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cdip-tiny_rvl_cdip-NK1000_kd_test
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: 0.4588
- Accuracy: 0.8227
- Brier Loss: 0.2552
- Nll: 1.9117
- F1 Micro: 0.8227
- F1 Macro: 0.8239
- Ece: 0.0507
- Aurc: 0.0420
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: 50
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
---|---|---|---|---|---|---|---|---|---|---|
No log | 1.0 | 125 | 1.3371 | 0.5423 | 0.5838 | 2.6217 | 0.5423 | 0.5341 | 0.0583 | 0.2219 |
No log | 2.0 | 250 | 0.9983 | 0.646 | 0.4725 | 2.3143 | 0.646 | 0.6407 | 0.0490 | 0.1432 |
No log | 3.0 | 375 | 0.8094 | 0.7085 | 0.3977 | 2.2571 | 0.7085 | 0.7034 | 0.0500 | 0.1017 |
1.2477 | 4.0 | 500 | 0.7633 | 0.7215 | 0.3806 | 2.2013 | 0.7215 | 0.7275 | 0.0447 | 0.0926 |
1.2477 | 5.0 | 625 | 0.7295 | 0.7505 | 0.3565 | 2.1741 | 0.7505 | 0.7417 | 0.0631 | 0.0775 |
1.2477 | 6.0 | 750 | 0.6706 | 0.7692 | 0.3321 | 2.1869 | 0.7692 | 0.7669 | 0.0614 | 0.0690 |
1.2477 | 7.0 | 875 | 0.6933 | 0.767 | 0.3344 | 2.1685 | 0.767 | 0.7650 | 0.0811 | 0.0676 |
0.3434 | 8.0 | 1000 | 0.6640 | 0.7778 | 0.3251 | 2.1666 | 0.7778 | 0.7795 | 0.0681 | 0.0650 |
0.3434 | 9.0 | 1125 | 0.6874 | 0.774 | 0.3328 | 2.1401 | 0.774 | 0.7739 | 0.0863 | 0.0660 |
0.3434 | 10.0 | 1250 | 0.6639 | 0.7795 | 0.3194 | 2.1335 | 0.7795 | 0.7800 | 0.0800 | 0.0618 |
0.3434 | 11.0 | 1375 | 0.6827 | 0.7728 | 0.3332 | 2.1140 | 0.7728 | 0.7771 | 0.0891 | 0.0650 |
0.1507 | 12.0 | 1500 | 0.6197 | 0.786 | 0.3106 | 2.1052 | 0.786 | 0.7873 | 0.0716 | 0.0586 |
0.1507 | 13.0 | 1625 | 0.6264 | 0.7823 | 0.3133 | 2.1077 | 0.7823 | 0.7834 | 0.0784 | 0.0595 |
0.1507 | 14.0 | 1750 | 0.5822 | 0.796 | 0.2964 | 2.0567 | 0.796 | 0.7983 | 0.0676 | 0.0549 |
0.1507 | 15.0 | 1875 | 0.5900 | 0.7923 | 0.3016 | 2.0704 | 0.7923 | 0.7936 | 0.0724 | 0.0541 |
0.107 | 16.0 | 2000 | 0.6044 | 0.7855 | 0.3099 | 2.0625 | 0.7855 | 0.7901 | 0.0730 | 0.0617 |
0.107 | 17.0 | 2125 | 0.5692 | 0.7973 | 0.2930 | 2.0627 | 0.7973 | 0.7990 | 0.0676 | 0.0528 |
0.107 | 18.0 | 2250 | 0.5836 | 0.7907 | 0.2984 | 2.0575 | 0.7907 | 0.7922 | 0.0749 | 0.0554 |
0.107 | 19.0 | 2375 | 0.5469 | 0.806 | 0.2835 | 2.0754 | 0.806 | 0.8060 | 0.0576 | 0.0498 |
0.0879 | 20.0 | 2500 | 0.5427 | 0.804 | 0.2892 | 2.0655 | 0.804 | 0.8089 | 0.0593 | 0.0528 |
0.0879 | 21.0 | 2625 | 0.5305 | 0.806 | 0.2777 | 2.0213 | 0.806 | 0.8070 | 0.0604 | 0.0495 |
0.0879 | 22.0 | 2750 | 0.5146 | 0.8113 | 0.2741 | 2.0127 | 0.8113 | 0.8121 | 0.0534 | 0.0480 |
0.0879 | 23.0 | 2875 | 0.5196 | 0.8107 | 0.2750 | 2.0261 | 0.8108 | 0.8117 | 0.0541 | 0.0489 |
