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End of training

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  1. README.md +52 -25
  2. model.safetensors +1 -1
  3. runs/Dec26_20-33-20_ip-10-90-0-154/events.out.tfevents.1735245201.ip-10-90-0-154.47762.0 +2 -2
README.md CHANGED
@@ -16,29 +16,29 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 5.5339
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- - Map: 0.0072
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- - Map 50: 0.0236
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- - Map 75: 0.0017
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- - Map Small: 0.0075
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  - Map Medium: -1.0
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  - Map Large: -1.0
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- - Mar 1: 0.0248
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- - Mar 10: 0.1156
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- - Mar 100: 0.1915
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- - Mar Small: 0.1915
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  - Mar Medium: -1.0
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  - Mar Large: -1.0
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- - Map Left: 0.0147
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- - Mar 100 Left: 0.4532
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- - Map Right: 0.0071
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- - Mar 100 Right: 0.1352
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- - Map Up: 0.0
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- - Mar 100 Up: 0.0
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- - Map Down: 0.0143
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- - Mar 100 Down: 0.369
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- - Map ?: 0.0
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- - Mar 100 ?: 0.0
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  ## Model description
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@@ -63,15 +63,42 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: cosine
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- - num_epochs: 3
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Left | Mar 100 Left | Map Right | Mar 100 Right | Map Up | Mar 100 Up | Map Down | Mar 100 Down | Map ? | Mar 100 ? |
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- |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:--------:|:------------:|:---------:|:-------------:|:------:|:----------:|:--------:|:------------:|:-----:|:---------:|
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- | No log | 1.0 | 8 | 11.7538 | 0.0024 | 0.0086 | 0.0004 | 0.0025 | -1.0 | -1.0 | 0.0097 | 0.0534 | 0.119 | 0.119 | -1.0 | -1.0 | 0.0079 | 0.3915 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0043 | 0.2034 | 0.0 | 0.0 |
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- | No log | 2.0 | 16 | 6.2166 | 0.0049 | 0.0154 | 0.0007 | 0.0051 | -1.0 | -1.0 | 0.0201 | 0.0864 | 0.1535 | 0.1535 | -1.0 | -1.0 | 0.0095 | 0.4085 | 0.0026 | 0.0833 | 0.0 | 0.0 | 0.0125 | 0.2759 | 0.0 | 0.0 |
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- | No log | 3.0 | 24 | 5.5339 | 0.0072 | 0.0236 | 0.0017 | 0.0075 | -1.0 | -1.0 | 0.0248 | 0.1156 | 0.1915 | 0.1915 | -1.0 | -1.0 | 0.0147 | 0.4532 | 0.0071 | 0.1352 | 0.0 | 0.0 | 0.0143 | 0.369 | 0.0 | 0.0 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.8155
