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vit-small_rvl_cdip_100_examples_per_class_kd_CE
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: 1.7493
- Accuracy: 0.6275
- Brier Loss: 0.5677
- Nll: 2.9769
- F1 Micro: 0.6275
- F1 Macro: 0.6250
- Ece: 0.2161
- Aurc: 0.1599
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 | 3.0120 | 0.085 | 0.9612 | 10.2074 | 0.085 | 0.0737 | 0.1585 | 0.8946 |
No log | 2.0 | 14 | 2.6380 | 0.12 | 0.9247 | 6.9313 | 0.12 | 0.1257 | 0.1753 | 0.8678 |
No log | 3.0 | 21 | 2.1951 | 0.36 | 0.7931 | 5.6390 | 0.36 | 0.3256 | 0.2066 | 0.4054 |
No log | 4.0 | 28 | 1.8405 | 0.445 | 0.6971 | 3.5387 | 0.445 | 0.4479 | 0.1889 | 0.3068 |
No log | 5.0 | 35 | 1.6213 | 0.525 | 0.6244 | 3.3423 | 0.525 | 0.5188 | 0.1821 | 0.2477 |
No log | 6.0 | 42 | 1.5983 | 0.5275 | 0.6177 | 3.1202 | 0.5275 | 0.5220 | 0.1781 | 0.2448 |
No log | 7.0 | 49 | 1.6214 | 0.54 | 0.6243 | 3.2514 | 0.54 | 0.5248 | 0.1758 | 0.2535 |
No log | 8.0 | 56 | 1.4964 | 0.5675 | 0.5862 | 2.6168 | 0.5675 | 0.5715 | 0.1585 | 0.2200 |
No log | 9.0 | 63 | 1.5696 | 0.575 | 0.5893 | 2.9901 | 0.575 | 0.5729 | 0.1851 | 0.2123 |
No log | 10.0 | 70 | 1.6620 | 0.54 | 0.6257 | 3.1275 | 0.54 | 0.5425 | 0.2353 | 0.2343 |
No log | 11.0 | 77 | 1.6901 | 0.585 | 0.5967 | 3.1708 | 0.585 | 0.5753 | 0.2006 | 0.1900 |
No log | 12.0 | 84 | 1.5686 | 0.61 | 0.5645 | 2.9975 | 0.61 | 0.6129 | 0.1904 | 0.1830 |
No log | 13.0 | 91 | 1.7390 | 0.5675 | 0.6159 | 3.0248 | 0.5675 | 0.5574 | 0.2200 | 0.2016 |
No log | 14.0 | 98 | 1.6423 | 0.59 | 0.5778 | 2.9212 | 0.59 | 0.5827 | 0.2015 | 0.1863 |
No log | 15.0 | 105 | 1.6262 | 0.61 | 0.5630 | 2.9492 | 0.61 | 0.6074 | 0.1950 | 0.1700 |
No log | 16.0 | 112 | 1.6987 | 0.5925 | 0.5791 | 3.0433 | 0.5925 | 0.5852 | 0.2123 | 0.1674 |
No log | 17.0 | 119 | 1.7256 | 0.5975 | 0.5782 | 3.0657 | 0.5975 | 0.5929 | 0.2214 | 0.1713 |
No log | 18.0 | 126 | 1.7127 | 0.6125 | 0.5697 | 2.9494 | 0.6125 | 0.6110 | 0.2044 | 0.1706 |
No log | 19.0 | 133 | 1.6961 | 0.62 | 0.5627 | 2.8745 | 0.62 | 0.6181 | 0.1972 | 0.1666 |
No log | 20.0 | 140 | 1.6784 | 0.6275 | 0.5565 | 2.9077 | 0.6275 | 0.6256 | 0.2005 | 0.1614 |
No log | 21.0 | 147 | 1.6699 | 0.62 | 0.5549 | 2.9148 | 0.62 | 0.6189 | 0.2089 | 0.1598 |
No log | 22.0 | 154 | 1.6705 | 0.62 | 0.5561 | 2.9207 | 0.62 | 0.6186 | 0.2036 | 0.1593 |
No log | 23.0 | 161 | 1.6749 | 0.62 | 0.5576 | 2.8938 | 0.62 | 0.6172 | 0.2017 | 0.1594 |
