File size: 13,993 Bytes
e902a5d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 6_e_200-tiny_tobacco3482_kd_CEKD_t5.0_a0.9
results: []
---
<!-- 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. -->
# 6_e_200-tiny_tobacco3482_kd_CEKD_t5.0_a0.9
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5583
- Accuracy: 0.82
- Brier Loss: 0.2563
- Nll: 1.8898
- F1 Micro: 0.82
- F1 Macro: 0.8009
- Ece: 0.1578
- Aurc: 0.0530
## 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: 32
- eval_batch_size: 32
- 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 | 25 | 1.9764 | 0.23 | 0.8621 | 4.6756 | 0.23 | 0.1902 | 0.2733 | 0.7604 |
| No log | 2.0 | 50 | 1.2764 | 0.535 | 0.5973 | 2.7212 | 0.535 | 0.4337 | 0.2769 | 0.2592 |
| No log | 3.0 | 75 | 0.9774 | 0.68 | 0.4478 | 2.1874 | 0.68 | 0.5915 | 0.2144 | 0.1334 |
| No log | 4.0 | 100 | 0.8047 | 0.755 | 0.3617 | 1.4629 | 0.755 | 0.7257 | 0.1850 | 0.0888 |
| No log | 5.0 | 125 | 0.7616 | 0.765 | 0.3363 | 1.4885 | 0.765 | 0.7391 | 0.2017 | 0.0843 |
| No log | 6.0 | 150 | 1.0029 | 0.72 | 0.4200 | 1.6550 | 0.72 | 0.7047 | 0.2303 | 0.1169 |
| No log | 7.0 | 175 | 0.6286 | 0.825 | 0.2766 | 1.2493 | 0.825 | 0.7930 | 0.1954 | 0.0646 |
| No log | 8.0 | 200 | 0.6859 | 0.82 | 0.2857 | 1.4847 | 0.82 | 0.7971 | 0.1837 | 0.0699 |
| No log | 9.0 | 225 | 0.6365 | 0.81 | 0.2765 | 1.1457 | 0.81 | 0.7913 | 0.1604 | 0.0669 |
| No log | 10.0 | 250 | 0.6085 | 0.81 | 0.2614 | 1.5809 | 0.81 | 0.7928 | 0.1874 | 0.0536 |
| No log | 11.0 | 275 | 0.5900 | 0.84 | 0.2620 | 1.1457 | 0.8400 | 0.8308 | 0.1695 | 0.0674 |
| No log | 12.0 | 300 | 0.8544 | 0.75 | 0.3667 | 1.9577 | 0.75 | 0.7330 | 0.1988 | 0.1329 |
| No log | 13.0 | 325 | 0.5265 | 0.845 | 0.2278 | 1.2521 | 0.845 | 0.8209 | 0.1518 | 0.0399 |
| No log | 14.0 | 350 | 0.5702 | 0.815 | 0.2567 | 1.5233 | 0.815 | 0.8032 | 0.1551 | 0.0519 |
| No log | 15.0 | 375 | 0.5933 | 0.845 | 0.2581 | 1.4776 | 0.845 | 0.8341 | 0.1659 | 0.0738 |
| No log | 16.0 | 400 | 0.5697 | 0.84 | 0.2496 | 1.6732 | 0.8400 | 0.8235 | 0.1470 | 0.0557 |
| No log | 17.0 | 425 | 0.5471 | 0.825 | 0.2428 | 1.7010 | 0.825 | 0.8093 | 0.1406 | 0.0461 |
| No log | 18.0 | 450 | 0.5696 | 0.825 | 0.2546 | 1.4095 | 0.825 | 0.7977 | 0.1461 | 0.0612 |
| No log | 19.0 | 475 | 0.6544 | 0.805 | 0.2959 | 1.8251 | 0.805 | 0.7970 | 0.1681 | 0.0605 |
| 0.4416 | 20.0 | 500 | 0.5113 | 0.83 | 0.2327 | 1.4103 | 0.83 | 0.8093 | 0.1380 | 0.0541 |
| 0.4416 | 21.0 | 525 | 0.5255 | 0.84 | 0.2375 | 1.6750 | 0.8400 | 0.8220 | 0.1320 | 0.0462 |