0.0755 | 24.0 | 3000 | 0.5169 | 0.8123 | 0.2743 | 1.9561 | 0.8123 | 0.8127 | 0.0595 | 0.0478 |
0.0755 | 25.0 | 3125 | 0.5129 | 0.8073 | 0.2777 | 2.0020 | 0.8073 | 0.8089 | 0.0552 | 0.0491 |
0.0755 | 26.0 | 3250 | 0.4898 | 0.8177 | 0.2649 | 1.9710 | 0.8178 | 0.8177 | 0.0474 | 0.0451 |
0.0755 | 27.0 | 3375 | 0.4966 | 0.8155 | 0.2682 | 2.0075 | 0.8155 | 0.8163 | 0.0514 | 0.0458 |
0.0652 | 28.0 | 3500 | 0.4883 | 0.813 | 0.2690 | 1.9655 | 0.813 | 0.8141 | 0.0557 | 0.0465 |
0.0652 | 29.0 | 3625 | 0.4860 | 0.8185 | 0.2659 | 1.9593 | 0.8185 | 0.8194 | 0.0481 | 0.0456 |
0.0652 | 30.0 | 3750 | 0.4760 | 0.818 | 0.2600 | 1.9517 | 0.818 | 0.8194 | 0.0505 | 0.0441 |
0.0652 | 31.0 | 3875 | 0.4755 | 0.8195 | 0.2611 | 1.9593 | 0.8195 | 0.8196 | 0.0507 | 0.0440 |
0.0568 | 32.0 | 4000 | 0.4763 | 0.8155 | 0.2628 | 1.9508 | 0.8155 | 0.8161 | 0.0484 | 0.0451 |
0.0568 | 33.0 | 4125 | 0.4675 | 0.8225 | 0.2574 | 1.9474 | 0.8225 | 0.8238 | 0.0477 | 0.0433 |
0.0568 | 34.0 | 4250 | 0.4664 | 0.8207 | 0.2579 | 1.9478 | 0.8207 | 0.8220 | 0.0498 | 0.0431 |
0.0568 | 35.0 | 4375 | 0.4635 | 0.8213 | 0.2567 | 1.9233 | 0.8213 | 0.8219 | 0.0481 | 0.0427 |
0.0514 | 36.0 | 4500 | 0.4584 | 0.8245 | 0.2551 | 1.9196 | 0.8245 | 0.8260 | 0.0461 | 0.0424 |
0.0514 | 37.0 | 4625 | 0.4627 | 0.825 | 0.2557 | 1.9274 | 0.825 | 0.8256 | 0.0454 | 0.0424 |
0.0514 | 38.0 | 4750 | 0.4603 | 0.8213 | 0.2552 | 1.9319 | 0.8213 | 0.8221 | 0.0478 | 0.0425 |
0.0514 | 39.0 | 4875 | 0.4610 | 0.8245 | 0.2560 | 1.9337 | 0.8245 | 0.8252 | 0.0476 | 0.0424 |
0.0483 | 40.0 | 5000 | 0.4603 | 0.825 | 0.2559 | 1.9319 | 0.825 | 0.8262 | 0.0460 | 0.0421 |
0.0483 | 41.0 | 5125 | 0.4589 | 0.8253 | 0.2545 | 1.9317 | 0.8253 | 0.8260 | 0.0459 | 0.0421 |
0.0483 | 42.0 | 5250 | 0.4586 | 0.8245 | 0.2552 | 1.9192 | 0.8245 | 0.8260 | 0.0524 | 0.0420 |
0.0483 | 43.0 | 5375 | 0.4581 | 0.825 | 0.2552 | 1.9179 | 0.825 | 0.8263 | 0.0477 | 0.0421 |
0.0465 | 44.0 | 5500 | 0.4573 | 0.8245 | 0.2543 | 1.9187 | 0.8245 | 0.8257 | 0.0457 | 0.0417 |
0.0465 | 45.0 | 5625 | 0.4589 | 0.8225 | 0.2554 | 1.9184 | 0.8225 | 0.8235 | 0.0549 | 0.0421 |
0.0465 | 46.0 | 5750 | 0.4582 | 0.823 | 0.2547 | 1.9128 | 0.823 | 0.8242 | 0.0512 | 0.0420 |
0.0465 | 47.0 | 5875 | 0.4587 | 0.823 | 0.2551 | 1.9135 | 0.823 | 0.8241 | 0.0484 | 0.0420 |
0.0458 | 48.0 | 6000 | 0.4585 | 0.8235 | 0.2550 | 1.9127 | 0.8235 | 0.8246 | 0.0479 | 0.0420 |
0.0458 | 49.0 | 6125 | 0.4589 | 0.8227 | 0.2553 | 1.9117 | 0.8227 | 0.8238 | 0.0490 | 0.0421 |
0.0458 | 50.0 | 6250 | 0.4588 | 0.8227 | 0.2552 | 1.9117 | 0.8227 | 0.8239 | 0.0507 | 0.0420 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2