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bdpc
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resnet101-base_tobacco-cnn_tobacco3482_kd_CEKD_t5.0_a0.9

Image Classification
Transformers
PyTorch
resnet
Generated from Trainer
Inference Endpoints
Model card Files Community
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  1. README.md +58 -58
  2. pytorch_model.bin +1 -1
  3. test-logits.npz +2 -2
  4. training_args.bin +1 -1
  5. validation-logits.npz +2 -2
README.md CHANGED
@@ -17,14 +17,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8862
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- - Accuracy: 0.675
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- - Brier Loss: 0.4233
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- - Nll: 2.4267
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- - F1 Micro: 0.675
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- - F1 Macro: 0.6266
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- - Ece: 0.2528
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- - Aurc: 0.1205
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  ## Model description
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@@ -56,56 +56,56 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
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- | No log | 1.0 | 13 | 2.1186 | 0.165 | 0.8967 | 8.5414 | 0.165 | 0.1128 | 0.2087 | 0.8330 |
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- | No log | 2.0 | 26 | 2.1139 | 0.14 | 0.8960 | 8.0889 | 0.14 | 0.0907 | 0.1924 | 0.8318 |
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- | No log | 3.0 | 39 | 2.0743 | 0.195 | 0.8880 | 6.6316 | 0.195 | 0.1098 | 0.2224 | 0.7879 |
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- | No log | 4.0 | 52 | 2.0101 | 0.205 | 0.8741 | 6.0411 | 0.205 | 0.0851 | 0.2448 | 0.7302 |
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- | No log | 5.0 | 65 | 1.9697 | 0.22 | 0.8650 | 5.8808 | 0.22 | 0.1090 | 0.2441 | 0.7307 |
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- | No log | 6.0 | 78 | 1.8642 | 0.27 | 0.8396 | 6.0693 | 0.27 | 0.1370 | 0.2742 | 0.6623 |
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- | No log | 7.0 | 91 | 1.7716 | 0.35 | 0.8100 | 5.7342 | 0.35 | 0.1964 | 0.3131 | 0.4496 |
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- | No log | 8.0 | 104 | 1.7580 | 0.33 | 0.8084 | 5.8902 | 0.33 | 0.1762 | 0.3185 | 0.5663 |
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- | No log | 9.0 | 117 | 1.7346 | 0.425 | 0.8000 | 5.5871 | 0.425 | 0.2645 | 0.3466 | 0.3888 |
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- | No log | 10.0 | 130 | 1.6557 | 0.365 | 0.7744 | 5.2246 | 0.3650 | 0.2256 | 0.2890 | 0.5081 |
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- | No log | 11.0 | 143 | 1.5067 | 0.46 | 0.7014 | 4.7492 | 0.46 | 0.3053 | 0.3024 | 0.2923 |
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- | No log | 12.0 | 156 | 1.5340 | 0.425 | 0.7212 | 4.4923 | 0.425 | 0.2746 | 0.2833 | 0.3650 |
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- | No log | 13.0 | 169 | 1.5064 | 0.495 | 0.7111 | 4.1576 | 0.495 | 0.3443 | 0.3225 | 0.2907 |
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- | No log | 14.0 | 182 | 1.4767 | 0.54 | 0.6972 | 3.7984 | 0.54 | 0.3804 | 0.3381 | 0.2831 |
