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segformer-b0-finetuned-segments-sidewalk-3

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  1. README.md +103 -2
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -1,7 +1,7 @@
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  ---
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- base_model: nvidia/mit-b0
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  library_name: transformers
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  license: other
 
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  tags:
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  - vision
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  - image-segmentation
@@ -17,6 +17,81 @@ should probably proofread and complete it, then remove this comment. -->
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  # segformer-b0-finetuned-segments-sidewalk-3
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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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  ## Model description
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@@ -41,7 +116,33 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Use OptimizerNames.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: 50
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  ---
 
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  library_name: transformers
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  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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  # segformer-b0-finetuned-segments-sidewalk-3
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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: 0.5669
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+ - Mean Iou: 0.3190
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+ - Mean Accuracy: 0.3821
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+ - Overall Accuracy: 0.8574
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Flat-road: 0.9199
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+ - Accuracy Flat-sidewalk: 0.9566
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+ - Accuracy Flat-crosswalk: 0.5855
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+ - Accuracy Flat-cyclinglane: 0.8187
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+ - Accuracy Flat-parkingdriveway: 0.4712
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+ - Accuracy Flat-railtrack: nan
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+ - Accuracy Flat-curb: 0.5293
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+ - Accuracy Human-person: 0.7962
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+ - Accuracy Human-rider: 0.0
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+ - Accuracy Vehicle-car: 0.9245
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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: 0.5123
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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: 0.8912
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+ - Accuracy Construction-door: 0.0
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+ - Accuracy Construction-wall: 0.5405