No log | 24.0 | 168 | 1.6811 | 0.62 | 0.5586 | 2.9303 | 0.62 | 0.6176 | 0.2064 | 0.1602 |
No log | 25.0 | 175 | 1.6870 | 0.625 | 0.5595 | 2.9457 | 0.625 | 0.6225 | 0.1996 | 0.1600 |
No log | 26.0 | 182 | 1.6905 | 0.625 | 0.5600 | 2.9438 | 0.625 | 0.6228 | 0.1957 | 0.1604 |
No log | 27.0 | 189 | 1.6920 | 0.625 | 0.5601 | 2.9207 | 0.625 | 0.6228 | 0.2030 | 0.1603 |
No log | 28.0 | 196 | 1.6928 | 0.6225 | 0.5596 | 2.9140 | 0.6225 | 0.6201 | 0.2104 | 0.1598 |
No log | 29.0 | 203 | 1.6934 | 0.6225 | 0.5596 | 2.9133 | 0.6225 | 0.6201 | 0.2171 | 0.1597 |
No log | 30.0 | 210 | 1.6952 | 0.6225 | 0.5600 | 2.9156 | 0.6225 | 0.6199 | 0.2175 | 0.1597 |
No log | 31.0 | 217 | 1.6962 | 0.6225 | 0.5604 | 2.9195 | 0.6225 | 0.6199 | 0.2151 | 0.1597 |
No log | 32.0 | 224 | 1.6982 | 0.625 | 0.5609 | 2.9466 | 0.625 | 0.6216 | 0.2052 | 0.1598 |
No log | 33.0 | 231 | 1.6996 | 0.625 | 0.5610 | 2.9468 | 0.625 | 0.6220 | 0.2073 | 0.1598 |
No log | 34.0 | 238 | 1.7008 | 0.625 | 0.5611 | 2.9223 | 0.625 | 0.6220 | 0.2099 | 0.1595 |
No log | 35.0 | 245 | 1.7028 | 0.625 | 0.5615 | 2.9159 | 0.625 | 0.6218 | 0.2062 | 0.1597 |
No log | 36.0 | 252 | 1.7053 | 0.6275 | 0.5621 | 2.9154 | 0.6275 | 0.6246 | 0.2166 | 0.1598 |
No log | 37.0 | 259 | 1.7078 | 0.625 | 0.5628 | 2.9132 | 0.625 | 0.6216 | 0.2113 | 0.1600 |
No log | 38.0 | 266 | 1.7098 | 0.6275 | 0.5631 | 2.9119 | 0.6275 | 0.6243 | 0.2209 | 0.1601 |
No log | 39.0 | 273 | 1.7112 | 0.625 | 0.5632 | 2.9136 | 0.625 | 0.6221 | 0.2164 | 0.1604 |
No log | 40.0 | 280 | 1.7122 | 0.625 | 0.5633 | 2.9183 | 0.625 | 0.6221 | 0.2206 | 0.1603 |
No log | 41.0 | 287 | 1.7134 | 0.6275 | 0.5635 | 2.9473 | 0.6275 | 0.6247 | 0.2192 | 0.1602 |
No log | 42.0 | 294 | 1.7142 | 0.625 | 0.5636 | 2.9477 | 0.625 | 0.6220 | 0.2172 | 0.1600 |
No log | 43.0 | 301 | 1.7152 | 0.6275 | 0.5634 | 2.9471 | 0.6275 | 0.6245 | 0.2090 | 0.1598 |
No log | 44.0 | 308 | 1.7160 | 0.6275 | 0.5634 | 2.9175 | 0.6275 | 0.6245 | 0.2074 | 0.1597 |
No log | 45.0 | 315 | 1.7172 | 0.6275 | 0.5637 | 2.9171 | 0.6275 | 0.6245 | 0.2138 | 0.1597 |
No log | 46.0 | 322 | 1.7188 | 0.63 | 0.5640 | 2.9184 | 0.63 | 0.6272 | 0.2138 | 0.1597 |
No log | 47.0 | 329 | 1.7204 | 0.63 | 0.5642 | 2.9171 | 0.63 | 0.6277 | 0.2146 | 0.1599 |
No log | 48.0 | 336 | 1.7220 | 0.63 | 0.5643 | 2.9178 | 0.63 | 0.6277 | 0.2088 | 0.1599 |
No log | 49.0 | 343 | 1.7233 | 0.6325 | 0.5643 | 2.9162 | 0.6325 | 0.6296 | 0.2114 | 0.1597 |
