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segformer-b0-finetuned-segments-sidewalk-test

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End of training

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  1. README.md +93 -27
  2. config.json +70 -4
  3. model.safetensors +2 -2
  4. runs/May26_21-25-14_c62e72524a85/events.out.tfevents.1716758721.c62e72524a85.903.7 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
@@ -1,10 +1,10 @@
1
  ---
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  license: other
 
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  tags:
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  - vision
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  - image-segmentation
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  - generated_from_trainer
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- base_model: nvidia/mit-b0
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  model-index:
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  - name: segformer-b0-finetuned-segments-sidewalk-test
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  results: []
@@ -17,14 +17,80 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1620
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- - Mean Iou: 0.4717
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- - Mean Accuracy: 0.9435
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- - Overall Accuracy: 0.9435
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- - Accuracy Other: nan
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- - Accuracy Flat-sidewalk: 0.9435
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- - Iou Other: 0.0
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- - Iou Flat-sidewalk: 0.9435
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Model description
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@@ -53,24 +119,24 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Other | Accuracy Flat-sidewalk | Iou Other | Iou Flat-sidewalk |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:----------------------:|:---------:|:-----------------:|
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- | 0.0426 | 0.125 | 20 | 0.1662 | 0.4707 | 0.9414 | 0.9414 | nan | 0.9414 | 0.0 | 0.9414 |
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- | 0.0301 | 0.25 | 40 | 0.1757 | 0.4761 | 0.9522 | 0.9522 | nan | 0.9522 | 0.0 | 0.9522 |
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- | 0.0307 | 0.375 | 60 | 0.1795 | 0.4676 | 0.9353 | 0.9353 | nan | 0.9353 | 0.0 | 0.9353 |
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- | 0.0451 | 0.5 | 80 | 0.1655 | 0.4730 | 0.9460 | 0.9460 | nan | 0.9460 | 0.0 | 0.9460 |
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- | 0.041 | 0.625 | 100 | 0.1745 | 0.4726 | 0.9452 | 0.9452 | nan | 0.9452 | 0.0 | 0.9452 |
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- | 0.0406 | 0.75 | 120 | 0.1629 | 0.4769 | 0.9539 | 0.9539 | nan | 0.9539 | 0.0 | 0.9539 |
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- | 0.0798 | 0.875 | 140 | 0.1594 | 0.4686 | 0.9372 | 0.9372 | nan | 0.9372 | 0.0 | 0.9372 |
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- | 0.0349 | 1.0 | 160 | 0.1582 | 0.4718 | 0.9436 | 0.9436 | nan | 0.9436 | 0.0 | 0.9436 |
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- | 0.0566 | 1.125 | 180 | 0.1785 | 0.4654 | 0.9307 | 0.9307 | nan | 0.9307 | 0.0 | 0.9307 |
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- | 0.0283 | 1.25 | 200 | 0.1637 | 0.4735 | 0.9470 | 0.9470 | nan | 0.9470 | 0.0 | 0.9470 |
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- | 0.0262 | 1.375 | 220 | 0.1740 | 0.4760 | 0.9519 | 0.9519 | nan | 0.9519 | 0.0 | 0.9519 |
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- | 0.0335 | 1.5 | 240 | 0.1640 | 0.4733 | 0.9465 | 0.9465 | nan | 0.9465 | 0.0 | 0.9465 |
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- | 0.0365 | 1.625 | 260 | 0.1631 | 0.4737 | 0.9474 | 0.9474 | nan | 0.9474 | 0.0 | 0.9474 |
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- | 0.0781 | 1.75 | 280 | 0.1653 | 0.4733 | 0.9466 | 0.9466 | nan | 0.9466 | 0.0 | 0.9466 |
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- | 0.0846 | 1.875 | 300 | 0.1671 | 0.4724 | 0.9449 | 0.9449 | nan | 0.9449 | 0.0 | 0.9449 |
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- | 0.0301 | 2.0 | 320 | 0.1620 | 0.4717 | 0.9435 | 0.9435 | nan | 0.9435 | 0.0 | 0.9435 |
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  ### Framework versions
 
1
  ---
2
  license: other
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+ base_model: nvidia/mit-b0
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  tags:
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  - vision
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  - image-segmentation
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  - generated_from_trainer
 
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  model-index:
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  - name: segformer-b0-finetuned-segments-sidewalk-test
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  results: []
 
