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Melo1512
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vit-msn-small-corect_cleaned_dataset_lateral_flow_ivalidation

Image Classification
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vit_msn
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  1. README.md +24 -98
  2. model.safetensors +1 -1
  3. runs/Jan14_16-39-32_c583982b4f3d/events.out.tfevents.1736872779.c583982b4f3d.215.4 +2 -2
README.md CHANGED
@@ -18,12 +18,12 @@ model-index:
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  name: imagefolder
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  type: imagefolder
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  config: default
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- split: validation
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  args: default
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9489402697495183
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -33,8 +33,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/vit-msn-small](https://huggingface.co/facebook/vit-msn-small) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1610
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- - Accuracy: 0.9489
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  ## Model description
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@@ -62,105 +62,31 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 100
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|
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- | No log | 0.9231 | 3 | 0.8617 | 0.1493 |
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- | No log | 1.8462 | 6 | 0.4977 | 0.8796 |
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- | No log | 2.7692 | 9 | 0.6143 | 0.7274 |
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- | 0.6181 | 4.0 | 13 | 0.4685 | 0.8324 |
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- | 0.6181 | 4.9231 | 16 | 0.3828 | 0.8622 |
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- | 0.6181 | 5.8462 | 19 | 0.4028 | 0.8497 |
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- | 0.3645 | 6.7692 | 22 | 0.2485 | 0.9210 |
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- | 0.3645 | 8.0 | 26 | 0.2426 | 0.9123 |
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- | 0.3645 | 8.9231 | 29 | 0.5674 | 0.7775 |
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- | 0.3492 | 9.8462 | 32 | 0.1610 | 0.9489 |
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- | 0.3492 | 10.7692 | 35 | 0.3225 | 0.9171 |
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- | 0.3492 | 12.0 | 39 | 0.2904 | 0.9123 |
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- | 0.3472 | 12.9231 | 42 | 0.2851 | 0.9133 |
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- | 0.3472 | 13.8462 | 45 | 0.3330 | 0.8931 |
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- | 0.3472 | 14.7692 | 48 | 0.6116 | 0.7389 |
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- | 0.2838 | 16.0 | 52 | 0.2677 | 0.9046 |
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- | 0.2838 | 16.9231 | 55 | 0.3216 | 0.8825 |
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- | 0.2838 | 17.8462 | 58 | 0.2165 | 0.9258 |
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- | 0.292 | 18.7692 | 61 | 0.4377 | 0.8333 |
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- | 0.292 | 20.0 | 65 | 0.3592 | 0.8699 |
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- | 0.292 | 20.9231 | 68 | 0.5011 | 0.7987 |
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- | 0.2809 | 21.8462 | 71 | 0.2319 | 0.9162 |
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- | 0.2809 | 22.7692 | 74 | 0.4018 | 0.8449 |
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- | 0.2809 | 24.0 | 78 | 0.4851 | 0.7996 |
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- | 0.251 | 24.9231 | 81 | 0.4668 | 0.8276 |
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- | 0.251 | 25.8462 | 84 | 0.4974 | 0.8179 |
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- | 0.251 | 26.7692 | 87 | 0.5482 | 0.7890 |
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- | 0.2371 | 28.0 | 91 | 0.6840 | 0.7370 |
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- | 0.2371 | 28.9231 | 94 | 0.3629 | 0.8613 |
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- | 0.2371 | 29.8462 | 97 | 0.6212 | 0.7331 |
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- | 0.2416 | 30.7692 | 100 | 0.3657 | 0.8642 |
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- | 0.2416 | 32.0 | 104 | 0.5857 | 0.7649 |
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- | 0.2416 | 32.9231 | 107 | 0.3610 | 0.8565 |
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- | 0.2312 | 33.8462 | 110 | 0.8753 | 0.6358 |
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- | 0.2312 | 34.7692 | 113 | 0.4993 | 0.7977 |
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- | 0.2312 | 36.0 | 117 | 0.4702 | 0.8131 |
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- | 0.2131 | 36.9231 | 120 | 0.3648 | 0.8584 |
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- | 0.2131 | 37.8462 | 123 | 0.7660 | 0.7062 |
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- | 0.2131 | 38.7692 | 126 | 0.4444 | 0.8304 |
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- | 0.2248 | 40.0 | 130 | 0.7568 | 0.7206 |
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- | 0.2248 | 40.9231 | 133 | 0.6134 | 0.7746 |
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- | 0.2248 | 41.8462 | 136 | 0.3969 | 0.8372 |
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- | 0.2248 | 42.7692 | 139 | 0.6100 | 0.7428 |
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- | 0.2341 | 44.0 | 143 | 0.6376 | 0.7486 |
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- | 0.2341 | 44.9231 | 146 | 0.8082 | 0.6965 |
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- | 0.2341 | 45.8462 | 149 | 0.5552 | 0.7987 |
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- | 0.1998 | 46.7692 | 152 | 0.5736 | 0.7784 |
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- | 0.1998 | 48.0 | 156 | 0.4477 | 0.8179 |
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- | 0.1998 | 48.9231 | 159 | 0.4925 | 0.8064 |
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- | 0.2075 | 49.8462 | 162 | 0.6641 | 0.7408 |
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- | 0.2075 | 50.7692 | 165 | 0.6718 | 0.7418 |
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- | 0.2075 | 52.0 | 169 | 0.4913 | 0.8170 |
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- | 0.197 | 52.9231 | 172 | 0.5316 | 0.7967 |
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- | 0.197 | 53.8462 | 175 | 0.7917 | 0.7033 |
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- | 0.197 | 54.7692 | 178 | 0.8232 | 0.6850 |
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- | 0.1769 | 56.0 | 182 | 0.8841 | 0.6753 |
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- | 0.1769 | 56.9231 | 185 | 0.7670 | 0.7206 |
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- | 0.1769 | 57.8462 | 188 | 0.7893 | 0.7168 |
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- | 0.1735 | 58.7692 | 191 | 1.1965 | 0.6002 |
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- | 0.1735 | 60.0 | 195 | 1.0561 | 0.6570 |
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- | 0.1735 | 60.9231 | 198 | 0.7164 | 0.7408 |
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- | 0.1905 | 61.8462 | 201 | 0.6160 | 0.7611 |
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- | 0.1905 | 62.7692 | 204 | 0.4964 | 0.8006 |
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- | 0.1905 | 64.0 | 208 | 0.6949 | 0.7370 |
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- | 0.1748 | 64.9231 | 211 | 0.5145 | 0.8044 |
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- | 0.1748 | 65.8462 | 214 | 0.6397 | 0.7707 |
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- | 0.1748 | 66.7692 | 217 | 0.5984 | 0.7900 |
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- | 0.1535 | 68.0 | 221 | 0.4233 | 0.8459 |
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- | 0.1535 | 68.9231 | 224 | 0.4464 | 0.8343 |
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- | 0.1535 | 69.8462 | 227 | 0.3953 | 0.8497 |
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- | 0.1633 | 70.7692 | 230 | 0.4314 | 0.8314 |
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- | 0.1633 | 72.0 | 234 | 0.5035 | 0.8025 |
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- | 0.1633 | 72.9231 | 237 | 0.5387 | 0.7803 |
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- | 0.145 | 73.8462 | 240 | 0.5016 | 0.8025 |
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- | 0.145 | 74.7692 | 243 | 0.4606 | 0.8160 |
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- | 0.145 | 76.0 | 247 | 0.6732 | 0.7524 |
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- | 0.1584 | 76.9231 | 250 | 0.6854 | 0.7524 |
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- | 0.1584 | 77.8462 | 253 | 0.6868 | 0.7572 |
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- | 0.1584 | 78.7692 | 256 | 0.6765 | 0.7582 |
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- | 0.1423 | 80.0 | 260 | 0.6295 | 0.7832 |
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- | 0.1423 | 80.9231 | 263 | 0.6124 | 0.7909 |
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- | 0.1423 | 81.8462 | 266 | 0.6027 | 0.7881 |
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- | 0.1423 | 82.7692 | 269 | 0.6008 | 0.7861 |
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- | 0.1449 | 84.0 | 273 | 0.6533 | 0.7688 |
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- | 0.1449 | 84.9231 | 276 | 0.6304 | 0.7697 |
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- | 0.1449 | 85.8462 | 279 | 0.5607 | 0.7996 |
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- | 0.1452 | 86.7692 | 282 | 0.5739 | 0.7929 |
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- | 0.1452 | 88.0 | 286 | 0.6115 | 0.7765 |
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- | 0.1452 | 88.9231 | 289 | 0.6277 | 0.7726 |
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- | 0.1232 | 89.8462 | 292 | 0.6273 | 0.7784 |
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- | 0.1232 | 90.7692 | 295 | 0.6300 | 0.7775 |
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- | 0.1232 | 92.0 | 299 | 0.6361 | 0.7765 |
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- | 0.1494 | 92.3077 | 300 | 0.6359 | 0.7765 |
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  ### Framework versions
 
