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KanWasTaken
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UNetOscillatoryNeuron

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UNetOscillatoryNeuron
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  2. model.safetensors +1 -1
README.md CHANGED
@@ -17,11 +17,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 3.7960
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- - Dice: 0.4819
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- - Iou: 0.3220
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- - Precision: 0.3221
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- - Recall: 0.9992
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  ## Model description
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@@ -40,12 +40,12 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.01
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  - train_batch_size: 8
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  - eval_batch_size: 8
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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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  - lr_scheduler_warmup_ratio: 0.05
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  - num_epochs: 50
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@@ -53,56 +53,56 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Dice | Iou | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|:------:|
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- | 0.6629 | 1.0 | 27 | 1.0761 | 0.6220 | 0.4539 | 0.5274 | 0.7807 |
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- | 0.0824 | 2.0 | 54 | 2.6095 | 0.4847 | 0.3248 | 0.3250 | 0.9989 |
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- | 0.0599 | 3.0 | 81 | 1.1832 | 0.6502 | 0.4912 | 0.5257 | 0.9234 |
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- | 0.0433 | 4.0 | 108 | 0.6347 | 0.7121 | 0.5568 | 0.6076 | 0.8862 |
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- | 0.0377 | 5.0 | 135 | 0.4589 | 0.7358 | 0.5849 | 0.6738 | 0.8398 |
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- | 0.0346 | 6.0 | 162 | 0.2619 | 0.7473 | 0.6010 | 0.7821 | 0.7346 |
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- | 0.0405 | 7.0 | 189 | 0.6009 | 0.6874 | 0.5273 | 0.5677 | 0.9005 |
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- | 0.0365 | 8.0 | 216 | 0.1796 | 0.7700 | 0.6334 | 0.8727 | 0.6996 |
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- | 0.0292 | 9.0 | 243 | 0.4657 | 0.7180 | 0.5644 | 0.6381 | 0.8580 |
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- | 0.0244 | 10.0 | 270 | 0.4219 | 0.7184 | 0.5652 | 0.6679 | 0.8029 |
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- | 0.0341 | 11.0 | 297 | 0.1566 | 0.7624 | 0.6229 | 0.8840 | 0.6795 |
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- | 0.063 | 12.0 | 324 | 1.2719 | 0.6887 | 0.5306 | 0.5455 | 0.9657 |
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- | 0.0921 | 13.0 | 351 | 0.6939 | 0.7049 | 0.5477 | 0.5990 | 0.8952 |
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- | 0.1498 | 14.0 | 378 | 0.6770 | 0.7466 | 0.5985 | 0.6553 | 0.8978 |
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- | 0.1376 | 15.0 | 405 | 6.0601 | 0.4263 | 0.2745 | 0.2745 | 1.0000 |
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- | 0.1153 | 16.0 | 432 | 7.4114 | 0.4068 | 0.2585 | 0.2585 | 1.0000 |
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- | 0.1002 | 17.0 | 459 | 4.9600 | 0.4500 | 0.2945 | 0.2945 | 0.9998 |
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- | 0.1005 | 18.0 | 486 | 3.8347 | 0.4785 | 0.3192 | 0.3193 | 0.9994 |
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- | 0.1013 | 19.0 | 513 | 5.3824 | 0.4298 | 0.2774 | 0.2774 | 0.9998 |
