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Melo1512
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vit-msn-small-wbc-blur-detector

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vit_msn
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  1. README.md +63 -63
  2. model.safetensors +1 -1
  3. runs/Dec11_16-13-35_ae1aa77fe319/events.out.tfevents.1733933627.ae1aa77fe319.236.4 +2 -2
README.md CHANGED
@@ -23,7 +23,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9565217391304348
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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: 0.1797
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- - Accuracy: 0.9565
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  ## Model description
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@@ -68,66 +68,66 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 3 | 0.6226 | 0.5761 |
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- | No log | 2.0 | 6 | 0.4175 | 0.8913 |
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- | No log | 3.0 | 9 | 0.3596 | 0.8370 |
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- | 0.5806 | 4.0 | 12 | 0.2136 | 0.9130 |
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- | 0.5806 | 5.0 | 15 | 0.1824 | 0.9348 |
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- | 0.5806 | 6.0 | 18 | 0.2122 | 0.9130 |
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- | 0.2353 | 7.0 | 21 | 0.2083 | 0.9457 |
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- | 0.2353 | 8.0 | 24 | 0.1385 | 0.9348 |
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- | 0.2353 | 9.0 | 27 | 0.1582 | 0.9457 |
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- | 0.1214 | 10.0 | 30 | 0.1802 | 0.9457 |
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- | 0.1214 | 11.0 | 33 | 0.1710 | 0.9457 |
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- | 0.1214 | 12.0 | 36 | 0.2765 | 0.9348 |
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- | 0.1214 | 13.0 | 39 | 0.2290 | 0.9457 |
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- | 0.0934 | 14.0 | 42 | 0.3126 | 0.9239 |
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- | 0.0934 | 15.0 | 45 | 0.1782 | 0.9457 |
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- | 0.0934 | 16.0 | 48 | 0.3799 | 0.8804 |
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- | 0.1525 | 17.0 | 51 | 0.1911 | 0.9457 |
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- | 0.1525 | 18.0 | 54 | 0.2292 | 0.9348 |
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- | 0.1525 | 19.0 | 57 | 0.3241 | 0.9130 |
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- | 0.1551 | 20.0 | 60 | 0.1797 | 0.9565 |
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- | 0.1551 | 21.0 | 63 | 0.2116 | 0.9239 |
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- | 0.1551 | 22.0 | 66 | 0.2775 | 0.9348 |
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- | 0.1551 | 23.0 | 69 | 0.2830 | 0.9239 |
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- | 0.0701 | 24.0 | 72 | 0.3331 | 0.9348 |
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- | 0.0701 | 25.0 | 75 | 0.2750 | 0.9348 |
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- | 0.0701 | 26.0 | 78 | 0.2003 | 0.9457 |
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- | 0.0733 | 27.0 | 81 | 0.2493 | 0.9457 |
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- | 0.0733 | 28.0 | 84 | 0.2808 | 0.9457 |
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- | 0.0733 | 29.0 | 87 | 0.2664 | 0.9457 |
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- | 0.0494 | 30.0 | 90 | 0.3621 | 0.9457 |
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- | 0.0494 | 31.0 | 93 | 0.2520 | 0.9457 |
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- | 0.0494 | 32.0 | 96 | 0.2722 | 0.9457 |
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- | 0.0494 | 33.0 | 99 | 0.2785 | 0.9457 |
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- | 0.0806 | 34.0 | 102 | 0.2530 | 0.9457 |
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- | 0.0806 | 35.0 | 105 | 0.2230 | 0.9457 |
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- | 0.0806 | 36.0 | 108 | 0.2483 | 0.9457 |
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- | 0.0324 | 37.0 | 111 | 0.2945 | 0.9457 |
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- | 0.0324 | 38.0 | 114 | 0.3244 | 0.9457 |
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- | 0.0324 | 39.0 | 117 | 0.3302 | 0.9457 |
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- | 0.0435 | 40.0 | 120 | 0.3315 | 0.9457 |
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- | 0.0435 | 41.0 | 123 | 0.3039 | 0.9348 |
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- | 0.0435 | 42.0 | 126 | 0.3605 | 0.9457 |
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- | 0.0435 | 43.0 | 129 | 0.3643 | 0.9348 |
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- | 0.0325 | 44.0 | 132 | 0.3468 | 0.9457 |
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- | 0.0325 | 45.0 | 135 | 0.3328 | 0.9348 |
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- | 0.0325 | 46.0 | 138 | 0.3261 | 0.9348 |
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- | 0.0541 | 47.0 | 141 | 0.3410 | 0.9348 |
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- | 0.0541 | 48.0 | 144 | 0.3533 | 0.9348 |
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- | 0.0541 | 49.0 | 147 | 0.3564 | 0.9348 |
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- | 0.022 | 50.0 | 150 | 0.3558 | 0.9348 |
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- | 0.022 | 51.0 | 153 | 0.3413 | 0.9348 |
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- | 0.022 | 52.0 | 156 | 0.3372 | 0.9348 |
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- | 0.022 | 53.0 | 159 | 0.3380 | 0.9348 |
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- | 0.0226 | 54.0 | 162 | 0.3289 | 0.9457 |
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- | 0.0226 | 55.0 | 165 | 0.3257 | 0.9348 |
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- | 0.0226 | 56.0 | 168 | 0.3263 | 0.9348 |
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- | 0.0358 | 57.0 | 171 | 0.3316 | 0.9348 |
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- | 0.0358 | 58.0 | 174 | 0.3367 | 0.9457 |
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- | 0.0358 | 59.0 | 177 | 0.3381 | 0.9457 |
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- | 0.0225 | 60.0 | 180 | 0.3383 | 0.9457 |
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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: 0.9456521739130435
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  ---
28
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
33
 
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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.
