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segformer-b0-finetuned-segments-sidewalk-2

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  1. README.md +55 -50
  2. config.json +1 -1
  3. model.safetensors +1 -1
  4. preprocessor_config.json +1 -1
  5. runs/Jan20_10-29-04_jupyter-admin01/events.out.tfevents.1737368961.jupyter-admin01.120.6 +3 -0
  6. runs/Jan20_10-29-42_jupyter-admin01/events.out.tfevents.1737368993.jupyter-admin01.120.7 +3 -0
  7. runs/Jan20_10-30-21_jupyter-admin01/events.out.tfevents.1737369059.jupyter-admin01.120.8 +3 -0
  8. runs/Jan20_10-31-46_jupyter-admin01/events.out.tfevents.1737369117.jupyter-admin01.120.9 +3 -0
  9. runs/Jan20_10-32-26_jupyter-admin01/events.out.tfevents.1737369158.jupyter-admin01.120.10 +3 -0
  10. training_args.bin +1 -1
README.md CHANGED
@@ -17,80 +17,80 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [segformer-b0-finetuned-ade-512-512](https://huggingface.co/segformer-b0-finetuned-ade-512-512) on the segments/sidewalk-semantic dataset.
19
  It achieves the following results on the evaluation set:
20
- - Loss: 2.4704
21
- - Mean Iou: 0.1228
22
- - Mean Accuracy: 0.1842
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- - Overall Accuracy: 0.6771
24
  - Accuracy Unlabeled: nan
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- - Accuracy Flat-road: 0.8619
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- - Accuracy Flat-sidewalk: 0.8589
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- - Accuracy Flat-crosswalk: 0.0
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- - Accuracy Flat-cyclinglane: 0.0530
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- - Accuracy Flat-parkingdriveway: 0.0006
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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: 0.8886
35
- - Accuracy Vehicle-truck: 0.0
36
  - Accuracy Vehicle-bus: 0.0
37
- - Accuracy Vehicle-tramtrain: nan
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  - Accuracy Vehicle-motorcycle: 0.0
39
  - Accuracy Vehicle-bicycle: 0.0
40
  - Accuracy Vehicle-caravan: 0.0
41
- - Accuracy Vehicle-cartrailer: 0.0
42
- - Accuracy Construction-building: 0.6177
43
  - Accuracy Construction-door: 0.0
44
  - Accuracy Construction-wall: 0.0
45
- - Accuracy Construction-fenceguardrail: 0.0
46
  - Accuracy Construction-bridge: 0.0
47
- - Accuracy Construction-tunnel: nan
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  - Accuracy Construction-stairs: 0.0
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  - Accuracy Object-pole: 0.0
50
  - Accuracy Object-trafficsign: 0.0
51
- - Accuracy Object-trafficlight: 0.0
52
- - Accuracy Nature-vegetation: 0.9365
53
- - Accuracy Nature-terrain: 0.5350
54
- - Accuracy Sky: 0.9510
55
  - Accuracy Void-ground: 0.0
56
  - Accuracy Void-dynamic: 0.0
57
- - Accuracy Void-static: 0.0074
58
- - Accuracy Void-unclear: 0.0
59
- - Iou Unlabeled: nan
60
- - Iou Flat-road: 0.4368
61
- - Iou Flat-sidewalk: 0.7208
62
- - Iou Flat-crosswalk: 0.0
63
- - Iou Flat-cyclinglane: 0.0505
64
- - Iou Flat-parkingdriveway: 0.0005
65
  - Iou Flat-railtrack: 0.0
66
  - Iou Flat-curb: 0.0
67
  - Iou Human-person: 0.0
68
- - Iou Human-rider: 0.0
69
- - Iou Vehicle-car: 0.5175
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- - Iou Vehicle-truck: 0.0
71
  - Iou Vehicle-bus: 0.0
72
- - Iou Vehicle-tramtrain: nan
73
  - Iou Vehicle-motorcycle: 0.0
74
  - Iou Vehicle-bicycle: 0.0
75
  - Iou Vehicle-caravan: 0.0
76
- - Iou Vehicle-cartrailer: 0.0
77
- - Iou Construction-building: 0.5053
78
  - Iou Construction-door: 0.0
79
  - Iou Construction-wall: 0.0
80
- - Iou Construction-fenceguardrail: 0.0
81
  - Iou Construction-bridge: 0.0
82
- - Iou Construction-tunnel: nan
83
  - 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: 0.6868
88
- - Iou Nature-terrain: 0.3948
89
- - Iou Sky: 0.6104
90
  - Iou Void-ground: 0.0
91
  - Iou Void-dynamic: 0.0
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- - Iou Void-static: 0.0073
93
- - Iou Void-unclear: 0.0
94
 