17
 
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  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
19
  It achieves the following results on the evaluation set:
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+ - Loss: 1.3438
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+ - Mean Iou: 0.1623
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+ - Mean Accuracy: 0.2111
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+ - Overall Accuracy: 0.7405
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Flat-road: 0.8041
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+ - Accuracy Flat-sidewalk: 0.9230
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+ - Accuracy Flat-crosswalk: 0.0
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+ - Accuracy Flat-cyclinglane: 0.4039
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+ - Accuracy Flat-parkingdriveway: 0.0060
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+ - Accuracy Flat-railtrack: nan
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+ - Accuracy Flat-curb: 0.0
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+ - Accuracy Human-person: 0.0
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+ - Accuracy Human-rider: 0.0
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+ - Accuracy Vehicle-car: 0.8846
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+ - Accuracy Vehicle-truck: 0.0
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+ - Accuracy Vehicle-bus: 0.0
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+ - Accuracy Vehicle-tramtrain: nan
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+ - Accuracy Vehicle-motorcycle: 0.0
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+ - Accuracy Vehicle-bicycle: 0.0
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+ - Accuracy Vehicle-caravan: 0.0
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+ - Accuracy Vehicle-cartrailer: 0.0
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+ - Accuracy Construction-building: 0.8642
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+ - Accuracy Construction-door: 0.0
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+ - Accuracy Construction-wall: 0.0
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+ - Accuracy Construction-fenceguardrail: 0.0
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+ - Accuracy Construction-bridge: 0.0
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+ - Accuracy Construction-tunnel: nan
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+ - Accuracy Construction-stairs: 0.0
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+ - Accuracy Object-pole: 0.0
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+ - Accuracy Object-trafficsign: 0.0
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+ - Accuracy Object-trafficlight: 0.0
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+ - Accuracy Nature-vegetation: 0.9187
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+ - Accuracy Nature-terrain: 0.8205
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+ - Accuracy Sky: 0.9200
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+ - Accuracy Void-ground: 0.0
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+ - Accuracy Void-dynamic: 0.0
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+ - Accuracy Void-static: 0.0
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+ - Accuracy Void-unclear: 0.0
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+ - Iou Unlabeled: nan
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+ - Iou Flat-road: 0.5280
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+ - Iou Flat-sidewalk: 0.7452
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+ - Iou Flat-crosswalk: 0.0
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+ - Iou Flat-cyclinglane: 0.3868
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+ - Iou Flat-parkingdriveway: 0.0059
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+ - Iou Flat-railtrack: nan
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+ - Iou Flat-curb: 0.0
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+ - Iou Human-person: 0.0
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+ - Iou Human-rider: 0.0
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+ - Iou Vehicle-car: 0.6061
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+ - Iou Vehicle-truck: 0.0
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+ - Iou Vehicle-bus: 0.0
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+ - Iou Vehicle-tramtrain: nan
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+ - Iou Vehicle-motorcycle: 0.0
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+ - Iou Vehicle-bicycle: 0.0
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+ - Iou Vehicle-caravan: 0.0
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+ - Iou Vehicle-cartrailer: 0.0
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+ - Iou Construction-building: 0.5539
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+ - Iou Construction-door: 0.0
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+ - Iou Construction-wall: 0.0
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+ - Iou Construction-fenceguardrail: 0.0
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+ - Iou Construction-bridge: 0.0
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+ - Iou Construction-tunnel: nan
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+ - Iou Construction-stairs: 0.0
84
+ - Iou Object-pole: 0.0
85
+ - Iou Object-trafficsign: 0.0
86
+ - Iou Object-trafficlight: 0.0
87
+ - Iou Nature-vegetation: 0.7712
88
+ - Iou Nature-terrain: 0.6207
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+ - Iou Sky: 0.8130
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+ - Iou Void-ground: 0.0
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+ - Iou Void-dynamic: 0.0
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+ - Iou Void-static: 0.0
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+ - Iou Void-unclear: 0.0
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  ## Model description
96
 
