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vit-base_rvl-cdip-small_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5_rand
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the None dataset. It achieves the following results on the evaluation set:
- Loss: 6.1637
- Accuracy: 0.6275
- Brier Loss: 0.6026
- Nll: 2.9068
- F1 Micro: 0.6275
- F1 Macro: 0.6313
- Ece: 0.2499
- Aurc: 0.1609
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: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|
| 6.3322 | 1.0 | 1000 | 6.0794 | 0.1835 | 0.8928 | 6.5679 | 0.1835 | 0.1322 | 0.0627 | 0.6846 |
| 5.8198 | 2.0 | 2000 | 5.5963 | 0.3668 | 0.7821 | 3.5543 | 0.3668 | 0.3217 | 0.0967 | 0.4448 |
| 5.53 | 3.0 | 3000 | 5.4184 | 0.4225 | 0.7382 | 3.4217 | 0.4225 | 0.3848 | 0.1087 | 0.3778 |
| 5.3449 | 4.0 | 4000 | 5.1895 | 0.4655 | 0.6813 | 3.0794 | 0.4655 | 0.4562 | 0.1076 | 0.3029 |
| 5.2467 | 5.0 | 5000 | 5.1813 | 0.4592 | 0.6845 | 2.9944 | 0.4592 | 0.4430 | 0.1009 | 0.3125 |
| 5.1382 | 6.0 | 6000 | 5.0102 | 0.4998 | 0.6423 | 2.7804 | 0.4998 | 0.4926 | 0.1013 | 0.2660 |
| 5.0255 | 7.0 | 7000 | 4.9611 | 0.501 | 0.6350 | 2.7692 | 0.501 | 0.5085 | 0.0795 | 0.2690 |
| 4.9089 | 8.0 | 8000 | 4.9327 | 0.508 | 0.6204 | 2.6580 | 0.508 | 0.5068 | 0.0622 | 0.2565 |
| 4.8337 | 9.0 | 9000 | 4.8324 | 0.5467 | 0.5866 | 2.5636 | 0.5467 | 0.5419 | 0.0642 | 0.2274 |
| 4.747 | 10.0 | 10000 | 5.0170 | 0.5302 | 0.6080 | 2.7672 | 0.5302 | 0.5193 | 0.0622 | 0.2452 |
| 4.622 | 11.0 | 11000 | 4.8259 | 0.5593 | 0.5709 | 2.6791 | 0.5593 | 0.5520 | 0.0619 | 0.2090 |
| 4.5449 | 12.0 | 12000 | 4.7696 | 0.5675 | 0.5583 | 2.5273 | 0.5675 | 0.5678 | 0.0541 | 0.2016 |
| 4.447 | 13.0 | 13000 | 4.8718 | 0.5575 | 0.5775 | 2.7597 | 0.5575 | 0.5557 | 0.0575 | 0.2142 |
| 4.341 | 14.0 | 14000 | 4.7644 | 0.5897 | 0.5368 | 2.5797 | 0.5897 | 0.5930 | 0.0560 | 0.1835 |
| 4.2476 | 15.0 | 15000 | 4.8339 | 0.5905 | 0.5485 | 2.6684 | 0.5905 | 0.5903 | 0.0719 | 0.1872 |
| 4.1592 | 16.0 | 16000 | 4.7828 | 0.5877 | 0.5456 | 2.7300 | 0.5877 | 0.5877 | 0.0784 | 0.1832 |
| 4.0513 | 17.0 | 17000 | 4.8771 | 0.5885 | 0.5533 | 2.9097 | 0.5885 | 0.5930 | 0.0965 | 0.1867 |
| 3.9646 | 18.0 | 18000 | 4.8980 | 0.596 | 0.5499 | 2.8383 | 0.596 | 0.5948 | 0.1025 | 0.1797 |
| 3.8768 | 19.0 | 19000 | 4.9787 | 0.605 | 0.5551 | 2.8903 | 0.605 | 0.6050 | 0.1302 | 0.1765 |
| 3.7739 | 20.0 | 20000 | 5.1202 | 0.5945 | 0.5727 | 3.0393 | 0.5945 | 0.5935 | 0.1493 | 0.1821 |
| 3.7023 | 21.0 | 21000 | 5.1879 | 0.5998 | 0.5785 | 2.9570 | 0.5998 | 0.5991 | 0.1690 | 0.1807 |
| 3.6301 | 22.0 | 22000 | 5.2707 | 0.5933 | 0.5908 | 3.1177 | 0.5933 | 0.5971 | 0.1863 | 0.1829 |
| 3.5857 | 23.0 | 23000 | 5.2522 | 0.5887 | 0.5994 | 3.2051 | 0.5887 | 0.5949 | 0.1928 | 0.1857 |