| 0.4416 | 22.0 | 550 | 0.5889 | 0.835 | 0.2681 | 1.7850 | 0.835 | 0.8242 | 0.1507 | 0.0683 |
| 0.4416 | 23.0 | 575 | 0.5456 | 0.835 | 0.2492 | 1.8481 | 0.835 | 0.8137 | 0.1716 | 0.0550 |
| 0.4416 | 24.0 | 600 | 0.5661 | 0.83 | 0.2611 | 1.8434 | 0.83 | 0.8156 | 0.1618 | 0.0591 |
| 0.4416 | 25.0 | 625 | 0.5444 | 0.83 | 0.2484 | 1.7579 | 0.83 | 0.8091 | 0.1478 | 0.0530 |
| 0.4416 | 26.0 | 650 | 0.5418 | 0.83 | 0.2503 | 1.7188 | 0.83 | 0.8125 | 0.1564 | 0.0484 |
| 0.4416 | 27.0 | 675 | 0.5532 | 0.835 | 0.2540 | 1.8931 | 0.835 | 0.8146 | 0.1694 | 0.0514 |
| 0.4416 | 28.0 | 700 | 0.5492 | 0.835 | 0.2518 | 1.8959 | 0.835 | 0.8155 | 0.1505 | 0.0495 |
| 0.4416 | 29.0 | 725 | 0.5478 | 0.825 | 0.2505 | 1.8907 | 0.825 | 0.8069 | 0.1548 | 0.0503 |
| 0.4416 | 30.0 | 750 | 0.5478 | 0.835 | 0.2510 | 1.8881 | 0.835 | 0.8178 | 0.1467 | 0.0521 |
| 0.4416 | 31.0 | 775 | 0.5472 | 0.825 | 0.2505 | 1.8888 | 0.825 | 0.8064 | 0.1527 | 0.0510 |
| 0.4416 | 32.0 | 800 | 0.5522 | 0.83 | 0.2527 | 1.8927 | 0.83 | 0.8126 | 0.1449 | 0.0520 |
| 0.4416 | 33.0 | 825 | 0.5513 | 0.825 | 0.2524 | 1.8989 | 0.825 | 0.8064 | 0.1625 | 0.0509 |
| 0.4416 | 34.0 | 850 | 0.5465 | 0.835 | 0.2504 | 1.8880 | 0.835 | 0.8148 | 0.1519 | 0.0520 |
| 0.4416 | 35.0 | 875 | 0.5489 | 0.825 | 0.2515 | 1.8866 | 0.825 | 0.8064 | 0.1538 | 0.0510 |
| 0.4416 | 36.0 | 900 | 0.5508 | 0.825 | 0.2521 | 1.8922 | 0.825 | 0.8053 | 0.1356 | 0.0526 |
| 0.4416 | 37.0 | 925 | 0.5495 | 0.825 | 0.2522 | 1.8881 | 0.825 | 0.8064 | 0.1517 | 0.0514 |
| 0.4416 | 38.0 | 950 | 0.5483 | 0.825 | 0.2514 | 1.8859 | 0.825 | 0.8064 | 0.1749 | 0.0511 |
| 0.4416 | 39.0 | 975 | 0.5508 | 0.825 | 0.2524 | 1.8868 | 0.825 | 0.8064 | 0.1459 | 0.0514 |
| 0.0519 | 40.0 | 1000 | 0.5519 | 0.825 | 0.2529 | 1.8862 | 0.825 | 0.8064 | 0.1532 | 0.0513 |
| 0.0519 | 41.0 | 1025 | 0.5522 | 0.825 | 0.2530 | 1.8882 | 0.825 | 0.8064 | 0.1665 | 0.0519 |
| 0.0519 | 42.0 | 1050 | 0.5507 | 0.825 | 0.2525 | 1.8870 | 0.825 | 0.8064 | 0.1613 | 0.0508 |
| 0.0519 | 43.0 | 1075 | 0.5528 | 0.825 | 0.2536 | 1.8884 | 0.825 | 0.8064 | 0.1634 | 0.0517 |
| 0.0519 | 44.0 | 1100 | 0.5520 | 0.825 | 0.2531 | 1.8879 | 0.825 | 0.8064 | 0.1519 | 0.0525 |
| 0.0519 | 45.0 | 1125 | 0.5524 | 0.825 | 0.2535 | 1.8876 | 0.825 | 0.8053 | 0.1582 | 0.0515 |
| 0.0519 | 46.0 | 1150 | 0.5525 | 0.825 | 0.2534 | 1.8867 | 0.825 | 0.8064 | 0.1592 | 0.0519 |
| 0.0519 | 47.0 | 1175 | 0.5532 | 0.825 | 0.2539 | 1.8875 | 0.825 | 0.8064 | 0.1621 | 0.0521 |
| 0.0519 | 48.0 | 1200 | 0.5540 | 0.825 | 0.2540 | 1.8865 | 0.825 | 0.8064 | 0.1502 | 0.0522 |