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+ - Accuracy Construction-fenceguardrail: 0.3863
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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: 0.4414
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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: 0.9331
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+ - Accuracy Nature-terrain: 0.8636
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+ - Accuracy Sky: 0.9713
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+ - Accuracy Void-ground: 0.0000
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+ - Accuracy Void-dynamic: 0.0
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+ - Accuracy Void-static: 0.3037
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+ - Accuracy Void-unclear: 0.0
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+ - Iou Unlabeled: nan
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+ - Iou Flat-road: 0.7635
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+ - Iou Flat-sidewalk: 0.8715
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+ - Iou Flat-crosswalk: 0.5513
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+ - Iou Flat-cyclinglane: 0.7441
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+ - Iou Flat-parkingdriveway: 0.3799
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+ - Iou Flat-railtrack: nan
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+ - Iou Flat-curb: 0.4143
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+ - Iou Human-person: 0.5353
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+ - Iou Human-rider: 0.0
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+ - Iou Vehicle-car: 0.8080
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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: 0.3478
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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: 0.7370
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+ - Iou Construction-door: 0.0
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+ - Iou Construction-wall: 0.3749
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+ - Iou Construction-fenceguardrail: 0.3203
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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
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+ - Iou Object-pole: 0.3284
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+ - Iou Object-trafficsign: 0.0
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+ - Iou Object-trafficlight: 0.0
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+ - Iou Nature-vegetation: 0.8586
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+ - Iou Nature-terrain: 0.7283
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+ - Iou Sky: 0.9243
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+ - Iou Void-ground: 0.0000
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+ - Iou Void-dynamic: 0.0
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+ - Iou Void-static: 0.2014
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+ - Iou Void-unclear: 0.0
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  ## Model description