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- | 0.1137 | 20.0 | 540 | 4.5780 | 0.4536 | 0.2973 | 0.2974 | 0.9997 |
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- | 0.1183 | 21.0 | 567 | 4.4835 | 0.4493 | 0.2937 | 0.2937 | 0.9998 |
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- | 0.1327 | 22.0 | 594 | 3.5288 | 0.4896 | 0.3289 | 0.3291 | 0.9988 |
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- | 0.1329 | 23.0 | 621 | 3.3211 | 0.5030 | 0.3411 | 0.3415 | 0.9982 |
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- | 0.1344 | 24.0 | 648 | 3.8180 | 0.4797 | 0.3201 | 0.3203 | 0.9989 |
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- | 0.1273 | 25.0 | 675 | 4.5982 | 0.4478 | 0.2924 | 0.2925 | 0.9998 |
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- | 0.1275 | 26.0 | 702 | 4.1043 | 0.4658 | 0.3079 | 0.3080 | 0.9995 |
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- | 0.1354 | 27.0 | 729 | 3.8208 | 0.4807 | 0.3210 | 0.3212 | 0.9989 |
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- | 0.1361 | 28.0 | 756 | 3.6626 | 0.4884 | 0.3278 | 0.3281 | 0.9988 |
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- | 0.1347 | 29.0 | 783 | 3.4135 | 0.4996 | 0.3380 | 0.3382 | 0.9986 |
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- | 0.1309 | 30.0 | 810 | 3.8916 | 0.4758 | 0.3167 | 0.3169 | 0.9992 |
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- | 0.1321 | 31.0 | 837 | 3.6416 | 0.4875 | 0.3271 | 0.3273 | 0.9989 |
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- | 0.1284 | 32.0 | 864 | 4.1093 | 0.4674 | 0.3094 | 0.3095 | 0.9995 |
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- | 0.1281 | 33.0 | 891 | 3.6306 | 0.4889 | 0.3282 | 0.3284 | 0.9990 |
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- | 0.1243 | 34.0 | 918 | 3.9137 | 0.4763 | 0.3170 | 0.3171 | 0.9992 |
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- | 0.1251 | 35.0 | 945 | 3.9373 | 0.4760 | 0.3169 | 0.3170 | 0.9993 |
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- | 0.1268 | 36.0 | 972 | 4.0403 | 0.4696 | 0.3112 | 0.3113 | 0.9995 |
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- | 0.1251 | 37.0 | 999 | 3.9820 | 0.4735 | 0.3147 | 0.3148 | 0.9994 |
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- | 0.1254 | 38.0 | 1026 | 4.1664 | 0.4637 | 0.3061 | 0.3062 | 0.9996 |
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- | 0.1256 | 39.0 | 1053 | 3.8444 | 0.4770 | 0.3177 | 0.3178 | 0.9993 |
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- | 0.1247 | 40.0 | 1080 | 3.7488 | 0.4821 | 0.3222 | 0.3224 | 0.9991 |
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- | 0.123 | 41.0 | 1107 | 3.9956 | 0.4723 | 0.3135 | 0.3136 | 0.9994 |
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- | 0.121 | 42.0 | 1134 | 3.8087 | 0.4807 | 0.3209 | 0.3211 | 0.9991 |
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- | 0.1202 | 43.0 | 1161 | 3.9857 | 0.4723 | 0.3136 | 0.3137 | 0.9994 |
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- | 0.1202 | 44.0 | 1188 | 3.9039 | 0.4755 | 0.3164 | 0.3165 | 0.9993 |
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- | 0.1213 | 45.0 | 1215 | 3.6906 | 0.4852 | 0.3250 | 0.3252 | 0.9991 |
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- | 0.1198 | 46.0 | 1242 | 3.8138 | 0.4801 | 0.3205 | 0.3206 | 0.9992 |
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- | 0.1203 | 47.0 | 1269 | 3.7747 | 0.4818 | 0.3220 | 0.3222 | 0.9992 |
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- | 0.1201 | 48.0 | 1296 | 3.7842 | 0.4819 | 0.3220 | 0.3221 | 0.9991 |
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- | 0.1191 | 49.0 | 1323 | 3.7903 | 0.4818 | 0.3220 | 0.3222 | 0.9992 |
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- | 0.1198 | 50.0 | 1350 | 3.7960 | 0.4819 | 0.3220 | 0.3221 | 0.9992 |