 
119
 
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:|
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+ | 2.7716 | 0.125 | 20 | 3.1137 | 0.0811 | 0.1421 | 0.5939 | nan | 0.3543 | 0.8601 | 0.0001 | 0.0624 | 0.0009 | nan | 0.0059 | 0.0 | 0.0 | 0.7448 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7505 | 0.0004 | 0.0000 | 0.0 | 0.0 | nan | 0.0004 | 0.0154 | 0.0 | 0.0 | 0.9620 | 0.0107 | 0.6345 | 0.0 | 0.0 | 0.0015 | 0.0 | 0.0 | 0.2853 | 0.6123 | 0.0001 | 0.0571 | 0.0009 | 0.0 | 0.0057 | 0.0 | 0.0 | 0.4056 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4427 | 0.0003 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0003 | 0.0070 | 0.0 | 0.0 | 0.5640 | 0.0101 | 0.4453 | 0.0 | 0.0 | 0.0011 | 0.0 |
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+ | 2.3983 | 0.25 | 40 | 2.3114 | 0.0953 | 0.1473 | 0.6217 | nan | 0.5446 | 0.8620 | 0.0 | 0.0116 | 0.0001 | nan | 0.0003 | 0.0 | 0.0 | 0.6298 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8869 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9467 | 0.0435 | 0.6394 | 0.0 | 0.0 | 0.0001 | 0.0 | 0.0 | 0.3736 | 0.6363 | 0.0 | 0.0114 | 0.0001 | nan | 0.0003 | 0.0 | 0.0 | 0.4702 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4103 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6126 | 0.0403 | 0.5883 | 0.0 | 0.0 | 0.0001 | 0.0 |
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+ | 1.9364 | 0.375 | 60 | 1.9470 | 0.1125 | 0.1622 | 0.6555 | nan | 0.6129 | 0.9011 | 0.0 | 0.0015 | 0.0000 | nan | 0.0000 | 0.0 | 0.0 | 0.7328 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8377 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9614 | 0.1659 | 0.8151 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4018 | 0.6627 | 0.0 | 0.0015 | 0.0000 | nan | 0.0000 | 0.0 | 0.0 | 0.5428 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.4751 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6545 | 0.1483 | 0.7132 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.8248 | 0.5 | 80 | 1.8423 | 0.1249 | 0.1766 | 0.6773 | nan | 0.7096 | 0.8967 | 0.0 | 0.0031 | 0.0001 | nan | 0.0000 | 0.0 | 0.0 | 0.8122 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8119 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.9544 | 0.4021 | 0.8846 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4158 | 0.6886 | 0.0 | 0.0031 | 0.0001 | nan | 0.0000 | 0.0 | 0.0 | 0.5826 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5075 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.6969 | 0.3488 | 0.7535 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.8107 | 0.625 | 100 | 1.6979 | 0.1344 | 0.1824 | 0.6868 | nan | 0.7597 | 0.8914 | 0.0 | 0.0116 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.8460 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8479 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9342 | 0.4696 | 0.8944 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4201 | 0.7042 | 0.0 | 0.0116 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.5888 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5129 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7334 | 0.4274 | 0.7687 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.5911 | 0.75 | 120 | 1.6101 | 0.1408 | 0.1922 | 0.6972 | nan | 0.7585 | 0.9043 | 0.0 | 0.0317 | 0.0004 | nan | 0.0 | 0.0 | 0.0 | 0.8168 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8677 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8795 | 0.7990 | 0.8994 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4462 | 0.7130 | 0.0 | 0.0317 | 0.0004 | nan | 0.0 | 0.0 | 0.0 | 0.6128 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5132 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7389 | 0.5323 | 0.7764 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.6869 | 0.875 | 140 | 1.5604 | 0.1406 | 0.1905 | 0.7016 | nan | 0.7813 | 0.9000 | 0.0 | 0.0166 | 0.0020 | nan | 0.0 | 0.0 | 0.0 | 0.8807 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8563 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.9394 | 0.6490 | 0.8801 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4544 | 0.7211 | 0.0 | 0.0166 | 0.0020 | nan | 0.0 | 0.0 | 0.0 | 0.5683 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5343 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.7401 | 0.5306 | 0.7923 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.395 | 1.0 | 160 | 1.5115 | 0.1436 | 0.1945 | 0.7109 | nan | 0.7718 | 0.9196 | 0.0 | 0.0812 | 0.0029 | nan | 0.0 | 0.0 | 0.0 | 0.8752 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8216 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9410 | 0.6934 | 0.9228 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4798 | 0.7282 | 0.0 | 0.0809 | 0.0029 | nan | 0.0 | 0.0 | 0.0 | 0.5696 