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- | No log | 15.0 | 195 | 1.3709 | 0.525 | 0.6453 | 3.7435 | 0.525 | 0.3771 | 0.3188 | 0.2541 |
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- | No log | 16.0 | 208 | 1.3204 | 0.535 | 0.6223 | 3.4971 | 0.535 | 0.3919 | 0.3115 | 0.2424 |
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- | No log | 17.0 | 221 | 1.4782 | 0.465 | 0.7008 | 3.4793 | 0.465 | 0.3731 | 0.3311 | 0.4138 |
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- | No log | 18.0 | 234 | 1.3456 | 0.49 | 0.6523 | 3.3409 | 0.49 | 0.3839 | 0.2832 | 0.3570 |
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- | No log | 19.0 | 247 | 1.2137 | 0.625 | 0.5708 | 3.4778 | 0.625 | 0.5087 | 0.3030 | 0.1904 |
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- | No log | 20.0 | 260 | 1.3527 | 0.565 | 0.6484 | 3.3840 | 0.565 | 0.4761 | 0.3402 | 0.3349 |
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- | No log | 21.0 | 273 | 1.1692 | 0.6 | 0.5633 | 2.9586 | 0.6 | 0.4932 | 0.3138 | 0.2299 |
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- | No log | 22.0 | 286 | 1.1144 | 0.65 | 0.5253 | 2.9930 | 0.65 | 0.5281 | 0.2768 | 0.1585 |
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- | No log | 23.0 | 299 | 1.0749 | 0.635 | 0.5048 | 2.8481 | 0.635 | 0.5404 | 0.2378 | 0.1642 |
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- | No log | 24.0 | 312 | 1.0619 | 0.665 | 0.5018 | 2.7665 | 0.665 | 0.5653 | 0.2741 | 0.1533 |
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- | No log | 25.0 | 325 | 1.0733 | 0.68 | 0.5036 | 2.6592 | 0.68 | 0.5960 | 0.2948 | 0.1633 |
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- | No log | 26.0 | 338 | 1.0319 | 0.655 | 0.4930 | 2.6467 | 0.655 | 0.5786 | 0.2598 | 0.1576 |
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- | No log | 27.0 | 351 | 1.0147 | 0.665 | 0.4805 | 2.6123 | 0.665 | 0.5877 | 0.2406 | 0.1405 |
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- | No log | 28.0 | 364 | 0.9862 | 0.675 | 0.4734 | 2.4990 | 0.675 | 0.5876 | 0.2512 | 0.1474 |
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- | No log | 29.0 | 377 | 0.9816 | 0.685 | 0.4696 | 2.5984 | 0.685 | 0.6131 | 0.2446 | 0.1428 |
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- | No log | 30.0 | 390 | 0.9755 | 0.66 | 0.4698 | 2.5609 | 0.66 | 0.6009 | 0.2562 | 0.1555 |
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- | No log | 31.0 | 403 | 0.9789 | 0.7 | 0.4601 | 2.6827 | 0.7 | 0.6374 | 0.2667 | 0.1271 |
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- | No log | 32.0 | 416 | 0.9426 | 0.695 | 0.4501 | 2.5256 | 0.695 | 0.6315 | 0.2560 | 0.1420 |
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- | No log | 33.0 | 429 | 0.9428 | 0.695 | 0.4461 | 2.6429 | 0.695 | 0.6298 | 0.2250 | 0.1243 |
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- | No log | 34.0 | 442 | 0.9370 | 0.675 | 0.4455 | 2.5812 | 0.675 | 0.6061 | 0.2523 | 0.1284 |
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- | No log | 35.0 | 455 | 0.9290 | 0.68 | 0.4391 | 2.3724 | 0.68 | 0.6174 | 0.2459 | 0.1361 |
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- | No log | 36.0 | 468 | 0.9190 | 0.66 | 0.4393 | 2.3838 | 0.66 | 0.6140 | 0.2201 | 0.1327 |
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- | No log | 37.0 | 481 | 0.9061 | 0.685 | 0.4310 | 2.3683 | 0.685 | 0.6390 | 0.2222 | 0.1196 |