35
  It achieves the following results on the evaluation set:
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+ - Loss: 0.3181
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+ - Accuracy: 0.9457
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 3 | 0.6142 | 0.6304 |
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+ | No log | 2.0 | 6 | 0.3853 | 0.8696 |
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+ | No log | 3.0 | 9 | 0.4070 | 0.8261 |
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+ | 0.494 | 4.0 | 12 | 0.1461 | 0.9348 |
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+ | 0.494 | 5.0 | 15 | 0.1189 | 0.9565 |
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+ | 0.494 | 6.0 | 18 | 0.1527 | 0.9457 |
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+ | 0.2024 | 7.0 | 21 | 0.3323 | 0.9022 |
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+ | 0.2024 | 8.0 | 24 | 0.1520 | 0.9457 |
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+ | 0.2024 | 9.0 | 27 | 0.1572 | 0.9457 |
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+ | 0.1419 | 10.0 | 30 | 0.1814 | 0.9348 |
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+ | 0.1419 | 11.0 | 33 | 0.1778 | 0.9348 |
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+ | 0.1419 | 12.0 | 36 | 0.1505 | 0.9348 |
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+ | 0.1419 | 13.0 | 39 | 0.1891 | 0.9457 |
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+ | 0.1053 | 14.0 | 42 | 0.7274 | 0.7935 |
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+ | 0.1053 | 15.0 | 45 | 0.2669 | 0.9348 |
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+ | 0.1053 | 16.0 | 48 | 0.2240 | 0.9348 |
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+ | 0.3044 | 17.0 | 51 | 0.3497 | 0.8913 |
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+ | 0.3044 | 18.0 | 54 | 0.2208 | 0.9348 |
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+ | 0.3044 | 19.0 | 57 | 0.1733 | 0.9565 |
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+ | 0.151 | 20.0 | 60 | 0.2038 | 0.9239 |
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+ | 0.151 | 21.0 | 63 | 0.1282 | 0.9565 |
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+ | 0.151 | 22.0 | 66 | 0.3231 | 0.9239 |
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+ | 0.151 | 23.0 | 69 | 0.1565 | 0.9565 |
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+ | 0.0875 | 24.0 | 72 | 0.1981 | 0.9457 |
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+ | 0.0875 | 25.0 | 75 | 0.1974 | 0.9457 |
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+ | 0.0875 | 26.0 | 78 | 0.2045 | 0.9457 |
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+ | 0.0851 | 27.0 | 81 | 0.1841 | 0.9457 |
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+ | 0.0851 | 28.0 | 84 | 0.2061 | 0.9565 |
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+ | 0.0851 | 29.0 | 87 | 0.2077 | 0.9457 |
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+ | 0.046 | 30.0 | 90 | 0.2199 | 0.9565 |
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+ | 0.046 | 31.0 | 93 | 0.2038 | 0.9565 |
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+ | 0.046 | 32.0 | 96 | 0.2077 | 0.9457 |
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+ | 0.046 | 33.0 | 99 | 0.1877 | 0.9565 |
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+ | 0.0533 | 34.0 | 102 | 0.2383 | 0.9348 |
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+ | 0.0533 | 35.0 | 105 | 0.2571 | 0.9239 |
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+ | 0.0533 | 36.0 | 108 | 0.2330 | 0.9565 |
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+ | 0.0451 | 37.0 | 111 | 0.2420 | 0.9457 |
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+ | 0.0451 | 38.0 | 114 | 0.2882 | 0.9239 |
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+ | 0.0451 | 39.0 | 117 | 0.2386 | 0.9457 |
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+ | 0.0401 | 40.0 | 120 | 0.2513 | 0.9348 |
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+ | 0.0401 | 41.0 | 123 | 0.2672 | 0.9348 |
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+ | 0.0401 | 42.0 | 126 | 0.2950 | 0.9457 |
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+ | 0.0401 | 43.0 | 129 | 0.3232 | 0.9457 |
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+ | 0.0329 | 44.0 | 132 | 0.3712 | 0.9239 |
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+ | 0.0329 | 45.0 | 135 | 0.3529 | 0.9348 |
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+ | 0.0329 | 46.0 | 138 | 0.2905 | 0.9457 |
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+ | 0.0519 | 47.0 | 141 | 0.2670 | 0.9457 |
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+ | 0.0519 | 48.0 | 144 | 0.2629 | 0.9457 |
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+ | 0.0519 | 49.0 | 147 | 0.2761 | 0.9457 |
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+ | 0.0281 | 50.0 | 150 | 0.3040 | 0.9457 |
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+ | 0.0281 | 51.0 | 153 | 0.3191 | 0.9457 |
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+ | 0.0281 | 52.0 | 156 | 0.3214 | 0.9457 |
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+ | 0.0281 | 53.0 | 159 | 0.3132 | 0.9457 |
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+ | 0.028 | 54.0 | 162 | 0.3115 | 0.9457 |
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+ | 0.028 | 55.0 | 165 | 0.3116 | 0.9565 |
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+ | 0.028 | 56.0 | 168 | 0.3225 | 0.9457 |
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+ | 0.0361 | 57.0 | 171 | 0.3235 | 0.9457 |
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+ | 0.0361 | 58.0 | 174 | 0.3200 | 0.9457 |
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+ | 0.0361 | 59.0 | 177 | 0.3183 | 0.9457 |
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+ | 0.0312 | 60.0 | 180 | 0.3181 | 0.9457 |
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  ### Framework versions
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