| 0.0519 | 49.0 | 1225 | 0.5523 | 0.825 | 0.2538 | 1.8268 | 0.825 | 0.8072 | 0.1625 | 0.0514 |
| 0.0519 | 50.0 | 1250 | 0.5535 | 0.825 | 0.2539 | 1.8871 | 0.825 | 0.8064 | 0.1684 | 0.0517 |
| 0.0519 | 51.0 | 1275 | 0.5526 | 0.825 | 0.2534 | 1.8850 | 0.825 | 0.8064 | 0.1621 | 0.0519 |
| 0.0519 | 52.0 | 1300 | 0.5543 | 0.825 | 0.2543 | 1.8865 | 0.825 | 0.8064 | 0.1429 | 0.0521 |
| 0.0519 | 53.0 | 1325 | 0.5526 | 0.825 | 0.2538 | 1.8866 | 0.825 | 0.8064 | 0.1613 | 0.0515 |
| 0.0519 | 54.0 | 1350 | 0.5530 | 0.82 | 0.2538 | 1.8877 | 0.82 | 0.8009 | 0.1620 | 0.0518 |
| 0.0519 | 55.0 | 1375 | 0.5550 | 0.825 | 0.2547 | 1.8872 | 0.825 | 0.8064 | 0.1567 | 0.0522 |
| 0.0519 | 56.0 | 1400 | 0.5565 | 0.825 | 0.2552 | 1.8859 | 0.825 | 0.8064 | 0.1400 | 0.0523 |
| 0.0519 | 57.0 | 1425 | 0.5552 | 0.825 | 0.2548 | 1.8874 | 0.825 | 0.8064 | 0.1543 | 0.0520 |
| 0.0519 | 58.0 | 1450 | 0.5537 | 0.825 | 0.2542 | 1.8860 | 0.825 | 0.8064 | 0.1531 | 0.0516 |
| 0.0519 | 59.0 | 1475 | 0.5559 | 0.825 | 0.2551 | 1.8879 | 0.825 | 0.8064 | 0.1564 | 0.0525 |
| 0.0508 | 60.0 | 1500 | 0.5548 | 0.825 | 0.2545 | 1.8866 | 0.825 | 0.8064 | 0.1526 | 0.0522 |
| 0.0508 | 61.0 | 1525 | 0.5557 | 0.825 | 0.2550 | 1.8884 | 0.825 | 0.8064 | 0.1443 | 0.0524 |
| 0.0508 | 62.0 | 1550 | 0.5548 | 0.82 | 0.2546 | 1.8874 | 0.82 | 0.8009 | 0.1709 | 0.0527 |
| 0.0508 | 63.0 | 1575 | 0.5556 | 0.825 | 0.2551 | 1.8899 | 0.825 | 0.8064 | 0.1606 | 0.0524 |
| 0.0508 | 64.0 | 1600 | 0.5562 | 0.825 | 0.2553 | 1.8872 | 0.825 | 0.8064 | 0.1467 | 0.0527 |
| 0.0508 | 65.0 | 1625 | 0.5569 | 0.825 | 0.2554 | 1.8879 | 0.825 | 0.8064 | 0.1537 | 0.0524 |
| 0.0508 | 66.0 | 1650 | 0.5567 | 0.825 | 0.2555 | 1.8873 | 0.825 | 0.8064 | 0.1601 | 0.0525 |
| 0.0508 | 67.0 | 1675 | 0.5556 | 0.825 | 0.2550 | 1.8878 | 0.825 | 0.8064 | 0.1601 | 0.0527 |
| 0.0508 | 68.0 | 1700 | 0.5570 | 0.825 | 0.2555 | 1.8879 | 0.825 | 0.8064 | 0.1679 | 0.0528 |
| 0.0508 | 69.0 | 1725 | 0.5560 | 0.825 | 0.2553 | 1.8886 | 0.825 | 0.8064 | 0.1525 | 0.0521 |
| 0.0508 | 70.0 | 1750 | 0.5562 | 0.825 | 0.2553 | 1.8878 | 0.825 | 0.8064 | 0.1531 | 0.0528 |
| 0.0508 | 71.0 | 1775 | 0.5572 | 0.82 | 0.2557 | 1.8883 | 0.82 | 0.8009 | 0.1718 | 0.0530 |
| 0.0508 | 72.0 | 1800 | 0.5567 | 0.82 | 0.2555 | 1.8888 | 0.82 | 0.8009 | 0.1630 | 0.0525 |
| 0.0508 | 73.0 | 1825 | 0.5571 | 0.825 | 0.2556 | 1.8882 | 0.825 | 0.8064 | 0.1598 | 0.0528 |
| 0.0508 | 74.0 | 1850 | 0.5580 | 0.825 | 0.2561 | 1.8901 | 0.825 | 0.8064 | 0.1543 | 0.0530 |