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- | No log | 38.0 | 494 | 0.9184 | 0.705 | 0.4387 | 2.5054 | 0.705 | 0.6444 | 0.2479 | 0.1191 |
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- | 1.0876 | 39.0 | 507 | 0.9185 | 0.685 | 0.4425 | 2.4429 | 0.685 | 0.6327 | 0.2450 | 0.1337 |
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- | 1.0876 | 40.0 | 520 | 0.9002 | 0.66 | 0.4289 | 2.4439 | 0.66 | 0.6226 | 0.2152 | 0.1302 |
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- | 1.0876 | 41.0 | 533 | 0.9027 | 0.68 | 0.4319 | 2.3802 | 0.68 | 0.6179 | 0.2247 | 0.1200 |
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- | 1.0876 | 42.0 | 546 | 0.8977 | 0.68 | 0.4321 | 2.3577 | 0.68 | 0.6195 | 0.2296 | 0.1250 |
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- | 1.0876 | 43.0 | 559 | 0.8861 | 0.685 | 0.4215 | 2.3150 | 0.685 | 0.6324 | 0.1870 | 0.1198 |
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- | 1.0876 | 44.0 | 572 | 0.8913 | 0.68 | 0.4235 | 2.4228 | 0.68 | 0.6328 | 0.2260 | 0.1193 |
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- | 1.0876 | 45.0 | 585 | 0.8895 | 0.675 | 0.4251 | 2.4104 | 0.675 | 0.6264 | 0.2409 | 0.1208 |
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- | 1.0876 | 46.0 | 598 | 0.8901 | 0.665 | 0.4223 | 2.3598 | 0.665 | 0.6146 | 0.2235 | 0.1208 |
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- | 1.0876 | 47.0 | 611 | 0.8809 | 0.68 | 0.4206 | 2.3528 | 0.68 | 0.6233 | 0.2306 | 0.1222 |
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- | 1.0876 | 48.0 | 624 | 0.8845 | 0.69 | 0.4243 | 2.4251 | 0.69 | 0.6362 | 0.2232 | 0.1219 |
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- | 1.0876 | 49.0 | 637 | 0.8849 | 0.675 | 0.4243 | 2.4261 | 0.675 | 0.6207 | 0.2192 | 0.1242 |
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- | 1.0876 | 50.0 | 650 | 0.8862 | 0.675 | 0.4233 | 2.4267 | 0.675 | 0.6266 | 0.2528 | 0.1205 |
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  ### Framework versions
 
17
 
18
  This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
19
  It achieves the following results on the evaluation set:
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+ - Loss: 0.8809
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+ - Accuracy: 0.7
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+ - Brier Loss: 0.4126
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+ - Nll: 2.4279
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+ - F1 Micro: 0.7
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+ - F1 Macro: 0.6279
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+ - Ece: 0.2569
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+ - Aurc: 0.1111
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
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+ | No log | 1.0 | 13 | 2.1185 | 0.165 | 0.8967 | 8.5399 | 0.165 | 0.1130 | 0.2151 | 0.8331 |
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+ | No log | 2.0 | 26 | 2.1127 | 0.13 | 0.8958 | 8.1152 | 0.13 | 0.0842 | 0.1816 | 0.8392 |
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+ | No log | 3.0 | 39 | 2.0781 | 0.165 | 0.8888 | 6.8828 | 0.165 | 0.0878 | 0.2150 | 0.8082 |
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+ | No log | 4.0 | 52 | 2.0197 | 0.22 | 0.8762 | 5.7578 | 0.22 | 0.1155 | 0.2521 | 0.7521 |
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+ | No log | 5.0 | 65 | 1.9499 | 0.205 | 0.8601 | 6.0641 | 0.205 | 0.0951 | 0.2567 | 0.7355 |