95
  ## Model description
96
 
@@ -115,17 +115,22 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Use 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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- - num_epochs: 1
119
 
120
  ### Training results
121
 
122
  | 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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- | 3.0949 | 0.2 | 20 | 3.0458 | 0.0819 | 0.1460 | 0.5398 | nan | 0.7787 | 0.7924 | 0.0044 | 0.1968 | 0.0005 | nan | 0.0002 | 0.0438 | 0.0096 | 0.8797 | 0.0059 | 0.0 | nan | 0.0130 | 0.0 | 0.0 | 0.0 | 0.0409 | 0.0 | 0.0023 | 0.0 | 0.0004 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6323 | 0.2713 | 0.7608 | 0.0000 | 0.0154 | 0.0043 | 0.0720 | 0.0 | 0.4417 | 0.6871 | 0.0038 | 0.1179 | 0.0005 | 0.0 | 0.0002 | 0.0246 | 0.0004 | 0.3778 | 0.0036 | 0.0 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0401 | 0.0 | 0.0023 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5177 | 0.2196 | 0.4219 | 0.0000 | 0.0011 | 0.0042 | 0.0001 |
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- | 2.9043 | 0.4 | 40 | 2.7801 | 0.1036 | 0.1809 | 0.6489 | nan | 0.8303 | 0.8567 | 0.0000 | 0.1591 | 0.0005 | nan | 0.0001 | 0.0036 | 0.0 | 0.9453 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.3779 | 0.0 | 0.0003 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8374 | 0.6238 | 0.9717 | 0.0 | 0.0000 | 0.0014 | 0.0 | 0.0 | 0.4619 | 0.7132 | 0.0000 | 0.1384 | 0.0005 | 0.0 | 0.0001 | 0.0033 | 0.0 | 0.3443 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3474 | 0.0 | 0.0003 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6680 | 0.4432 | 0.5029 | 0.0 | 0.0000 | 0.0014 | 0.0 |
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- | 2.7418 | 0.6 | 60 | 2.5939 | 0.1131 | 0.1800 | 0.6631 | nan | 0.8577 | 0.8508 | 0.0 | 0.0594 | 0.0004 | nan | 0.0000 | 0.0002 | 0.0 | 0.9340 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5521 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9077 | 0.4507 | 0.9612 | 0.0 | 0.0 | 0.0044 | 0.0 | nan | 0.4298 | 0.7138 | 0.0 | 0.0555 | 0.0004 | 0.0 | 0.0000 | 0.0002 | 0.0 | 0.4210 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.4768 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6815 | 0.3562 | 0.5935 | 0.0 | 0.0 | 0.0043 | 0.0 |
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- | 2.6386 | 0.8 | 80 | 2.4834 | 0.1216 | 0.1831 | 0.6776 | nan | 0.8479 | 0.8715 | 0.0 | 0.0632 | 0.0005 | nan | 0.0 | 0.0000 | 0.0 | 0.8980 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6096 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9324 | 0.4880 | 0.9573 | 0.0 | 0.0 | 0.0088 | 0.0 | nan | 0.4463 | 0.7197 | 0.0 | 0.0604 | 0.0005 | 0.0 | 0.0 | 0.0000 | 0.0 | 0.5022 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5004 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6846 | 0.3790 | 0.5905 | 0.0 | 0.0 | 0.0086 | 0.0 |
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- | 2.4504 | 1.0 | 100 | 2.4704 | 0.1228 | 0.1842 | 0.6771 | nan | 0.8619 | 0.8589 | 0.0 | 0.0530 | 0.0006 | nan | 0.0 | 0.0 | 0.0 | 0.8886 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6177 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9365 | 0.5350 | 0.9510 | 0.0 | 0.0 | 0.0074 | 0.0 | nan | 0.4368 | 0.7208 | 0.0 | 0.0505 | 0.0005 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5175 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5053 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6868 | 0.3948 | 0.6104 | 0.0 | 0.0 | 0.0073 | 0.0 |
 