No log | 50.0 | 350 | 1.7244 | 0.6325 | 0.5644 | 2.9149 | 0.6325 | 0.6296 | 0.2117 | 0.1598 |
No log | 51.0 | 357 | 1.7253 | 0.6325 | 0.5645 | 2.9168 | 0.6325 | 0.6296 | 0.2078 | 0.1597 |
No log | 52.0 | 364 | 1.7260 | 0.63 | 0.5647 | 2.9198 | 0.63 | 0.6271 | 0.2002 | 0.1598 |
No log | 53.0 | 371 | 1.7268 | 0.63 | 0.5649 | 2.9230 | 0.63 | 0.6270 | 0.2068 | 0.1596 |
No log | 54.0 | 378 | 1.7271 | 0.6275 | 0.5649 | 2.9547 | 0.6275 | 0.6241 | 0.2019 | 0.1599 |
No log | 55.0 | 385 | 1.7281 | 0.6275 | 0.5652 | 2.9814 | 0.6275 | 0.6241 | 0.2084 | 0.1599 |
No log | 56.0 | 392 | 1.7293 | 0.6275 | 0.5652 | 2.9522 | 0.6275 | 0.6241 | 0.2086 | 0.1599 |
No log | 57.0 | 399 | 1.7306 | 0.6275 | 0.5653 | 2.9227 | 0.6275 | 0.6244 | 0.2160 | 0.1600 |
No log | 58.0 | 406 | 1.7315 | 0.6275 | 0.5654 | 2.9203 | 0.6275 | 0.6244 | 0.2140 | 0.1598 |
No log | 59.0 | 413 | 1.7322 | 0.6275 | 0.5655 | 2.9190 | 0.6275 | 0.6244 | 0.2229 | 0.1600 |
No log | 60.0 | 420 | 1.7333 | 0.6275 | 0.5657 | 2.9184 | 0.6275 | 0.6250 | 0.2150 | 0.1600 |
No log | 61.0 | 427 | 1.7343 | 0.63 | 0.5658 | 2.9166 | 0.63 | 0.6273 | 0.2304 | 0.1599 |
No log | 62.0 | 434 | 1.7351 | 0.63 | 0.5660 | 2.9230 | 0.63 | 0.6275 | 0.2154 | 0.1598 |
No log | 63.0 | 441 | 1.7354 | 0.63 | 0.5660 | 2.9476 | 0.63 | 0.6275 | 0.2056 | 0.1597 |
No log | 64.0 | 448 | 1.7359 | 0.63 | 0.5661 | 2.9483 | 0.63 | 0.6275 | 0.2050 | 0.1598 |
No log | 65.0 | 455 | 1.7366 | 0.6275 | 0.5661 | 2.9515 | 0.6275 | 0.6250 | 0.2053 | 0.1600 |
No log | 66.0 | 462 | 1.7371 | 0.6275 | 0.5661 | 2.9588 | 0.6275 | 0.6250 | 0.2110 | 0.1600 |
No log | 67.0 | 469 | 1.7378 | 0.6275 | 0.5663 | 2.9780 | 0.6275 | 0.6250 | 0.2108 | 0.1599 |
No log | 68.0 | 476 | 1.7384 | 0.6275 | 0.5663 | 2.9530 | 0.6275 | 0.6250 | 0.2150 | 0.1599 |
No log | 69.0 | 483 | 1.7392 | 0.63 | 0.5663 | 2.9631 | 0.63 | 0.6275 | 0.2114 | 0.1596 |
No log | 70.0 | 490 | 1.7398 | 0.63 | 0.5663 | 2.9778 | 0.63 | 0.6275 | 0.2129 | 0.1596 |
No log | 71.0 | 497 | 1.7402 | 0.63 | 0.5664 | 2.9544 | 0.63 | 0.6275 | 0.2227 | 0.1596 |
0.1799 | 72.0 | 504 | 1.7408 | 0.63 | 0.5665 | 2.9521 | 0.63 | 0.6275 | 0.2157 | 0.1596 |
0.1799 | 73.0 | 511 | 1.7412 | 0.63 | 0.5666 | 2.9508 | 0.63 | 0.6275 | 0.2262 | 0.1596 |
0.1799 | 74.0 | 518 | 1.7417 | 0.63 | 0.5666 | 2.9509 | 0.63 | 0.6272 | 0.2248 | 0.1596 |
0.1799 | 75.0 | 525 | 1.7420 | 0.63 | 0.5666 | 2.9555 | 0.63 | 0.6272 | 0.2219 | 0.1596 |