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  - seed: 42
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  - optimizer: Use OptimizerNames.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: 20
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+
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+ ### Training results
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+
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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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+ | 1.2409 | 1.0 | 200 | 1.2471 | 0.1570 | 0.2044 | 0.7356 | nan | 0.8032 | 0.9526 | 0.0 | 0.3067 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.9054 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8352 | 0.0 | 0.0007 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9434 | 0.6803 | 0.9090 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5173 | 0.7510 | 0.0 | 0.2997 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.5787 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5852 | 0.0 | 0.0007 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7164 | 0.5939 | 0.8246 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.1443 | 2.0 | 400 | 0.9634 | 0.1732 | 0.2195 | 0.7607 | nan | 0.8231 | 0.9651 | 0.0 | 0.5125 | 0.0159 | nan | 0.0005 | 0.0 | 0.0 | 0.9316 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8419 | 0.0 | 0.0476 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9224 | 0.8029 | 0.9413 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5515 | 0.7650 | 0.0 | 0.4662 | 0.0157 | nan | 0.0005 | 0.0 | 0.0 | 0.6181 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6102 | 0.0 | 0.0440 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7835 | 0.6602 | 0.8541 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.8805 | 3.0 | 600 | 0.8309 | 0.1963 | 0.2429 | 0.7804 | nan | 0.8858 | 0.9438 | 0.0 | 0.5258 | 0.2765 | nan | 0.2373 | 0.0 | 0.0 | 0.8852 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9159 | 0.0 | 0.1947 | 0.0000 | 0.0 | nan | 0.0 | 0.0004 | 0.0 | 0.0 | 0.9174 | 0.8250 | 0.9226 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5450 | 0.8189 | 0.0 | 0.4853 | 0.2291 | nan | 0.1953 | 0.0 | 0.0 | 0.7014 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6219 | 0.0 | 0.1442 | 0.0000 | 0.0 | nan | 0.0 | 0.0004 | 0.0 | 0.0 | 0.7977 | 0.6821 | 0.8641 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 0.5657 | 4.0 | 800 | 0.7312 | 0.2177 | 0.2662 | 0.8045 | nan | 0.8296 | 0.9595 | 0.0118 | 0.7076 | 0.3690 | nan | 0.3339 | 0.0056 | 0.0 | 0.9077 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9036 | 0.0 | 0.4576 | 0.0102 | 0.0 | nan | 0.0 | 0.0242 | 0.0 | 0.0 | 0.9308 | 0.8451 | 0.9532 | 0.0 | 0.0 | 0.0040 | 0.0 | nan | 0.6207 | 0.8228 | 0.0118 | 0.6313 | 0.2862 | nan | 0.2661 | 0.0055 | 0.0 | 0.7236 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6724 | 0.0 | 0.2571 | 0.0102 | 0.0 | nan | 0.0 | 0.0240 | 0.0 | 0.0 | 0.8220 | 0.7057 | 0.8862 | 0.0 | 0.0 | 0.0039 | 0.0 |