 
 
 
 
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  ### Framework versions
 
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18
  This model is a fine-tuned version of [segformer-b0-finetuned-ade-512-512](https://huggingface.co/segformer-b0-finetuned-ade-512-512) on the segments/sidewalk-semantic dataset.
19
  It achieves the following results on the evaluation set:
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+ - Loss: 1.5824
21
+ - Mean Iou: 0.0054
22
+ - Mean Accuracy: 0.0686
23
+ - Overall Accuracy: 0.0400
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  - Accuracy Unlabeled: nan
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+ - Accuracy Flat-road: 0.7137
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+ - Accuracy Flat-sidewalk: 0.0
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+ - Accuracy Flat-crosswalk: 0.0005
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+ - Accuracy Flat-cyclinglane: nan
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+ - Accuracy Flat-parkingdriveway: 0.0
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+ - Accuracy Flat-railtrack: 0.0
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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: 0.0947
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+ - Accuracy Vehicle-car: 0.0
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+ - Accuracy Vehicle-truck: nan
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  - Accuracy Vehicle-bus: 0.0
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+ - Accuracy Vehicle-tramtrain: 0.0
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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: 0.9616
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+ - Accuracy Construction-building: 0.0
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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: nan
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  - Accuracy Construction-bridge: 0.0
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+ - Accuracy Construction-tunnel: 0.0
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  - Accuracy Construction-stairs: 0.0
49
  - Accuracy Object-pole: 0.0
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  - Accuracy Object-trafficsign: 0.0
51
+ - Accuracy Object-trafficlight: 0.2176
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+ - Accuracy Nature-vegetation: 0.0000
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+ - Accuracy Nature-terrain: 0.0
54
+ - Accuracy Sky: 0.0
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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: nan
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+ - Accuracy Void-unclear: nan
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+ - Iou Unlabeled: 0.0
60
+ - Iou Flat-road: 0.1014
61
+ - Iou Flat-sidewalk: 0.0
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+ - Iou Flat-crosswalk: 0.0004
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+ - Iou Flat-cyclinglane: 0.0
64
+ - Iou Flat-parkingdriveway: 0.0
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  - Iou Flat-railtrack: 0.0
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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: 0.0039
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+ - Iou Vehicle-car: 0.0
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+ - Iou Vehicle-truck: nan
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  - Iou Vehicle-bus: 0.0
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+ - Iou Vehicle-tramtrain: 0.0
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  - Iou Vehicle-motorcycle: 0.0
74
  - Iou Vehicle-bicycle: 0.0
75
  - Iou Vehicle-caravan: 0.0
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+ - Iou Vehicle-cartrailer: 0.0228
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+ - Iou Construction-building: 0.0
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  - Iou Construction-door: 0.0
79
  - Iou Construction-wall: 0.0
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+ - Iou Construction-fenceguardrail: nan
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  - Iou Construction-bridge: 0.0
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+ - Iou Construction-tunnel: 0.0
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  - Iou Construction-stairs: 0.0
84
  - Iou Object-pole: 0.0
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  - Iou Object-trafficsign: 0.0
86
+ - Iou Object-trafficlight: 0.0394
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+ - Iou Nature-vegetation: 0.0000
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+ - Iou Nature-terrain: 0.0
89
+ - Iou Sky: 0.0
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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: nan
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+ - Iou Void-unclear: nan
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  ## Model description
96
 
