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sashes_model

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Eval Results
Inference Endpoints
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  1. README.md +86 -77
  2. model.safetensors +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9090909090909091
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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
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1787
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- - Accuracy: 0.9091
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  ## Model description
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@@ -61,85 +61,94 @@ 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 | 0.7273 | 2 | 0.2246 | 0.9212 |
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- | No log | 1.8182 | 5 | 0.1985 | 0.9333 |
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- | No log | 2.9091 | 8 | 0.3016 | 0.8788 |
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- | 0.1764 | 4.0 | 11 | 0.1781 | 0.9455 |
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- | 0.1764 | 4.7273 | 13 | 0.2224 | 0.9091 |
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- | 0.1764 | 5.8182 | 16 | 0.1740 | 0.9394 |
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- | 0.1764 | 6.9091 | 19 | 0.2611 | 0.8848 |
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- | 0.1981 | 8.0 | 22 | 0.2415 | 0.9212 |
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- | 0.1981 | 8.7273 | 24 | 0.2253 | 0.9091 |
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- | 0.1981 | 9.8182 | 27 | 0.2396 | 0.9030 |
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- | 0.1993 | 10.9091 | 30 | 0.2822 | 0.9030 |
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- | 0.1993 | 12.0 | 33 | 0.2739 | 0.8788 |
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- | 0.1993 | 12.7273 | 35 | 0.1977 | 0.9333 |
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- | 0.1993 | 13.8182 | 38 | 0.1926 | 0.9091 |
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- | 0.178 | 14.9091 | 41 | 0.2566 | 0.9091 |
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- | 0.178 | 16.0 | 44 | 0.2286 | 0.9030 |
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- | 0.178 | 16.7273 | 46 | 0.2647 | 0.9152 |
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- | 0.178 | 17.8182 | 49 | 0.2424 | 0.8970 |
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- | 0.1947 | 18.9091 | 52 | 0.2525 | 0.8909 |
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- | 0.1947 | 20.0 | 55 | 0.2102 | 0.9212 |
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- | 0.1947 | 20.7273 | 57 | 0.2037 | 0.9273 |
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- | 0.1997 | 21.8182 | 60 | 0.1405 | 0.9576 |
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- | 0.1997 | 22.9091 | 63 | 0.2292 | 0.8970 |
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- | 0.1997 | 24.0 | 66 | 0.1884 | 0.9394 |
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- | 0.1997 | 24.7273 | 68 | 0.2245 | 0.8970 |
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- | 0.1853 | 25.8182 | 71 | 0.1941 | 0.9455 |
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- | 0.1853 | 26.9091 | 74 | 0.1821 | 0.9455 |
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- | 0.1853 | 28.0 | 77 | 0.1761 | 0.9455 |
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- | 0.1853 | 28.7273 | 79 | 0.1923 | 0.9212 |
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- | 0.1779 | 29.8182 | 82 | 0.2422 | 0.8909 |
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- | 0.1779 | 30.9091 | 85 | 0.2035 | 0.9394 |
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- | 0.1779 | 32.0 | 88 | 0.2369 | 0.9212 |
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- | 0.1944 | 32.7273 | 90 | 0.1982 | 0.9091 |
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- | 0.1944 | 33.8182 | 93 | 0.1995 | 0.9152 |
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- | 0.1944 | 34.9091 | 96 | 0.1886 | 0.9333 |
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- | 0.1944 | 36.0 | 99 | 0.1938 | 0.9333 |
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- | 0.1183 | 36.7273 | 101 | 0.1553 | 0.9394 |
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- | 0.1183 | 37.8182 | 104 | 0.2268 | 0.8970 |
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- | 0.1183 | 38.9091 | 107 | 0.1980 | 0.9152 |
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- | 0.1647 | 40.0 | 110 | 0.1553 | 0.9333 |
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- | 0.1647 | 40.7273 | 112 | 0.1020 | 0.9697 |
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- | 0.1647 | 41.8182 | 115 | 0.2174 | 0.9152 |
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- | 0.1647 | 42.9091 | 118 | 0.1499 | 0.9333 |
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- | 0.1321 | 44.0 | 121 | 0.1632 | 0.9576 |
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- | 0.1321 | 44.7273 | 123 | 0.1793 | 0.9333 |
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- | 0.1321 | 45.8182 | 126 | 0.2242 | 0.9091 |
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- | 0.1321 | 46.9091 | 129 | 0.2656 | 0.9030 |
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- | 0.1616 | 48.0 | 132 | 0.1614 | 0.9273 |
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- | 0.1616 | 48.7273 | 134 | 0.1677 | 0.9333 |