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+ | No log | 6.0 | 78 | 1.9019 | 0.25 | 0.8483 | 5.8930 | 0.25 | 0.1178 | 0.2728 | 0.6862 |
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+ | No log | 7.0 | 91 | 1.8252 | 0.28 | 0.8301 | 5.8062 | 0.28 | 0.1660 | 0.2890 | 0.6982 |
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+ | No log | 8.0 | 104 | 1.8194 | 0.28 | 0.8275 | 5.2642 | 0.28 | 0.1625 | 0.2874 | 0.6935 |
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+ | No log | 9.0 | 117 | 1.7671 | 0.355 | 0.8109 | 5.1326 | 0.3550 | 0.2211 | 0.3018 | 0.5678 |
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+ | No log | 10.0 | 130 | 1.6582 | 0.355 | 0.7774 | 5.2226 | 0.3550 | 0.2200 | 0.2991 | 0.5305 |
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+ | No log | 11.0 | 143 | 1.5849 | 0.395 | 0.7422 | 5.0239 | 0.395 | 0.2436 | 0.2979 | 0.3974 |
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+ | No log | 12.0 | 156 | 1.4908 | 0.46 | 0.7001 | 4.2790 | 0.46 | 0.3169 | 0.3091 | 0.3003 |
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+ | No log | 13.0 | 169 | 1.6016 | 0.395 | 0.7496 | 4.2149 | 0.395 | 0.2793 | 0.2929 | 0.4640 |
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+ | No log | 14.0 | 182 | 1.4714 | 0.475 | 0.6971 | 4.0742 | 0.4750 | 0.3299 | 0.3177 | 0.3613 |
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+ | No log | 15.0 | 195 | 1.5007 | 0.46 | 0.7119 | 3.8252 | 0.46 | 0.3145 | 0.3111 | 0.3954 |
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+ | No log | 16.0 | 208 | 1.4352 | 0.515 | 0.6776 | 3.4028 | 0.515 | 0.3948 | 0.3376 | 0.2993 |
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+ | No log | 17.0 | 221 | 1.2890 | 0.575 | 0.6104 | 3.4453 | 0.575 | 0.4478 | 0.2940 | 0.2119 |
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+ | No log | 18.0 | 234 | 1.2190 | 0.595 | 0.5719 | 3.2413 | 0.595 | 0.4662 | 0.2608 | 0.1981 |
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+ | No log | 19.0 | 247 | 1.2287 | 0.59 | 0.5764 | 3.2303 | 0.59 | 0.4857 | 0.2811 | 0.2020 |
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+ | No log | 20.0 | 260 | 1.1726 | 0.64 | 0.5494 | 2.9544 | 0.64 | 0.5307 | 0.2993 | 0.1708 |
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+ | No log | 21.0 | 273 | 1.1305 | 0.61 | 0.5384 | 2.9557 | 0.61 | 0.5170 | 0.2771 | 0.1949 |
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+ | No log | 22.0 | 286 | 1.1256 | 0.645 | 0.5295 | 2.7934 | 0.645 | 0.5381 | 0.3181 | 0.1629 |
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+ | No log | 23.0 | 299 | 1.1209 | 0.645 | 0.5217 | 2.8697 | 0.645 | 0.5432 | 0.3055 | 0.1687 |
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+ | No log | 24.0 | 312 | 1.2513 | 0.685 | 0.5917 | 2.7262 | 0.685 | 0.5639 | 0.3779 | 0.1833 |
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+ | No log | 25.0 | 325 | 1.0321 | 0.695 | 0.4819 | 2.7202 | 0.695 | 0.5896 | 0.2810 | 0.1280 |
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+ | No log | 26.0 | 338 | 1.0405 | 0.645 | 0.4957 | 2.6116 | 0.645 | 0.5661 | 0.2515 | 0.1700 |
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+ | No log | 27.0 | 351 | 1.0580 | 0.695 | 0.4933 | 2.7436 | 0.695 | 0.5996 | 0.2967 | 0.1339 |
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+ | No log | 28.0 | 364 | 0.9740 | 0.65 | 0.4575 | 2.5682 | 0.65 | 0.5731 | 0.2513 | 0.1384 |