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  ### Framework versions
 
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18
  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
19
  It achieves the following results on the evaluation set:
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+ - Loss: 0.0222
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+ - Dice: 0.6912
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+ - Iou: 0.5379
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+ - Precision: 0.9949
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+ - Recall: 0.5395
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  ## Model description
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40
  ### Training hyperparameters
41
 
42
  The following hyperparameters were used during training:
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+ - learning_rate: 0.001
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  - train_batch_size: 8
45
  - eval_batch_size: 8
46
  - 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: linear
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  - lr_scheduler_warmup_ratio: 0.05
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  - num_epochs: 50
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  | Training Loss | Epoch | Step | Validation Loss | Dice | Iou | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|:------:|
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+ | 0.9974 | 1.0 | 27 | 6.4241 | 0.4013 | 0.2537 | 0.2590 | 0.9319 |
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+ | 0.4597 | 2.0 | 54 | 0.5612 | 0.6393 | 0.4714 | 0.5705 | 0.7437 |
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+ | 0.1191 | 3.0 | 81 | 0.1381 | 0.7612 | 0.6225 | 0.8853 | 0.6797 |
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+ | 0.064 | 4.0 | 108 | 0.1400 | 0.7652 | 0.6276 | 0.8892 | 0.6825 |
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+ | 0.0473 | 5.0 | 135 | 0.0575 | 0.7339 | 0.5885 | 0.9563 | 0.6051 |
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+ | 0.0438 | 6.0 | 162 | 0.0864 | 0.7435 | 0.5995 | 0.9429 | 0.6212 |
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+ | 0.0366 | 7.0 | 189 | 0.0374 | 0.6901 | 0.5353 | 0.9899 | 0.5384 |
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+ | 0.0345 | 8.0 | 216 | 0.0530 | 0.7390 | 0.5943 | 0.9591 | 0.6092 |
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+ | 0.0326 | 9.0 | 243 | 0.0351 | 0.7177 | 0.5690 | 0.9799 | 0.5761 |
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+ | 0.0311 | 10.0 | 270 | 0.0345 | 0.7204 | 0.5714 | 0.9780 | 0.5787 |
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+ | 0.028 | 11.0 | 297 | 0.0582 | 0.7436 | 0.6005 | 0.9527 | 0.6192 |
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+ | 0.0272 | 12.0 | 324 | 0.0304 | 0.7126 | 0.5623 | 0.9838 | 0.5675 |
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+ | 0.0269 | 13.0 | 351 | 0.0291 | 0.7126 | 0.5626 | 0.9832 | 0.5682 |
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+ | 0.0269 | 14.0 | 378 | 0.0273 | 0.7053 | 0.5537 | 0.9886 | 0.5574 |
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+ | 0.0264 | 15.0 | 405 | 0.0290 | 0.7202 | 0.5717 | 0.9837 | 0.5772 |
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+ | 0.0238 | 16.0 | 432 | 0.0359 | 0.7309 | 0.5844 | 0.9743 | 0.5931 |
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+ | 0.0247 | 17.0 | 459 | 0.0272 | 0.7204 | 0.5718 | 0.9844 | 0.5770 |
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+ | 0.024 | 18.0 | 486 | 0.0419 | 0.7354 | 0.5902 | 0.9665 | 0.6029 |
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+ | 0.021 | 19.0 | 513 | 0.0457 | 0.7404 | 0.5963 | 0.9627 | 0.6103 |
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+ | 0.0217 | 20.0 | 540 | 0.0257 | 0.7176 | 0.5683 | 0.9855 | 0.5729 |