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- | 0.1616 | 49.8182 | 137 | 0.1946 | 0.9333 |
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- | 0.1682 | 50.9091 | 140 | 0.1380 | 0.9576 |
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- | 0.1682 | 52.0 | 143 | 0.2623 | 0.8848 |
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- | 0.1682 | 52.7273 | 145 | 0.1565 | 0.9455 |
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- | 0.1682 | 53.8182 | 148 | 0.1886 | 0.9152 |
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- | 0.1719 | 54.9091 | 151 | 0.1943 | 0.9152 |
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- | 0.1719 | 56.0 | 154 | 0.2413 | 0.8909 |
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- | 0.1719 | 56.7273 | 156 | 0.1563 | 0.9394 |
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- | 0.1719 | 57.8182 | 159 | 0.1194 | 0.9636 |
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- | 0.1775 | 58.9091 | 162 | 0.1687 | 0.9333 |
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- | 0.1775 | 60.0 | 165 | 0.1041 | 0.9818 |
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- | 0.1775 | 60.7273 | 167 | 0.2387 | 0.8848 |
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- | 0.1636 | 61.8182 | 170 | 0.1202 | 0.9576 |
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- | 0.1636 | 62.9091 | 173 | 0.1462 | 0.9333 |
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- | 0.1636 | 64.0 | 176 | 0.1630 | 0.9394 |
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- | 0.1636 | 64.7273 | 178 | 0.2130 | 0.9152 |
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- | 0.1841 | 65.8182 | 181 | 0.1948 | 0.8970 |
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- | 0.1841 | 66.9091 | 184 | 0.1859 | 0.9333 |
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- | 0.1841 | 68.0 | 187 | 0.2148 | 0.9152 |
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- | 0.1841 | 68.7273 | 189 | 0.1626 | 0.9515 |
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- | 0.1586 | 69.8182 | 192 | 0.1820 | 0.9212 |
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- | 0.1586 | 70.9091 | 195 | 0.1124 | 0.9455 |
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- | 0.1586 | 72.0 | 198 | 0.1298 | 0.9394 |
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- | 0.1383 | 72.7273 | 200 | 0.1787 | 0.9091 |
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9393939393939394
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  ---
27
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
32
 
33
  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1902
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+ - Accuracy: 0.9394
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  ## Model description
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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: 112
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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 | 0.7273 | 2 | 1.1089 | 0.2909 |
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+ | No log | 1.8182 | 5 | 1.0961 | 0.3758 |
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+ | No log | 2.9091 | 8 | 1.0822 | 0.3758 |
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+ | 1.0988 | 4.0 | 11 | 1.0480 | 0.5939 |
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+ | 1.0988 | 4.7273 | 13 | 1.0333 | 0.6061 |
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+ | 1.0988 | 5.8182 | 16 | 0.9881 | 0.6667 |
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+ | 1.0988 | 6.9091 | 19 | 0.9274 | 0.6970 |
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+ | 1.0061 | 8.0 | 22 | 0.8714 | 0.8364 |
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+ | 1.0061 | 8.7273 | 24 | 0.8210 | 0.7879 |
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+ | 1.0061 | 9.8182 | 27 | 0.7619 | 0.8121 |
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+ | 0.8136 | 10.9091 | 30 | 0.6483 | 0.8667 |
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+ | 0.8136 | 12.0 | 33 | 0.6937 | 0.7939 |
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+ | 0.8136 | 12.7273 | 35 | 0.5885 | 0.8545 |
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+ | 0.8136 | 13.8182 | 38 | 0.6046 | 0.8182 |
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+ | 0.642 | 14.9091 | 41 | 0.5518 | 0.8364 |
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+ | 0.642 | 16.0 | 44 | 0.5370 | 0.8242 |
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+ | 0.642 | 16.7273 | 46 | 0.4765 | 0.8788 |
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+ | 0.642 | 17.8182 | 49 | 0.4416 | 0.8606 |
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+ | 0.5145 | 18.9091 | 52 | 0.4140 | 0.8970 |
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+ | 0.5145 | 20.0 | 55 | 0.4007 | 0.8970 |
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+ | 0.5145 | 20.7273 | 57 | 0.3803 | 0.8970 |
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+ | 0.4226 | 21.8182 | 60 | 0.3167 | 0.9394 |
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+ | 0.4226 | 22.9091 | 63 | 0.3398 | 0.9030 |
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+ | 0.4226 | 24.0 | 66 | 0.3147 | 0.9273 |
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+ | 0.4226 | 24.7273 | 68 | 0.3273 | 0.8970 |
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+ | 0.3282 | 25.8182 | 71 | 0.3125 | 0.9030 |
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+ | 0.3282 | 26.9091 | 74 | 0.2712 | 0.9212 |