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+ | No log | 29.0 | 377 | 0.9934 | 0.695 | 0.4651 | 2.5753 | 0.695 | 0.6108 | 0.2775 | 0.1171 |
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+ | No log | 30.0 | 390 | 0.9900 | 0.645 | 0.4695 | 2.6280 | 0.645 | 0.5668 | 0.2459 | 0.1558 |
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+ | No log | 31.0 | 403 | 0.9671 | 0.695 | 0.4504 | 2.8174 | 0.695 | 0.6094 | 0.2505 | 0.1188 |
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+ | No log | 32.0 | 416 | 0.9327 | 0.715 | 0.4324 | 2.5285 | 0.715 | 0.6415 | 0.2565 | 0.1086 |
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+ | No log | 33.0 | 429 | 0.9628 | 0.71 | 0.4464 | 2.5876 | 0.7100 | 0.6435 | 0.2709 | 0.1152 |
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+ | No log | 34.0 | 442 | 0.9316 | 0.715 | 0.4353 | 2.7111 | 0.715 | 0.6334 | 0.2361 | 0.1078 |
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+ | No log | 35.0 | 455 | 0.9275 | 0.7 | 0.4364 | 2.5226 | 0.7 | 0.6251 | 0.2586 | 0.1207 |
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+ | No log | 36.0 | 468 | 0.9301 | 0.7 | 0.4346 | 2.6464 | 0.7 | 0.6232 | 0.2482 | 0.1142 |
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+ | No log | 37.0 | 481 | 0.9013 | 0.695 | 0.4194 | 2.5575 | 0.695 | 0.6197 | 0.2554 | 0.1098 |
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+ | No log | 38.0 | 494 | 0.9008 | 0.695 | 0.4196 | 2.6270 | 0.695 | 0.6156 | 0.2246 | 0.1063 |
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+ | 1.0903 | 39.0 | 507 | 0.9185 | 0.71 | 0.4311 | 2.6290 | 0.7100 | 0.6362 | 0.2626 | 0.1165 |
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+ | 1.0903 | 40.0 | 520 | 0.9053 | 0.685 | 0.4254 | 2.5057 | 0.685 | 0.6239 | 0.2210 | 0.1171 |
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+ | 1.0903 | 41.0 | 533 | 0.8955 | 0.7 | 0.4189 | 2.4823 | 0.7 | 0.6291 | 0.1995 | 0.1103 |
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+ | 1.0903 | 42.0 | 546 | 0.9012 | 0.69 | 0.4223 | 2.5377 | 0.69 | 0.6195 | 0.2486 | 0.1119 |
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+ | 1.0903 | 43.0 | 559 | 0.8894 | 0.71 | 0.4138 | 2.6167 | 0.7100 | 0.6382 | 0.2459 | 0.1022 |
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+ | 1.0903 | 44.0 | 572 | 0.8846 | 0.695 | 0.4132 | 2.5130 | 0.695 | 0.6265 | 0.2198 | 0.1093 |
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+ | 1.0903 | 45.0 | 585 | 0.8946 | 0.69 | 0.4190 | 2.6357 | 0.69 | 0.6230 | 0.2375 | 0.1145 |
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+ | 1.0903 | 46.0 | 598 | 0.8931 | 0.705 | 0.4168 | 2.6306 | 0.705 | 0.6342 | 0.2555 | 0.1102 |
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+ | 1.0903 | 47.0 | 611 | 0.8842 | 0.71 | 0.4160 | 2.3021 | 0.7100 | 0.6347 | 0.2096 | 0.1120 |
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+ | 1.0903 | 48.0 | 624 | 0.8805 | 0.695 | 0.4140 | 2.3447 | 0.695 | 0.6237 | 0.2181 | 0.1128 |
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+ | 1.0903 | 49.0 | 637 | 0.8816 | 0.7 | 0.4142 | 2.4358 | 0.7 | 0.6295 | 0.2550 | 0.1112 |
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+ | 1.0903 | 50.0 | 650 | 0.8809 | 0.7 | 0.4126 | 2.4279 | 0.7 | 0.6279 | 0.2569 | 0.1111 |
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  ### Framework versions
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