| 3.5256 | 24.0 | 24000 | 5.3443 | 0.6102 | 0.5857 | 2.9687 | 0.6102 | 0.6084 | 0.1953 | 0.1760 |
| 3.4954 | 25.0 | 25000 | 5.3010 | 0.6045 | 0.5874 | 3.0184 | 0.6045 | 0.6053 | 0.1851 | 0.1807 |
| 3.46 | 26.0 | 26000 | 5.4451 | 0.5992 | 0.5994 | 3.0539 | 0.5992 | 0.6033 | 0.2053 | 0.1819 |
| 3.4086 | 27.0 | 27000 | 5.4299 | 0.608 | 0.5913 | 3.1127 | 0.608 | 0.6082 | 0.2027 | 0.1751 |
| 3.3769 | 28.0 | 28000 | 5.6979 | 0.601 | 0.6236 | 3.1077 | 0.601 | 0.6024 | 0.2396 | 0.1777 |
| 3.3238 | 29.0 | 29000 | 5.6090 | 0.611 | 0.6013 | 3.0875 | 0.611 | 0.6114 | 0.2238 | 0.1729 |
| 3.3011 | 30.0 | 30000 | 5.6356 | 0.6105 | 0.5991 | 2.9450 | 0.6105 | 0.6123 | 0.2243 | 0.1719 |
| 3.2708 | 31.0 | 31000 | 5.7634 | 0.604 | 0.6181 | 2.9119 | 0.604 | 0.6075 | 0.2402 | 0.1771 |
| 3.2556 | 32.0 | 32000 | 5.7042 | 0.617 | 0.6002 | 2.9324 | 0.617 | 0.6199 | 0.2263 | 0.1740 |
| 3.2213 | 33.0 | 33000 | 5.7388 | 0.603 | 0.6121 | 2.9240 | 0.603 | 0.6108 | 0.2345 | 0.1782 |
| 3.2138 | 34.0 | 34000 | 5.8008 | 0.6218 | 0.6001 | 2.9209 | 0.6218 | 0.6206 | 0.2284 | 0.1701 |
| 3.1994 | 35.0 | 35000 | 5.7350 | 0.6142 | 0.5967 | 2.9021 | 0.6142 | 0.6147 | 0.2294 | 0.1688 |
| 3.1776 | 36.0 | 36000 | 5.7487 | 0.609 | 0.6032 | 2.8651 | 0.609 | 0.6121 | 0.2329 | 0.1689 |
| 3.1606 | 37.0 | 37000 | 5.8022 | 0.6165 | 0.6075 | 2.8604 | 0.6165 | 0.6189 | 0.2398 | 0.1677 |
| 3.1405 | 38.0 | 38000 | 5.8133 | 0.6235 | 0.5949 | 2.8775 | 0.6235 | 0.6272 | 0.2319 | 0.1640 |
| 3.132 | 39.0 | 39000 | 5.8934 | 0.6232 | 0.5974 | 2.9324 | 0.6232 | 0.6274 | 0.2389 | 0.1639 |
| 3.1303 | 40.0 | 40000 | 5.8902 | 0.6288 | 0.5947 | 2.9049 | 0.6288 | 0.6322 | 0.2344 | 0.1634 |
| 3.1187 | 41.0 | 41000 | 5.9076 | 0.6215 | 0.5987 | 2.8584 | 0.6215 | 0.6261 | 0.2394 | 0.1630 |
| 3.0969 | 42.0 | 42000 | 5.9469 | 0.6265 | 0.5984 | 2.8509 | 0.6265 | 0.6309 | 0.2375 | 0.1631 |
| 3.0964 | 43.0 | 43000 | 5.9442 | 0.6252 | 0.5951 | 2.9309 | 0.6252 | 0.6291 | 0.2397 | 0.1607 |
| 3.0953 | 44.0 | 44000 | 6.0126 | 0.6238 | 0.5998 | 2.8956 | 0.6238 | 0.6274 | 0.2419 | 0.1630 |
| 3.0904 | 45.0 | 45000 | 6.0602 | 0.6295 | 0.5991 | 2.8669 | 0.6295 | 0.6334 | 0.2417 | 0.1609 |
| 3.0794 | 46.0 | 46000 | 6.0782 | 0.6282 | 0.6027 | 2.8830 | 0.6282 | 0.6321 | 0.2442 | 0.1616 |
| 3.0788 | 47.0 | 47000 | 6.1062 | 0.6275 | 0.6003 | 2.8472 | 0.6275 | 0.6316 | 0.2471 | 0.1610 |
| 3.0802 | 48.0 | 48000 | 6.1079 | 0.6285 | 0.5998 | 2.8916 | 0.6285 | 0.6322 | 0.2465 | 0.1600 |
| 3.0644 | 49.0 | 49000 | 6.1569 | 0.6275 | 0.6025 | 2.8941 | 0.6275 | 0.6314 | 0.2497 | 0.1610 |
| 3.0751 | 50.0 | 50000 | 6.1637 | 0.6275 | 0.6026 | 2.9068 | 0.6275 | 0.6313 | 0.2499 | 0.1609 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