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+ | 0.3282 | 28.0 | 77 | 0.2871 | 0.9273 |
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+ | 0.3282 | 28.7273 | 79 | 0.2534 | 0.9273 |
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+ | 0.3076 | 29.8182 | 82 | 0.2620 | 0.9273 |
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+ | 0.3076 | 30.9091 | 85 | 0.3845 | 0.8848 |
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+ | 0.3076 | 32.0 | 88 | 0.2495 | 0.9273 |
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+ | 0.3081 | 32.7273 | 90 | 0.3018 | 0.9091 |
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+ | 0.3081 | 33.8182 | 93 | 0.2204 | 0.9455 |
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+ | 0.3081 | 34.9091 | 96 | 0.2769 | 0.9152 |
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+ | 0.3081 | 36.0 | 99 | 0.2261 | 0.9394 |
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+ | 0.2451 | 36.7273 | 101 | 0.2092 | 0.9515 |
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+ | 0.2451 | 37.8182 | 104 | 0.3196 | 0.8727 |
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+ | 0.2451 | 38.9091 | 107 | 0.2629 | 0.9091 |
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+ | 0.2741 | 40.0 | 110 | 0.2360 | 0.9333 |
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+ | 0.2741 | 40.7273 | 112 | 0.1927 | 0.9515 |
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+ | 0.2741 | 41.8182 | 115 | 0.2834 | 0.9030 |
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+ | 0.2741 | 42.9091 | 118 | 0.2173 | 0.9394 |
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+ | 0.244 | 44.0 | 121 | 0.1997 | 0.9394 |
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+ | 0.244 | 44.7273 | 123 | 0.2163 | 0.9273 |
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+ | 0.244 | 45.8182 | 126 | 0.2865 | 0.8970 |
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+ | 0.244 | 46.9091 | 129 | 0.2483 | 0.9152 |
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+ | 0.224 | 48.0 | 132 | 0.1707 | 0.9576 |
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+ | 0.224 | 48.7273 | 134 | 0.1988 | 0.9455 |
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+ | 0.224 | 49.8182 | 137 | 0.2168 | 0.9455 |
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+ | 0.213 | 50.9091 | 140 | 0.1807 | 0.9576 |
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+ | 0.213 | 52.0 | 143 | 0.2478 | 0.9152 |
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+ | 0.213 | 52.7273 | 145 | 0.1975 | 0.9455 |
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+ | 0.213 | 53.8182 | 148 | 0.2218 | 0.9212 |
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+ | 0.2298 | 54.9091 | 151 | 0.2046 | 0.9455 |
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+ | 0.2298 | 56.0 | 154 | 0.2557 | 0.9152 |
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+ | 0.2298 | 56.7273 | 156 | 0.1962 | 0.9394 |
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+ | 0.2298 | 57.8182 | 159 | 0.1879 | 0.9394 |
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+ | 0.2189 | 58.9091 | 162 | 0.1983 | 0.9576 |
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+ | 0.2189 | 60.0 | 165 | 0.1285 | 0.9697 |
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+ | 0.2189 | 60.7273 | 167 | 0.2227 | 0.9212 |
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+ | 0.211 | 61.8182 | 170 | 0.1671 | 0.9515 |
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+ | 0.211 | 62.9091 | 173 | 0.1489 | 0.9636 |
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+ | 0.211 | 64.0 | 176 | 0.1842 | 0.9394 |
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+ | 0.211 | 64.7273 | 178 | 0.1687 | 0.9636 |
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+ | 0.1834 | 65.8182 | 181 | 0.2118 | 0.9091 |
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+ | 0.1834 | 66.9091 | 184 | 0.2191 | 0.9273 |
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+ | 0.1834 | 68.0 | 187 | 0.2014 | 0.9273 |
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+ | 0.1834 | 68.7273 | 189 | 0.1861 | 0.9515 |
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+ | 0.1846 | 69.8182 | 192 | 0.1309 | 0.9758 |
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+ | 0.1846 | 70.9091 | 195 | 0.1236 | 0.9636 |
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+ | 0.1846 | 72.0 | 198 | 0.1541 | 0.9455 |
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+ | 0.1581 | 72.7273 | 200 | 0.1577 | 0.9576 |
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+ | 0.1581 | 73.8182 | 203 | 0.1927 | 0.9273 |
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+ | 0.1581 | 74.9091 | 206 | 0.2247 | 0.9273 |
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+ | 0.1581 | 76.0 | 209 | 0.1811 | 0.9576 |
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+ | 0.1742 | 76.7273 | 211 | 0.2190 | 0.9273 |
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+ | 0.1742 | 77.8182 | 214 | 0.1487 | 0.9697 |
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+ | 0.1742 | 78.9091 | 217 | 0.1836 | 0.9576 |
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+ | 0.1837 | 80.0 | 220 | 0.1228 | 0.9758 |
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+ | 0.1837 | 80.7273 | 222 | 0.1400 | 0.9636 |
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+ | 0.1837 | 81.4545 | 224 | 0.1902 | 0.9394 |
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  ### Framework versions
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