| 0.0508 | 75.0 | 1875 | 0.5579 | 0.82 | 0.2561 | 1.8892 | 0.82 | 0.8009 | 0.1721 | 0.0530 |
| 0.0508 | 76.0 | 1900 | 0.5574 | 0.82 | 0.2559 | 1.8892 | 0.82 | 0.8009 | 0.1636 | 0.0528 |
| 0.0508 | 77.0 | 1925 | 0.5569 | 0.82 | 0.2557 | 1.8393 | 0.82 | 0.8009 | 0.1634 | 0.0526 |
| 0.0508 | 78.0 | 1950 | 0.5572 | 0.82 | 0.2558 | 1.8887 | 0.82 | 0.8009 | 0.1637 | 0.0530 |
| 0.0508 | 79.0 | 1975 | 0.5578 | 0.82 | 0.2560 | 1.8888 | 0.82 | 0.8009 | 0.1579 | 0.0530 |
| 0.0507 | 80.0 | 2000 | 0.5577 | 0.82 | 0.2559 | 1.8889 | 0.82 | 0.8009 | 0.1578 | 0.0532 |
| 0.0507 | 81.0 | 2025 | 0.5578 | 0.82 | 0.2560 | 1.8889 | 0.82 | 0.8009 | 0.1578 | 0.0533 |
| 0.0507 | 82.0 | 2050 | 0.5579 | 0.825 | 0.2561 | 1.8891 | 0.825 | 0.8064 | 0.1602 | 0.0528 |
| 0.0507 | 83.0 | 2075 | 0.5581 | 0.825 | 0.2562 | 1.8894 | 0.825 | 0.8064 | 0.1544 | 0.0528 |
| 0.0507 | 84.0 | 2100 | 0.5579 | 0.82 | 0.2561 | 1.8894 | 0.82 | 0.8009 | 0.1581 | 0.0531 |
| 0.0507 | 85.0 | 2125 | 0.5580 | 0.82 | 0.2561 | 1.8896 | 0.82 | 0.8009 | 0.1578 | 0.0528 |
| 0.0507 | 86.0 | 2150 | 0.5581 | 0.82 | 0.2562 | 1.8891 | 0.82 | 0.8009 | 0.1580 | 0.0532 |
| 0.0507 | 87.0 | 2175 | 0.5582 | 0.82 | 0.2562 | 1.8467 | 0.82 | 0.8009 | 0.1581 | 0.0528 |
| 0.0507 | 88.0 | 2200 | 0.5583 | 0.82 | 0.2562 | 1.8891 | 0.82 | 0.8009 | 0.1580 | 0.0531 |
| 0.0507 | 89.0 | 2225 | 0.5584 | 0.815 | 0.2563 | 1.8894 | 0.815 | 0.7976 | 0.1608 | 0.0534 |
| 0.0507 | 90.0 | 2250 | 0.5578 | 0.82 | 0.2561 | 1.8894 | 0.82 | 0.8009 | 0.1578 | 0.0530 |
| 0.0507 | 91.0 | 2275 | 0.5584 | 0.815 | 0.2563 | 1.8896 | 0.815 | 0.7976 | 0.1607 | 0.0532 |
| 0.0507 | 92.0 | 2300 | 0.5583 | 0.82 | 0.2562 | 1.8893 | 0.82 | 0.8009 | 0.1581 | 0.0531 |
| 0.0507 | 93.0 | 2325 | 0.5582 | 0.82 | 0.2562 | 1.8898 | 0.82 | 0.8009 | 0.1579 | 0.0530 |
| 0.0507 | 94.0 | 2350 | 0.5582 | 0.82 | 0.2562 | 1.8392 | 0.82 | 0.8009 | 0.1578 | 0.0530 |
| 0.0507 | 95.0 | 2375 | 0.5584 | 0.82 | 0.2563 | 1.8897 | 0.82 | 0.8009 | 0.1582 | 0.0531 |
| 0.0507 | 96.0 | 2400 | 0.5582 | 0.82 | 0.2562 | 1.8898 | 0.82 | 0.8009 | 0.1578 | 0.0530 |
| 0.0507 | 97.0 | 2425 | 0.5583 | 0.82 | 0.2563 | 1.8896 | 0.82 | 0.8009 | 0.1580 | 0.0530 |
| 0.0507 | 98.0 | 2450 | 0.5582 | 0.82 | 0.2562 | 1.8898 | 0.82 | 0.8009 | 0.1578 | 0.0530 |
| 0.0507 | 99.0 | 2475 | 0.5583 | 0.82 | 0.2563 | 1.8898 | 0.82 | 0.8009 | 0.1578 | 0.0530 |
| 0.0507 | 100.0 | 2500 | 0.5583 | 0.82 | 0.2563 | 1.8898 | 0.82 | 0.8009 | 0.1578 | 0.0530 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1
- Datasets 2.13.1
- Tokenizers 0.13.3
|