0.0709 | 21.0 | 14007 | 0.4484 | 0.8415 | 0.2368 | 1.6231 | 0.8415 | 0.8411 | 0.0595 | 0.0368 |
0.0691 | 22.0 | 14674 | 0.4194 | 0.8495 | 0.2287 | 1.5968 | 0.8495 | 0.8505 | 0.0531 | 0.0335 |
0.068 | 23.0 | 15341 | 0.4308 | 0.8413 | 0.2346 | 1.5599 | 0.8413 | 0.8410 | 0.0542 | 0.0360 |
0.0641 | 24.0 | 16008 | 0.4209 | 0.8405 | 0.2336 | 1.5539 | 0.8405 | 0.8422 | 0.0590 | 0.0339 |
0.0617 | 25.0 | 16675 | 0.4181 | 0.841 | 0.2352 | 1.5735 | 0.841 | 0.8435 | 0.0568 | 0.0356 |
0.0633 | 26.0 | 17342 | 0.4193 | 0.8508 | 0.2286 | 1.5299 | 0.8508 | 0.8510 | 0.0650 | 0.0348 |
0.0569 | 27.0 | 18009 | 0.4065 | 0.8468 | 0.2278 | 1.5267 | 0.8468 | 0.8479 | 0.0546 | 0.0332 |
0.0571 | 28.0 | 18676 | 0.4109 | 0.8498 | 0.2255 | 1.5147 | 0.8498 | 0.8499 | 0.0590 | 0.0331 |
0.0543 | 29.0 | 19343 | 0.4026 | 0.8482 | 0.2250 | 1.5187 | 0.8482 | 0.8498 | 0.0623 | 0.0327 |
0.0543 | 30.0 | 20010 | 0.4124 | 0.847 | 0.2293 | 1.5125 | 0.847 | 0.8473 | 0.0605 | 0.0330 |
0.0536 | 31.0 | 20677 | 0.4022 | 0.851 | 0.2238 | 1.5100 | 0.851 | 0.8527 | 0.0594 | 0.0323 |
0.0522 | 32.0 | 21344 | 0.4120 | 0.8475 | 0.2290 | 1.5044 | 0.8475 | 0.8483 | 0.0633 | 0.0327 |
0.0493 | 33.0 | 22011 | 0.3990 | 0.8492 | 0.2258 | 1.5197 | 0.8492 | 0.8503 | 0.0589 | 0.0318 |
0.0512 | 34.0 | 22678 | 0.3983 | 0.85 | 0.2251 | 1.4644 | 0.85 | 0.8503 | 0.0597 | 0.0319 |
0.0517 | 35.0 | 23345 | 0.3969 | 0.8465 | 0.2257 | 1.4814 | 0.8465 | 0.8479 | 0.0630 | 0.0309 |
0.0477 | 36.0 | 24012 | 0.3939 | 0.8528 | 0.2237 | 1.4797 | 0.8528 | 0.8531 | 0.0604 | 0.0316 |
0.0482 | 37.0 | 24679 | 0.3934 | 0.852 | 0.2218 | 1.4595 | 0.852 | 0.8527 | 0.0613 | 0.0316 |
0.0481 | 38.0 | 25346 | 0.3930 | 0.8532 | 0.2217 | 1.4561 | 0.8532 | 0.8544 | 0.0593 | 0.0306 |
0.0477 | 39.0 | 26013 | 0.3875 | 0.8512 | 0.2202 | 1.4610 | 0.8512 | 0.8523 | 0.0609 | 0.0310 |
0.048 | 40.0 | 26680 | 0.3900 | 0.8538 | 0.2202 | 1.4541 | 0.8537 | 0.8546 | 0.0629 | 0.0307 |
0.0448 | 41.0 | 27347 | 0.3901 | 0.8525 | 0.2221 | 1.4519 | 0.8525 | 0.8532 | 0.0621 | 0.0308 |
0.0454 | 42.0 | 28014 | 0.3858 | 0.851 | 0.2186 | 1.4554 | 0.851 | 0.8519 | 0.0633 | 0.0298 |
0.0464 | 43.0 | 28681 | 0.3861 | 0.8528 | 0.2197 | 1.4516 | 0.8528 | 0.8535 | 0.0618 | 0.0307 |
0.0444 | 44.0 | 29348 | 0.3824 | 0.8548 | 0.2176 | 1.4288 | 0.8547 | 0.8557 | 0.0607 | 0.0299 |
0.0461 | 45.0 | 30015 | 0.3833 | 0.8555 | 0.2181 | 1.4330 | 0.8555 | 0.8566 | 0.0606 | 0.0302 |
0.0442 | 46.0 | 30682 | 0.3830 | 0.8552 | 0.2174 | 1.4358 | 0.8552 | 0.8560 | 0.0604 | 0.0302 |
0.0456 | 47.0 | 31349 | 0.3797 | 0.8552 | 0.2173 | 1.4264 | 0.8552 | 0.8560 | 0.0596 | 0.0297 |
0.0447 | 48.0 | 32016 | 0.3811 | 0.8558 | 0.2176 | 1.4273 | 0.8558 | 0.8566 | 0.0595 | 0.0300 |
0.0439 | 49.0 | 32683 | 0.3814 | 0.856 | 0.2176 | 1.4252 | 0.856 | 0.8568 | 0.0600 | 0.0300 |
0.0437 | 50.0 | 33350 | 0.3813 | 0.8558 | 0.2176 | 1.4251 | 0.8558 | 0.8566 | 0.0597 | 0.0299 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2