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+ | 0.0228 | 21.0 | 567 | 0.0293 | 0.7195 | 0.5708 | 0.9801 | 0.5777 |
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+ | 0.0218 | 22.0 | 594 | 0.0309 | 0.7271 | 0.5801 | 0.9782 | 0.5878 |
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+ | 0.0207 | 23.0 | 621 | 0.0305 | 0.7267 | 0.5797 | 0.9783 | 0.5873 |
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+ | 0.0192 | 24.0 | 648 | 0.0234 | 0.7141 | 0.5645 | 0.9881 | 0.5685 |
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+ | 0.019 | 25.0 | 675 | 0.0247 | 0.7192 | 0.5706 | 0.9860 | 0.5752 |
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+ | 0.0194 | 26.0 | 702 | 0.0251 | 0.7183 | 0.5695 | 0.9855 | 0.5744 |
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+ | 0.0194 | 27.0 | 729 | 0.0220 | 0.7051 | 0.5535 | 0.9914 | 0.5563 |
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+ | 0.0203 | 28.0 | 756 | 0.0237 | 0.7130 | 0.5635 | 0.9876 | 0.5678 |
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+ | 0.019 | 29.0 | 783 | 0.0230 | 0.7157 | 0.5666 | 0.9881 | 0.5703 |
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+ | 0.0176 | 30.0 | 810 | 0.0241 | 0.7198 | 0.5711 | 0.9865 | 0.5754 |
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+ | 0.0168 | 31.0 | 837 | 0.0261 | 0.7235 | 0.5759 | 0.9835 | 0.5815 |
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+ | 0.0169 | 32.0 | 864 | 0.0264 | 0.7213 | 0.5732 | 0.9826 | 0.5794 |
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+ | 0.0165 | 33.0 | 891 | 0.0243 | 0.7196 | 0.5710 | 0.9849 | 0.5760 |
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+ | 0.0163 | 34.0 | 918 | 0.0213 | 0.7033 | 0.5514 | 0.9915 | 0.5541 |
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+ | 0.0161 | 35.0 | 945 | 0.0214 | 0.7057 | 0.5544 | 0.9906 | 0.5574 |
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+ | 0.0153 | 36.0 | 972 | 0.0208 | 0.7093 | 0.5587 | 0.9908 | 0.5616 |
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+ | 0.0152 | 37.0 | 999 | 0.0218 | 0.7101 | 0.5595 | 0.9898 | 0.5627 |
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+ | 0.0151 | 38.0 | 1026 | 0.0224 | 0.7160 | 0.5666 | 0.9875 | 0.5707 |
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+ | 0.0145 | 39.0 | 1053 | 0.0204 | 0.7015 | 0.5497 | 0.9927 | 0.5521 |
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+ | 0.0143 | 40.0 | 1080 | 0.0208 | 0.7035 | 0.5519 | 0.9921 | 0.5545 |
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+ | 0.014 | 41.0 | 1107 | 0.0205 | 0.7015 | 0.5496 | 0.9932 | 0.5517 |
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+ | 0.0142 | 42.0 | 1134 | 0.0207 | 0.7027 | 0.5512 | 0.9924 | 0.5536 |
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+ | 0.014 | 43.0 | 1161 | 0.0214 | 0.7121 | 0.5623 | 0.9892 | 0.5658 |
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+ | 0.0136 | 44.0 | 1188 | 0.0213 | 0.6950 | 0.5423 | 0.9944 | 0.5441 |
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+ | 0.0134 | 45.0 | 1215 | 0.0212 | 0.6980 | 0.5456 | 0.9935 | 0.5477 |
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+ | 0.0133 | 46.0 | 1242 | 0.0214 | 0.6946 | 0.5420 | 0.9943 | 0.5438 |
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+ | 0.013 | 47.0 | 1269 | 0.0210 | 0.6959 | 0.5432 | 0.9945 | 0.5449 |
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+ | 0.013 | 48.0 | 1296 | 0.0221 | 0.6919 | 0.5387 | 0.9949 | 0.5403 |
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+ | 0.0129 | 49.0 | 1323 | 0.0219 | 0.6925 | 0.5393 | 0.9945 | 0.5410 |
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+ | 0.0125 | 50.0 | 1350 | 0.0222 | 0.6912 | 0.5379 | 0.9949 | 0.5395 |
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  ### Framework versions
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