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+ | 0.7813 | 5.0 | 1000 | 0.6767 | 0.2476 | 0.2950 | 0.8246 | nan | 0.8929 | 0.9575 | 0.3536 | 0.7434 | 0.3652 | nan | 0.3249 | 0.1722 | 0.0 | 0.9367 | 0.0 | 0.0 | nan | 0.0 | 0.0191 | 0.0 | 0.0 | 0.9078 | 0.0 | 0.4430 | 0.1157 | 0.0 | nan | 0.0 | 0.1126 | 0.0 | 0.0 | 0.9239 | 0.8638 | 0.9630 | 0.0 | 0.0 | 0.0504 | 0.0 | nan | 0.6867 | 0.8456 | 0.3488 | 0.6502 | 0.3199 | nan | 0.2679 | 0.1656 | 0.0 | 0.7052 | 0.0 | 0.0 | nan | 0.0 | 0.0189 | 0.0 | 0.0 | 0.6895 | 0.0 | 0.2999 | 0.1092 | 0.0 | nan | 0.0 | 0.1055 | 0.0 | 0.0 | 0.8259 | 0.6999 | 0.8927 | 0.0 | 0.0 | 0.0447 | 0.0 |
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+ | 0.7435 | 6.0 | 1200 | 0.6344 | 0.2760 | 0.3279 | 0.8357 | nan | 0.9055 | 0.9490 | 0.5276 | 0.7616 | 0.4465 | nan | 0.4423 | 0.6483 | 0.0 | 0.9186 | 0.0 | 0.0 | nan | 0.0 | 0.0494 | 0.0 | 0.0 | 0.9033 | 0.0 | 0.4453 | 0.1511 | 0.0 | nan | 0.0 | 0.2289 | 0.0 | 0.0 | 0.9545 | 0.7769 | 0.9534 | 0.0 | 0.0 | 0.1015 | 0.0 | nan | 0.7209 | 0.8647 | 0.5022 | 0.6648 | 0.3552 | nan | 0.3400 | 0.4843 | 0.0 | 0.7524 | 0.0 | 0.0 | nan | 0.0 | 0.0493 | 0.0 | 0.0 | 0.7024 | 0.0 | 0.3020 | 0.1429 | 0.0 | nan | 0.0 | 0.1947 | 0.0 | 0.0 | 0.8107 | 0.6785 | 0.9045 | 0.0 | 0.0 | 0.0868 | 0.0 |
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+ | 0.669 | 7.0 | 1400 | 0.6076 | 0.2955 | 0.3517 | 0.8449 | nan | 0.9070 | 0.9577 | 0.6665 | 0.7372 | 0.4645 | nan | 0.4506 | 0.7302 | 0.0 | 0.9348 | 0.0 | 0.0 | nan | 0.0 | 0.1936 | 0.0 | 0.0 | 0.8990 | 0.0 | 0.5097 | 0.2575 | 0.0 | nan | 0.0 | 0.2668 | 0.0 | 0.0 | 0.9282 | 0.8178 | 0.9587 | 0.0 | 0.0 | 0.2242 | 0.0 | nan | 0.7469 | 0.8615 | 0.5876 | 0.6676 | 0.3702 | nan | 0.3492 | 0.5076 | 0.0 | 0.7583 | 0.0 | 0.0 | nan | 0.0 | 0.1678 | 0.0 | 0.0 | 0.7182 | 0.0 | 0.3413 | 0.2286 | 0.0 | nan | 0.0 | 0.2275 | 0.0 | 0.0 | 0.8431 | 0.7139 | 0.9073 | 0.0 | 0.0 | 0.1654 | 0.0 |
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+ | 0.4628 | 8.0 | 1600 | 0.6011 | 0.2977 | 0.3549 | 0.8429 | nan | 0.8884 | 0.9527 | 0.5578 | 0.7506 | 0.4890 | nan | 0.4638 | 0.7406 | 0.0 | 0.9319 | 0.0 | 0.0 | nan | 0.0 | 0.3178 | 0.0 | 0.0 | 0.9010 | 0.0 | 0.4809 | 0.3284 | 0.0 | nan | 0.0 | 0.2448 | 0.0 | 0.0 | 0.9241 | 0.8614 | 0.9616 | 0.0 | 0.0 | 0.2085 | 0.0 | nan | 0.7334 | 0.8587 | 0.5336 | 0.6751 | 0.3767 | nan | 0.3613 | 0.5230 | 0.0 | 0.7468 | 0.0 | 0.0 | nan | 0.0 | 0.2516 | 0.0 | 0.0 | 0.7101 | 0.0 | 0.3365 | 0.2738 | 0.0 | nan | 0.0 | 0.2136 | 0.0 | 0.0 | 0.8427 | 0.7254 | 0.9085 | 0.0 | 0.0 | 0.1589 | 0.0 |
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+ | 1.3419 | 9.0 | 1800 | 0.5904 | 0.3029 | 0.3709 | 0.8462 | nan | 0.9057 | 0.9510 | 0.6361 | 0.7788 | 0.4788 | nan | 0.4900 | 0.8107 | 0.0 | 0.9469 | 0.0 | 0.0 | nan | 0.0 | 0.4336 | 0.0 | 0.0 | 0.8507 | 0.0 | 0.5368 | 0.3238 | 0.0 | nan | 0.0 | 0.2916 | 0.0 | 0.0 | 0.9233 | 0.8610 | 0.9637 | 0.0 | 0.0 | 0.3155 | 0.0 | nan | 0.7455 | 0.8668 | 0.5717 | 0.7045 | 0.3669 | nan | 0.3797 | 0.4684 | 0.0 | 0.7556 | 0.0 | 0.0 | nan | 0.0 | 0.2745 | 0.0 | 0.0 | 0.7145 | 0.0 | 0.3328 | 0.2865 | 0.0 | nan | 0.0 | 0.2461 | 0.0 | 0.0 | 0.8508 | 0.7261 | 0.9126 | 0.0 | 0.0 | 0.1879 | 0.0 |
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+ | 0.5734 | 10.0 | 2000 | 0.5937 | 0.3046 | 0.3620 | 0.8455 | nan | 0.8903 | 0.9576 | 0.4874 | 0.7802 | 0.4436 | nan | 0.5076 | 0.7526 | 0.0 | 0.9252 | 0.0 | 0.0 | nan | 0.0 | 0.3795 | 0.0 | 0.0 | 0.8933 | 0.0 | 0.5108 | 0.3422 | 0.0 | nan | 0.0 | 0.3250 | 0.0 | 0.0 | 0.9355 | 0.8361 | 0.9634 | 0.0 | 0.0 | 0.2901 | 0.0 | nan | 0.7358 | 0.8573 | 0.4710 | 0.6988 | 0.3621 | nan | 0.3880 | 0.5318 | 0.0 | 0.7827 | 0.0 | 0.0 | nan | 0.0 | 0.3118 | 0.0 | 0.0 | 0.7178 | 0.0 | 0.3584 | 0.2906 | 0.0 | nan | 0.0 | 0.2654 | 0.0 | 0.0 | 0.8469 | 0.7203 | 0.9165 | 0.0 | 0.0 | 0.1862 | 0.0 |