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+ - Map: 0.1291
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+ - Map 50: 0.1942
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+ - Map 75: 0.1499
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+ - Map Small: 0.1291
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  - Map Medium: -1.0
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  - Map Large: -1.0
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+ - Mar 1: 0.2282
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+ - Mar 10: 0.7027
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+ - Mar 100: 0.7855
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+ - Mar Small: 0.7855
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  - Mar Medium: -1.0
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  - Mar Large: -1.0
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+ - Map Left: 0.1364
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+ - Mar 100 Left: 0.8404
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+ - Map Right: 0.1038
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+ - Mar 100 Right: 0.8389
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+ - Map Up: 0.1453
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+ - Mar 100 Up: 0.7917
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+ - Map Down: 0.0569
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+ - Mar 100 Down: 0.7138
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+ - Map ?: 0.2029
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+ - Mar 100 ?: 0.7427
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  ## Model description
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: cosine
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+ - num_epochs: 30
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Left | Mar 100 Left | Map Right | Mar 100 Right | Map Up | Mar 100 Up | Map Down | Mar 100 Down | Map ? | Mar 100 ? |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:--------:|:------------:|:---------:|:-------------:|:------:|:----------:|:--------:|:------------:|:------:|:---------:|
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+ | No log | 1.0 | 8 | 11.3765 | 0.0004 | 0.0024 | 0.0 | 0.0004 | -1.0 | -1.0 | 0.0009 | 0.0069 | 0.0141 | 0.0141 | -1.0 | -1.0 | 0.0014 | 0.0085 | 0.0006 | 0.0444 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0177 |
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+ | No log | 2.0 | 16 | 4.0235 | 0.0098 | 0.0349 | 0.001 | 0.01 | -1.0 | -1.0 | 0.0094 | 0.1264 | 0.2197 | 0.2197 | -1.0 | -1.0 | 0.0204 | 0.3872 | 0.0001 | 0.0185 | 0.011 | 0.3104 | 0.0004 | 0.0483 | 0.0171 | 0.3339 |
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+ | No log | 3.0 | 24 | 2.1472 | 0.032 | 0.0868 | 0.0123 | 0.0321 | -1.0 | -1.0 | 0.0314 | 0.2188 | 0.3431 | 0.3431 | -1.0 | -1.0 | 0.083 | 0.483 | 0.0047 | 0.1889 | 0.0383 | 0.4917 | 0.0015 | 0.0966 | 0.0324 | 0.4556 |
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+ | No log | 4.0 | 32 | 1.6748 | 0.0318 | 0.0907 | 0.0132 | 0.0318 | -1.0 | -1.0 | 0.0476 | 0.2917 | 0.4206 | 0.4206 | -1.0 | -1.0 | 0.0572 | 0.566 | 0.0274 | 0.4889 | 0.0297 | 0.3479 | 0.0075 | 0.2172 | 0.0371 | 0.4831 |
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+ | No log | 5.0 | 40 | 1.4340 | 0.0396 | 0.1062 | 0.0206 | 0.0396 | -1.0 | -1.0 | 0.0477 | 0.3648 | 0.5125 | 0.5125 | -1.0 | -1.0 | 0.0642 | 0.6213 | 0.0432 | 0.587 | 0.0543 | 0.6042 | 0.0063 | 0.2276 | 0.0303 | 0.5226 |