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  name: imagefolder
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  type: imagefolder
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  config: default
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+ split: test
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  args: default
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9084249084249084
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  ---
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29
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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34
  This model is a fine-tuned version of [facebook/vit-msn-small](https://huggingface.co/facebook/vit-msn-small) on the imagefolder dataset.
35
  It achieves the following results on the evaluation set:
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+ - Loss: 0.2305
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+ - Accuracy: 0.9084
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  ## Model description
40
 
 
62
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
63
  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 20
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67
  ### Training results
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69
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|
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+ | No log | 0.9231 | 3 | 0.6468 | 0.5604 |
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+ | No log | 1.8462 | 6 | 0.4227 | 0.8462 |
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+ | No log | 2.7692 | 9 | 0.3390 | 0.8608 |
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+ | 0.5336 | 4.0 | 13 | 0.3115 | 0.8864 |
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+ | 0.5336 | 4.9231 | 16 | 0.2986 | 0.8938 |
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+ | 0.5336 | 5.8462 | 19 | 0.2318 | 0.9231 |
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+ | 0.3565 | 6.7692 | 22 | 0.2767 | 0.9121 |
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+ | 0.3565 | 8.0 | 26 | 0.2490 | 0.9084 |
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+ | 0.3565 | 8.9231 | 29 | 0.3151 | 0.8938 |
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+ | 0.3166 | 9.8462 | 32 | 0.2404 | 0.9231 |
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+ | 0.3166 | 10.7692 | 35 | 0.2520 | 0.9158 |
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+ | 0.3166 | 12.0 | 39 | 0.2515 | 0.9048 |
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+ | 0.2657 | 12.9231 | 42 | 0.2344 | 0.9121 |
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+ | 0.2657 | 13.8462 | 45 | 0.2187 | 0.9194 |
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+ | 0.2657 | 14.7692 | 48 | 0.2289 | 0.9194 |
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+ | 0.259 | 16.0 | 52 | 0.2251 | 0.9194 |
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+ | 0.259 | 16.9231 | 55 | 0.2238 | 0.9231 |
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+ | 0.259 | 17.8462 | 58 | 0.2312 | 0.9121 |
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+ | 0.2514 | 18.4615 | 60 | 0.2305 | 0.9084 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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