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+ | 0.3721 | 11.0 | 2200 | 0.5782 | 0.3079 | 0.3675 | 0.8503 | nan | 0.9192 | 0.9460 | 0.5240 | 0.8234 | 0.4822 | nan | 0.5023 | 0.7689 | 0.0 | 0.8965 | 0.0 | 0.0 | nan | 0.0 | 0.4271 | 0.0 | 0.0 | 0.9199 | 0.0 | 0.5107 | 0.3373 | 0.0 | nan | 0.0 | 0.4079 | 0.0 | 0.0 | 0.9468 | 0.8152 | 0.9624 | 0.0 | 0.0 | 0.2037 | 0.0 | nan | 0.7498 | 0.8668 | 0.5037 | 0.7028 | 0.3740 | nan | 0.3838 | 0.5157 | 0.0 | 0.7946 | 0.0 | 0.0 | nan | 0.0 | 0.3237 | 0.0 | 0.0 | 0.7340 | 0.0 | 0.3586 | 0.2842 | 0.0 | nan | 0.0 | 0.3043 | 0.0 | 0.0 | 0.8469 | 0.7219 | 0.9174 | 0.0 | 0.0 | 0.1618 | 0.0 |
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+ | 0.5698 | 12.0 | 2400 | 0.5601 | 0.3147 | 0.3796 | 0.8547 | nan | 0.9163 | 0.9502 | 0.6118 | 0.8102 | 0.5160 | nan | 0.4905 | 0.7891 | 0.0 | 0.9196 | 0.0 | 0.0 | nan | 0.0 | 0.4718 | 0.0 | 0.0 | 0.8898 | 0.0 | 0.5471 | 0.4070 | 0.0 | nan | 0.0 | 0.4158 | 0.0 | 0.0 | 0.9320 | 0.8523 | 0.9700 | 0.0 | 0.0 | 0.2787 | 0.0 | nan | 0.7765 | 0.8684 | 0.5737 | 0.7307 | 0.3860 | nan | 0.3917 | 0.5271 | 0.0 | 0.7968 | 0.0 | 0.0 | nan | 0.0 | 0.2862 | 0.0 | 0.0 | 0.7351 | 0.0 | 0.3521 | 0.3301 | 0.0 | nan | 0.0 | 0.3084 | 0.0 | 0.0 | 0.8525 | 0.7310 | 0.9171 | 0.0 | 0.0 | 0.1921 | 0.0 |
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+ | 0.4268 | 13.0 | 2600 | 0.5706 | 0.3144 | 0.3758 | 0.8546 | nan | 0.9149 | 0.9584 | 0.5832 | 0.8127 | 0.4595 | nan | 0.5052 | 0.7785 | 0.0 | 0.9385 | 0.0 | 0.0 | nan | 0.0 | 0.4538 | 0.0 | 0.0 | 0.8974 | 0.0 | 0.5469 | 0.3876 | 0.0 | nan | 0.0 | 0.3971 | 0.0 | 0.0 | 0.9269 | 0.8398 | 0.9644 | 0.0 | 0.0 | 0.2853 | 0.0 | nan | 0.7632 | 0.8670 | 0.5583 | 0.7345 | 0.3711 | nan | 0.3931 | 0.5330 | 0.0 | 0.7866 | 0.0 | 0.0 | nan | 0.0 | 0.3082 | 0.0 | 0.0 | 0.7333 | 0.0 | 0.3694 | 0.3199 | 0.0 | nan | 0.0 | 0.3094 | 0.0 | 0.0 | 0.8565 | 0.7309 | 0.9212 | 0.0 | 0.0 | 0.1913 | 0.0 |
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+ | 0.3612 | 14.0 | 2800 | 0.5615 | 0.3182 | 0.3797 | 0.8587 | nan | 0.9143 | 0.9554 | 0.6527 | 0.8256 | 0.4717 | nan | 0.5467 | 0.7521 | 0.0 | 0.9229 | 0.0 | 0.0 | nan | 0.0 | 0.4951 | 0.0 | 0.0 | 0.9226 | 0.0 | 0.5236 | 0.3953 | 0.0 | nan | 0.0 | 0.4144 | 0.0 | 0.0 | 0.9329 | 0.8347 | 0.9637 | 0.0 | 0.0 | 0.2473 | 0.0 | nan | 0.7803 | 0.8714 | 0.5854 | 0.7509 | 0.3843 | nan | 0.4085 | 0.5363 | 0.0 | 0.7994 | 0.0 | 0.0 | nan | 0.0 | 0.3118 | 0.0 | 0.0 | 0.7381 | 0.0 | 0.3688 | 0.3250 | 0.0 | nan | 0.0 | 0.3138 | 0.0 | 0.0 | 0.8547 | 0.7242 | 0.9233 | 0.0 | 0.0 | 0.1865 | 0.0 |
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+ | 0.2714 | 15.0 | 3000 | 0.5687 | 0.3165 | 0.3819 | 0.8557 | nan | 0.9102 | 0.9546 | 0.5390 | 0.8252 | 0.4823 | nan | 0.5408 | 0.8170 | 0.0 | 0.9223 | 0.0 | 0.0 | nan | 0.0 | 0.5004 | 0.0 | 0.0 | 0.8929 | 0.0 | 0.5402 | 0.4228 | 0.0 | nan | 0.0 | 0.4218 | 0.0 | 0.0 | 0.9324 | 0.8585 | 0.9674 | 0.0 | 0.0 | 0.3121 | 0.0 | nan | 0.7610 | 0.8703 | 0.5177 | 0.7369 | 0.3866 | nan | 0.4110 | 0.5213 | 0.0 | 0.8051 | 0.0 | 0.0 | nan | 0.0 | 0.3223 | 0.0 | 0.0 | 0.7368 | 0.0 | 0.3795 | 0.3303 | 0.0 | nan | 0.0 | 0.3181 | 0.0 | 0.0 | 0.8558 | 0.7279 | 0.9245 | 0.0 | 0.0 | 0.2057 | 0.0 |