 
115
  - seed: 42
116
  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
117
  - lr_scheduler_type: linear
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+ - num_epochs: 2
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120
  ### Training results
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122
  | 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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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:|
124
+ | 2.3223 | 0.2 | 20 | 2.3183 | 0.0081 | 0.0599 | 0.0598 | nan | 0.7724 | 0.0000 | 0.0019 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0005 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3997 | 0.0077 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5064 | 0.0125 | 0.0350 | 0.0004 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0945 | 0.0000 | 0.0018 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0456 | 0.0073 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0955 | 0.0036 | 0.0020 | 0.0001 | 0.0 | 0.0 | nan | nan |
125
+ | 2.3053 | 0.4 | 40 | 2.1137 | 0.0070 | 0.0753 | 0.0608 | nan | 0.7392 | 0.0000 | 0.0018 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0659 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8513 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5173 | 0.0012 | 0.0063 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0932 | 0.0000 | 0.0017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0032 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0273 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0898 | 0.0006 | 0.0006 | 0.0 | 0.0 | 0.0 | nan | nan |
126
+ | 2.0433 | 0.6 | 60 | 1.9808 | 0.0076 | 0.0829 | 0.0702 | nan | 0.7626 | 0.0 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0679 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9286 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6419 | 0.0001 | 0.0015 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0947 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0034 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0248 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1130 | 0.0000 | 0.0002 | 0.0 | 0.0 | 0.0 | nan | nan |
127
+ | 1.9249 | 0.8 | 80 | 1.8427 | 0.0070 | 0.0791 | 0.0632 | nan | 0.7638 | 0.0 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0417 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9489 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5388 | 0.0000 | 0.0003 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0968 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0023 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0235 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0936 | 0.0000 | 0.0000 | 0.0 | 0.0 | 0.0 | nan | nan |
128
+ | 1.7157 | 1.0 | 100 | 1.7687 | 0.0065 | 0.0790 | 0.0588 | nan | 0.7749 | 0.0 | 0.0005 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0989 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9497 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4683 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0903 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0043 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0242 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0819 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan |
129
+ | 1.6624 | 1.2 | 120 | 1.6653 | 0.0054 | 0.0709 | 0.0441 | nan | 0.7509 | 0.0 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0849 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9559 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2636 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0931 | 0.0 | 0.0002 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0038 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0235 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0472 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan |
130
+ | 1.6718 | 1.4 | 140 | 1.6575 | 0.0050 | 0.0639 | 0.0370 | nan | 0.5706 | 0.0 | 0.0008 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0856 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9640 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2325 | 0.0000 | 0.0000 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0864 | 0.0 | 0.0005 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0037 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0223 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0423 | 0.0000 | 0.0000 | 0.0 | 0.0 | 0.0 | nan | nan |
131
+ | 1.731 | 1.6 | 160 | 1.5986 | 0.0056 | 0.0693 | 0.0438 | nan | 0.6585 | 0.0 | 0.0004 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0946 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9597 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2964 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.0949 | 0.0 | 0.0003 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0041 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0238 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0515 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan |
132
+ | 1.7125 | 1.8 | 180 | 1.5779 | 0.0055 | 0.0693 | 0.0416 | nan | 0.7108 | 0.0 | 0.0006 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0942 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9633 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2420 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.1009 | 0.0 | 0.0004 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0039 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0230 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0433 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan |
133
+ | 1.6421 | 2.0 | 200 | 1.5824 | 0.0054 | 0.0686 | 0.0400 | nan | 0.7137 | 0.0 | 0.0005 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0947 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9616 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2176 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan | 0.0 | 0.1014 | 0.0 | 0.0004 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0039 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0228 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0394 | 0.0000 | 0.0 | 0.0 | 0.0 | 0.0 | nan | nan |
134
 
135
 
136
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