0.1799 | 76.0 | 532 | 1.7425 | 0.63 | 0.5667 | 2.9541 | 0.63 | 0.6268 | 0.2233 | 0.1596 |
0.1799 | 77.0 | 539 | 1.7430 | 0.63 | 0.5668 | 2.9773 | 0.63 | 0.6276 | 0.2133 | 0.1596 |
0.1799 | 78.0 | 546 | 1.7435 | 0.63 | 0.5668 | 2.9772 | 0.63 | 0.6276 | 0.2134 | 0.1597 |
0.1799 | 79.0 | 553 | 1.7439 | 0.63 | 0.5669 | 2.9514 | 0.63 | 0.6276 | 0.2142 | 0.1596 |
0.1799 | 80.0 | 560 | 1.7444 | 0.6325 | 0.5669 | 2.9499 | 0.6325 | 0.6303 | 0.2118 | 0.1594 |
0.1799 | 81.0 | 567 | 1.7451 | 0.6325 | 0.5669 | 2.9506 | 0.6325 | 0.6303 | 0.2078 | 0.1594 |
0.1799 | 82.0 | 574 | 1.7455 | 0.6325 | 0.5670 | 2.9617 | 0.6325 | 0.6303 | 0.2079 | 0.1594 |
0.1799 | 83.0 | 581 | 1.7459 | 0.6325 | 0.5671 | 2.9766 | 0.6325 | 0.6303 | 0.2130 | 0.1594 |
0.1799 | 84.0 | 588 | 1.7463 | 0.63 | 0.5672 | 2.9770 | 0.63 | 0.6278 | 0.2085 | 0.1597 |
0.1799 | 85.0 | 595 | 1.7466 | 0.6275 | 0.5672 | 2.9768 | 0.6275 | 0.6250 | 0.2111 | 0.1598 |
0.1799 | 86.0 | 602 | 1.7469 | 0.63 | 0.5673 | 2.9769 | 0.63 | 0.6278 | 0.2086 | 0.1597 |
0.1799 | 87.0 | 609 | 1.7472 | 0.6275 | 0.5673 | 2.9770 | 0.6275 | 0.6250 | 0.2140 | 0.1598 |
0.1799 | 88.0 | 616 | 1.7474 | 0.6275 | 0.5674 | 2.9771 | 0.6275 | 0.6250 | 0.2111 | 0.1598 |
0.1799 | 89.0 | 623 | 1.7477 | 0.6275 | 0.5674 | 2.9772 | 0.6275 | 0.6250 | 0.2112 | 0.1598 |
0.1799 | 90.0 | 630 | 1.7480 | 0.6275 | 0.5675 | 2.9770 | 0.6275 | 0.6250 | 0.2112 | 0.1598 |
0.1799 | 91.0 | 637 | 1.7483 | 0.6275 | 0.5675 | 2.9770 | 0.6275 | 0.6250 | 0.2112 | 0.1599 |
0.1799 | 92.0 | 644 | 1.7485 | 0.6275 | 0.5676 | 2.9769 | 0.6275 | 0.6250 | 0.2112 | 0.1598 |
0.1799 | 93.0 | 651 | 1.7486 | 0.6275 | 0.5676 | 2.9770 | 0.6275 | 0.6250 | 0.2112 | 0.1598 |
0.1799 | 94.0 | 658 | 1.7488 | 0.6275 | 0.5676 | 2.9770 | 0.6275 | 0.6250 | 0.2131 | 0.1598 |
0.1799 | 95.0 | 665 | 1.7489 | 0.6275 | 0.5676 | 2.9768 | 0.6275 | 0.6250 | 0.2143 | 0.1598 |
0.1799 | 96.0 | 672 | 1.7491 | 0.6275 | 0.5676 | 2.9768 | 0.6275 | 0.6250 | 0.2161 | 0.1599 |
0.1799 | 97.0 | 679 | 1.7492 | 0.6275 | 0.5676 | 2.9768 | 0.6275 | 0.6250 | 0.2161 | 0.1599 |
0.1799 | 98.0 | 686 | 1.7493 | 0.6275 | 0.5677 | 2.9768 | 0.6275 | 0.6250 | 0.2161 | 0.1599 |
0.1799 | 99.0 | 693 | 1.7493 | 0.6275 | 0.5677 | 2.9769 | 0.6275 | 0.6250 | 0.2161 | 0.1599 |
0.1799 | 100.0 | 700 | 1.7493 | 0.6275 | 0.5677 | 2.9769 | 0.6275 | 0.6250 | 0.2161 | 0.1599 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2