140
+ | 0.3566 | 16.0 | 3200 | 0.5746 | 0.3154 | 0.3831 | 0.8543 | nan | 0.9156 | 0.9537 | 0.5807 | 0.8263 | 0.4642 | nan | 0.5228 | 0.8130 | 0.0 | 0.9300 | 0.0 | 0.0 | nan | 0.0 | 0.5625 | 0.0 | 0.0 | 0.8697 | 0.0 | 0.5473 | 0.3708 | 0.0 | nan | 0.0 | 0.4250 | 0.0 | 0.0 | 0.9320 | 0.8671 | 0.9687 | 0.0 | 0.0 | 0.3279 | 0.0 | nan | 0.7622 | 0.8696 | 0.5476 | 0.7333 | 0.3773 | nan | 0.4062 | 0.5234 | 0.0 | 0.8021 | 0.0 | 0.0 | nan | 0.0 | 0.3295 | 0.0 | 0.0 | 0.7271 | 0.0 | 0.3621 | 0.3111 | 0.0 | nan | 0.0 | 0.3157 | 0.0 | 0.0 | 0.8569 | 0.7294 | 0.9246 | 0.0 | 0.0 | 0.1997 | 0.0 |
141
+ | 0.3486 | 17.0 | 3400 | 0.5716 | 0.3168 | 0.3810 | 0.8550 | nan | 0.9087 | 0.9592 | 0.5998 | 0.7778 | 0.4799 | nan | 0.5562 | 0.7959 | 0.0 | 0.9295 | 0.0 | 0.0 | nan | 0.0 | 0.4951 | 0.0 | 0.0 | 0.8892 | 0.0 | 0.5531 | 0.3860 | 0.0 | nan | 0.0 | 0.4274 | 0.0 | 0.0 | 0.9295 | 0.8539 | 0.9663 | 0.0 | 0.0 | 0.3037 | 0.0 | nan | 0.7539 | 0.8708 | 0.5570 | 0.7185 | 0.3903 | nan | 0.4183 | 0.5347 | 0.0 | 0.7991 | 0.0 | 0.0 | nan | 0.0 | 0.3381 | 0.0 | 0.0 | 0.7322 | 0.0 | 0.3614 | 0.3128 | 0.0 | nan | 0.0 | 0.3210 | 0.0 | 0.0 | 0.8602 | 0.7322 | 0.9260 | 0.0 | 0.0 | 0.1958 | 0.0 |
142
+ | 0.2541 | 18.0 | 3600 | 0.5641 | 0.3185 | 0.3816 | 0.8573 | nan | 0.9128 | 0.9560 | 0.6054 | 0.8210 | 0.4610 | nan | 0.5325 | 0.7977 | 0.0 | 0.9235 | 0.0 | 0.0 | nan | 0.0 | 0.5042 | 0.0 | 0.0 | 0.9042 | 0.0 | 0.5403 | 0.3783 | 0.0 | nan | 0.0 | 0.4368 | 0.0 | 0.0 | 0.9330 | 0.8523 | 0.9728 | 0.0 | 0.0 | 0.2982 | 0.0 | nan | 0.7718 | 0.8707 | 0.5635 | 0.7389 | 0.3730 | nan | 0.4092 | 0.5344 | 0.0 | 0.8080 | 0.0 | 0.0 | nan | 0.0 | 0.3504 | 0.0 | 0.0 | 0.7379 | 0.0 | 0.3729 | 0.3107 | 0.0 | nan | 0.0 | 0.3226 | 0.0 | 0.0 | 0.8587 | 0.7271 | 0.9226 | 0.0 | 0.0 | 0.2016 | 0.0 |
143
+ | 0.3204 | 19.0 | 3800 | 0.5781 | 0.3170 | 0.3798 | 0.8554 | nan | 0.9236 | 0.9609 | 0.5748 | 0.7706 | 0.4581 | nan | 0.5274 | 0.7996 | 0.0 | 0.9291 | 0.0 | 0.0 | nan | 0.0 | 0.5223 | 0.0 | 0.0 | 0.9019 | 0.0 | 0.5425 | 0.3743 | 0.0 | nan | 0.0 | 0.4318 | 0.0 | 0.0 | 0.9265 | 0.8595 | 0.9685 | 0.0 | 0.0 | 0.3016 | 0.0 | nan | 0.7477 | 0.8692 | 0.5451 | 0.7127 | 0.3767 | nan | 0.4137 | 0.5304 | 0.0 | 0.8061 | 0.0 | 0.0 | nan | 0.0 | 0.3493 | 0.0 | 0.0 | 0.7376 | 0.0 | 0.3776 | 0.3140 | 0.0 | nan | 0.0 | 0.3249 | 0.0 | 0.0 | 0.8609 | 0.7330 | 0.9252 | 0.0 | 0.0 | 0.2027 | 0.0 |
144
+ | 0.3648 | 20.0 | 4000 | 0.5669 | 0.3190 | 0.3821 | 0.8574 | nan | 0.9199 | 0.9566 | 0.5855 | 0.8187 | 0.4712 | nan | 0.5293 | 0.7962 | 0.0 | 0.9245 | 0.0 | 0.0 | nan | 0.0 | 0.5123 | 0.0 | 0.0 | 0.8912 | 0.0 | 0.5405 | 0.3863 | 0.0 | nan | 0.0 | 0.4414 | 0.0 | 0.0 | 0.9331 | 0.8636 | 0.9713 | 0.0000 | 0.0 | 0.3037 | 0.0 | nan | 0.7635 | 0.8715 | 0.5513 | 0.7441 | 0.3799 | nan | 0.4143 | 0.5353 | 0.0 | 0.8080 | 0.0 | 0.0 | nan | 0.0 | 0.3478 | 0.0 | 0.0 | 0.7370 | 0.0 | 0.3749 | 0.3203 | 0.0 | nan | 0.0 | 0.3284 | 0.0 | 0.0 | 0.8586 | 0.7283 | 0.9243 | 0.0000 | 0.0 | 0.2014 | 0.0 |
145
+
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147
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