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5451 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7332 | 0.5201 | 0.7904 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.4842 | 1.125 | 180 | 1.4582 | 0.1504 | 0.2010 | 0.7192 | nan | 0.8239 | 0.8991 | 0.0 | 0.1813 | 0.0016 | nan | 0.0 | 0.0 | 0.0 | 0.8714 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8660 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9219 | 0.7649 | 0.9006 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4825 | 0.7399 | 0.0 | 0.1805 | 0.0016 | nan | 0.0 | 0.0 | 0.0 | 0.5932 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5427 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7540 | 0.5687 | 0.8005 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.4327 | 1.25 | 200 | 1.4157 | 0.1555 | 0.2068 | 0.7253 | nan | 0.8297 | 0.8936 | 0.0 | 0.3055 | 0.0036 | nan | 0.0 | 0.0 | 0.0 | 0.8738 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8629 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9042 | 0.8321 | 0.9051 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4944 | 0.7401 | 0.0 | 0.3024 | 0.0036 | nan | 0.0 | 0.0 | 0.0 | 0.5952 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5470 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7452 | 0.5870 | 0.8050 | 0.0 | 0.0 | 0.0 | 0.0 |
134
+ | 1.572 | 1.375 | 220 | 1.3965 | 0.1599 | 0.2083 | 0.7319 | nan | 0.8294 | 0.8967 | 0.0 | 0.3786 | 0.0037 | nan | 0.0 | 0.0 | 0.0 | 0.8695 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8644 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9259 | 0.7706 | 0.9193 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4980 | 0.7405 | 0.0 | 0.3668 | 0.0036 | nan | 0.0 | 0.0 | 0.0 | 0.6125 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5476 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7613 | 0.6235 | 0.8037 | 0.0 | 0.0 | 0.0 | 0.0 |
135
+ | 1.2597 | 1.5 | 240 | 1.3644 | 0.1606 | 0.2103 | 0.7365 | nan | 0.8033 | 0.9166 | 0.0 | 0.3886 | 0.0060 | nan | 0.0 | 0.0 | 0.0 | 0.8849 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8813 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.9033 | 0.8315 | 0.9054 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5225 | 0.7428 | 0.0 | 0.3727 | 0.0059 | nan | 0.0 | 0.0 | 0.0 | 0.6030 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5511 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.7639 | 0.6031 | 0.8149 | 0.0 | 0.0 | 0.0 | 0.0 |
136
+ | 1.7608 | 1.625 | 260 | 1.3297 | 0.1610 | 0.2102 | 0.7368 | nan | 0.8097 | 0.9127 | 0.0 | 0.3923 | 0.0047 | nan | 0.0 | 0.0 | 0.0 | 0.8654 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8689 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9181 | 0.8314 | 0.9130 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5185 | 0.7447 | 0.0 | 0.3761 | 0.0047 | nan | 0.0 | 0.0 | 0.0 | 0.6169 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5503 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7600 | 0.6090 | 0.8109 | 0.0 | 0.0 | 0.0 | 0.0 |
137
+ | 1.4774 | 1.75 | 280 | 1.3179 | 0.1618 | 0.2106 | 0.7399 | nan | 0.7953 | 0.9246 | 0.0 | 0.4041 | 0.0066 | nan | 0.0 | 0.0 | 0.0 | 0.8769 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8654 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9207 | 0.8097 | 0.9252 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5312 | 0.7433 | 0.0 | 0.3854 | 0.0065 | nan | 0.0 | 0.0 | 0.0 | 0.6059 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5503 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7687 | 0.6161 | 0.8086 | 0.0 | 0.0 | 0.0 | 0.0 |
138
+ | 1.4301 | 1.875 | 300 | 1.3037 | 0.1621 | 0.2112 | 0.7406 | nan | 0.7913 | 0.9313 | 0.0 | 0.3997 | 0.0062 | nan | 0.0 | 0.0 | 0.0 | 0.8855 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8750 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.9023 | 0.8278 | 0.9271 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5340 | 0.7429 | 0.0 | 0.3833 | 0.0061 | nan | 0.0 | 0.0 | 0.0 | 0.6074 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5494 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.7753 | 0.6166 | 0.8106 | 0.0 | 0.0 | 0.0 | 0.0 |
139
+ | 1.4838 | 2.0 | 320 | 1.3438 | 0.1623 | 0.2111 | 0.7405 | nan | 0.8041 | 0.9230 | 0.0 | 0.4039 | 0.0060 | nan | 0.0 | 0.0 | 0.0 | 0.8846 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8642 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9187 | 0.8205 | 0.9200 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5280 | 0.7452 | 0.0 | 0.3868 | 0.0059 | nan | 0.0 | 0.0 | 0.0 | 0.6061 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5539 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7712 | 0.6207 | 0.8130 | 0.0 | 0.0 | 0.0 | 0.0 |
140
 
141
 
142
  ### Framework versions
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