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+ | No log | 6.0 | 48 | 1.2659 | 0.0501 | 0.1098 | 0.0332 | 0.0501 | -1.0 | -1.0 | 0.0785 | 0.4125 | 0.5584 | 0.5584 | -1.0 | -1.0 | 0.0872 | 0.6872 | 0.0415 | 0.6296 | 0.0654 | 0.5854 | 0.0106 | 0.2759 | 0.0459 | 0.6137 |
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+ | No log | 7.0 | 56 | 1.3187 | 0.0466 | 0.1349 | 0.0173 | 0.0466 | -1.0 | -1.0 | 0.0655 | 0.3572 | 0.4875 | 0.4875 | -1.0 | -1.0 | 0.0738 | 0.5617 | 0.0444 | 0.563 | 0.0572 | 0.5229 | 0.0206 | 0.231 | 0.0371 | 0.5589 |
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+ | No log | 8.0 | 64 | 1.1879 | 0.0692 | 0.1595 | 0.0365 | 0.0692 | -1.0 | -1.0 | 0.1132 | 0.4274 | 0.568 | 0.568 | -1.0 | -1.0 | 0.1319 | 0.6809 | 0.0434 | 0.6389 | 0.0953 | 0.5958 | 0.0216 | 0.3414 | 0.0536 | 0.5831 |
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+ | No log | 9.0 | 72 | 1.0866 | 0.0734 | 0.1604 | 0.0531 | 0.0734 | -1.0 | -1.0 | 0.1024 | 0.5221 | 0.6347 | 0.6347 | -1.0 | -1.0 | 0.0973 | 0.717 | 0.0706 | 0.6981 | 0.091 | 0.6146 | 0.0312 | 0.5034 | 0.077 | 0.6403 |
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+ | No log | 10.0 | 80 | 1.1141 | 0.0838 | 0.1778 | 0.0581 | 0.0838 | -1.0 | -1.0 | 0.1141 | 0.477 | 0.5995 | 0.5995 | -1.0 | -1.0 | 0.1254 | 0.7106 | 0.0469 | 0.5296 | 0.1313 | 0.6271 | 0.0639 | 0.4828 | 0.0517 | 0.6476 |
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+ | No log | 11.0 | 88 | 1.0441 | 0.1081 | 0.2059 | 0.113 | 0.1081 | -1.0 | -1.0 | 0.1468 | 0.5476 | 0.6598 | 0.6598 | -1.0 | -1.0 | 0.123 | 0.7596 | 0.0994 | 0.6926 | 0.2022 | 0.6979 | 0.0644 | 0.531 | 0.0513 | 0.6177 |
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+ | No log | 12.0 | 96 | 1.0162 | 0.0925 | 0.1924 | 0.0722 | 0.0925 | -1.0 | -1.0 | 0.1406 | 0.5546 | 0.6588 | 0.6588 | -1.0 | -1.0 | 0.1207 | 0.7447 | 0.0741 | 0.6481 | 0.1138 | 0.6708 | 0.0622 | 0.5586 | 0.0917 | 0.6718 |
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+ | No log | 13.0 | 104 | 1.0286 | 0.0654 | 0.1532 | 0.0471 | 0.0654 | -1.0 | -1.0 | 0.1208 | 0.4983 | 0.6221 | 0.6221 | -1.0 | -1.0 | 0.0899 | 0.7106 | 0.0484 | 0.613 | 0.0739 | 0.6313 | 0.0314 | 0.5103 | 0.0833 | 0.6452 |
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+ | No log | 14.0 | 112 | 1.0095 | 0.0779 | 0.1526 | 0.0658 | 0.078 | -1.0 | -1.0 | 0.1414 | 0.5457 | 0.6401 | 0.6401 | -1.0 | -1.0 | 0.0886 | 0.6894 | 0.0907 | 0.6981 | 0.088 | 0.6458 | 0.0389 | 0.5517 | 0.0834 | 0.6153 |
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+ | No log | 15.0 | 120 | 1.0553 | 0.0679 | 0.1489 | 0.0442 | 0.0679 | -1.0 | -1.0 | 0.0927 | 0.5145 | 0.6169 | 0.6169 | -1.0 | -1.0 | 0.0746 | 0.6766 | 0.0705 | 0.6204 | 0.0702 | 0.5854 | 0.0312 | 0.5931 | 0.0929 | 0.6089 |
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+ | No log | 16.0 | 128 | 0.9701 | 0.0871 | 0.1632 | 0.0832 | 0.0871 | -1.0 | -1.0 | 0.1328 | 0.5955 | 0.6803 | 0.6803 | -1.0 | -1.0 | 0.0797 | 0.734 | 0.0975 | 0.7315 | 0.0975 | 0.7167 | 0.0246 | 0.5862 | 0.136 | 0.6331 |
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+ | No log | 17.0 | 136 | 0.9433 | 0.0939 | 0.1694 | 0.0913 | 0.0939 | -1.0 | -1.0 | 0.1462 | 0.5953 | 0.6828 | 0.6828 | -1.0 | -1.0 | 0.0782 | 0.7234 | 0.1164 | 0.7685 | 0.0954 | 0.7167 | 0.0236 | 0.5448 | 0.156 | 0.6605 |
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+ | No log | 18.0 | 144 | 0.9000 | 0.1041 | 0.1756 | 0.116 | 0.1041 | -1.0 | -1.0 | 0.1777 | 0.6134 | 0.707 | 0.707 | -1.0 | -1.0 | 0.0824 | 0.7574 | 0.1205 | 0.7981 | 0.1057 | 0.7521 | 0.0248 | 0.5241 | 0.1868 | 0.7032 |
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+ | No log | 19.0 | 152 | 0.8657 | 0.1087 | 0.1706 | 0.1245 | 0.1087 | -1.0 | -1.0 | 0.1802 | 0.6586 | 0.7418 | 0.7418 | -1.0 | -1.0 | 0.0928 | 0.8255 | 0.0946 | 0.8037 | 0.116 | 0.7958 | 0.0286 | 0.5793 | 0.2114 | 0.7048 |
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+ | No log | 20.0 | 160 | 0.8645 | 0.1105 | 0.1855 | 0.1279 | 0.1105 | -1.0 | -1.0 | 0.1855 | 0.658 | 0.7452 | 0.7452 | -1.0 | -1.0 | 0.1074 | 0.8191 | 0.0912 | 0.8167 | 0.1259 | 0.7604 | 0.0375 | 0.6207 | 0.1906 | 0.7089 |
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+ | No log | 21.0 | 168 | 0.8434 | 0.1124 | 0.1785 | 0.1309 | 0.1124 | -1.0 | -1.0 | 0.2117 | 0.6712 | 0.7628 | 0.7628 | -1.0 | -1.0 | 0.1133 | 0.8404 | 0.0842 | 0.8167 | 0.1361 | 0.7833 | 0.0437 | 0.6552 | 0.1846 | 0.7185 |
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+ | No log | 22.0 | 176 | 0.8429 | 0.1141 | 0.1797 | 0.1332 | 0.1141 | -1.0 | -1.0 | 0.2087 | 0.6945 | 0.7789 | 0.7789 | -1.0 | -1.0 | 0.1176 | 0.817 | 0.0833 | 0.8278 | 0.1357 | 0.7979 | 0.0501 | 0.7379 | 0.1835 | 0.7137 |
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+ | No log | 23.0 | 184 | 0.8639 | 0.1106 | 0.1817 | 0.1213 | 0.1106 | -1.0 | -1.0 | 0.2023 | 0.6686 | 0.7507 | 0.7507 | -1.0 | -1.0 | 0.1275 | 0.8106 | 0.0756 | 0.7907 | 0.1272 | 0.7563 | 0.0457 | 0.6966 | 0.1767 | 0.6992 |
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+ | No log | 24.0 | 192 | 0.8237 | 0.1255 | 0.1907 | 0.1466 | 0.1255 | -1.0 | -1.0 | 0.2142 | 0.7028 | 0.7838 | 0.7838 | -1.0 | -1.0 | 0.1297 | 0.8277 | 0.0963 | 0.8352 | 0.1492 | 0.7979 | 0.0515 | 0.7241 | 0.2011 | 0.7339 |
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+ | No log | 25.0 | 200 | 0.8217 | 0.1305 | 0.1935 | 0.1579 | 0.1305 | -1.0 | -1.0 | 0.2163 | 0.7144 | 0.7838 | 0.7838 | -1.0 | -1.0 | 0.1317 | 0.8319 | 0.1076 | 0.8315 | 0.1577 | 0.7958 | 0.0541 | 0.7241 | 0.2014 | 0.7355 |
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+ | No log | 26.0 | 208 | 0.8197 | 0.1292 | 0.1955 | 0.1509 | 0.1292 | -1.0 | -1.0 | 0.2208 | 0.7083 | 0.7781 | 0.7781 | -1.0 | -1.0 | 0.1337 | 0.8277 | 0.1035 | 0.8389 | 0.1536 | 0.7854 | 0.0532 | 0.7 | 0.2023 | 0.7387 |
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+ | No log | 27.0 | 216 | 0.8266 | 0.1273 | 0.1942 | 0.1488 | 0.1273 | -1.0 | -1.0 | 0.2247 | 0.6947 | 0.7743 | 0.7743 | -1.0 | -1.0 | 0.1336 | 0.8277 | 0.1022 | 0.8315 | 0.1437 | 0.7792 | 0.0564 | 0.7 | 0.2004 | 0.7331 |
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+ | No log | 28.0 | 224 | 0.8207 | 0.1276 | 0.1941 | 0.1488 | 0.1276 | -1.0 | -1.0 | 0.2242 | 0.6992 | 0.7773 | 0.7773 | -1.0 | -1.0 | 0.1347 | 0.8319 | 0.1036 | 0.8296 | 0.1443 | 0.7833 | 0.0564 | 0.7069 | 0.1992 | 0.7347 |
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+ | No log | 29.0 | 232 | 0.8158 | 0.129 | 0.1942 | 0.1497 | 0.129 | -1.0 | -1.0 | 0.2275 | 0.7027 | 0.7851 | 0.7851 | -1.0 | -1.0 | 0.1364 | 0.8383 | 0.1038 | 0.8389 | 0.1451 | 0.7917 | 0.0569 | 0.7138 | 0.2028 | 0.7427 |
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+ | No log | 30.0 | 240 | 0.8155 | 0.1291 | 0.1942 | 0.1499 | 0.1291 | -1.0 | -1.0 | 0.2282 | 0.7027 | 0.7855 | 0.7855 | -1.0 | -1.0 | 0.1364 | 0.8404 | 0.1038 | 0.8389 | 0.1453 | 0.7917 | 0.0569 | 0.7138 | 0.2029 | 0.7427 |
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  ### Framework versions
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