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segformer-b0-finetuned-segments-sidewalk-oct-22

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End of training

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  1. README.md +58 -93
  2. config.json +1 -1
  3. model.safetensors +1 -1
  4. runs/Dec09_12-46-11_59526bf0f3e3/events.out.tfevents.1733748414.59526bf0f3e3.477.0 +3 -0
  5. runs/Dec09_12-51-30_59526bf0f3e3/events.out.tfevents.1733748707.59526bf0f3e3.477.1 +3 -0
  6. runs/Dec09_12-53-53_59526bf0f3e3/events.out.tfevents.1733748853.59526bf0f3e3.477.2 +3 -0
  7. runs/Dec09_12-56-00_59526bf0f3e3/events.out.tfevents.1733748976.59526bf0f3e3.477.3 +3 -0
  8. runs/Dec09_12-59-42_59526bf0f3e3/events.out.tfevents.1733749304.59526bf0f3e3.477.4 +3 -0
  9. training_args.bin +1 -1
README.md CHANGED
@@ -18,80 +18,80 @@ 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: 0.7351
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- - Mean Iou: 0.2097
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- - Mean Accuracy: 0.2588
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- - Overall Accuracy: 0.7962
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  - Accuracy Unlabeled: nan
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- - Accuracy Flat-road: 0.7781
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- - Accuracy Flat-sidewalk: 0.9422
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  - Accuracy Flat-crosswalk: 0.0
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- - Accuracy Flat-cyclinglane: 0.6678
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- - Accuracy Flat-parkingdriveway: 0.5103
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  - Accuracy Flat-railtrack: nan
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- - Accuracy Flat-curb: 0.3970
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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: 0.9361
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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: 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: 0.0
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- - Accuracy Construction-building: 0.9283
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  - Accuracy Construction-door: 0.0
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- - Accuracy Construction-wall: 0.2268
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- - Accuracy Construction-fenceguardrail: 0.0000
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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: 0.0873
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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: 0.9230
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- - Accuracy Nature-terrain: 0.9084
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- - Accuracy Sky: 0.9461
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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: 0.0306
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- - Accuracy Void-unclear: 0.0
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  - Iou Unlabeled: nan
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- - Iou Flat-road: 0.6262
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- - Iou Flat-sidewalk: 0.8234
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  - Iou Flat-crosswalk: 0.0
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- - Iou Flat-cyclinglane: 0.5624
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- - Iou Flat-parkingdriveway: 0.3620
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  - Iou Flat-railtrack: nan
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- - Iou Flat-curb: 0.3029
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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: 0.7060
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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: 0.0
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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: 0.6436
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  - Iou Construction-door: 0.0
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- - Iou Construction-wall: 0.1895
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- - Iou Construction-fenceguardrail: 0.0000
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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: 0.0808
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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: 0.7906
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- - Iou Nature-terrain: 0.7024
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- - Iou Sky: 0.8933
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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: 0.0273
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- - Iou Void-unclear: 0.0
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.0002
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- - train_batch_size: 2
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- - eval_batch_size: 2
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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: 2
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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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- | 2.6052 | 0.05 | 20 | 2.6583 | 0.0781 | 0.1236 | 0.5809 | nan | 0.1191 | 0.9803 | 0.0 | 0.0 | 0.0014 | nan | 0.0011 | 0.0 | 0.0 | 0.9544 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6683 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8576 | 0.0002 | 0.3741 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.1069 | 0.5811 | 0.0 | 0.0 | 0.0014 | nan | 0.0011 | 0.0 | 0.0 | 0.4003 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4146 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6237 | 0.0002 | 0.3710 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.9158 | 0.1 | 40 | 1.8276 | 0.0852 | 0.1341 | 0.6115 | nan | 0.8028 | 0.8197 | 0.0 | 0.0 | 0.0043 | nan | 0.0013 | 0.0 | 0.0 | 0.8105 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8498 | 0.0 | 0.0002 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9232 | 0.0006 | 0.0783 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3391 | 0.6942 | 0.0 | 0.0 | 0.0042 | nan | 0.0013 | 0.0 | 0.0 | 0.5540 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4445 | 0.0 | 0.0002 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6091 | 0.0006 | 0.0781 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.4713 | 0.15 | 60 | 1.5056 | 0.1027 | 0.1504 | 0.6575 | nan | 0.6471 | 0.9460 | 0.0 | 0.0 | 0.0031 | nan | 0.0043 | 0.0 | 0.0 | 0.9179 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8383 | 0.0 | 0.0002 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9344 | 0.0030 | 0.5194 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3964 | 0.7171 | 0.0 | 0.0 | 0.0031 | nan | 0.0043 | 0.0 | 0.0 | 0.5233 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5092 | 0.0 | 0.0002 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6168 | 0.0030 | 0.5139 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.1025 | 0.2 | 80 | 1.3972 | 0.1167 | 0.1598 | 0.6714 | nan | 0.6796 | 0.9507 | 0.0 | 0.0001 | 0.0002 | nan | 0.0017 | 0.0 | 0.0 | 0.7917 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8734 | 0.0 | 0.0090 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9286 | 0.0247 | 0.8540 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4167 | 0.7149 | 0.0 | 0.0001 | 0.0002 | nan | 0.0017 | 0.0 | 0.0 | 0.6355 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5041 | 0.0 | 0.0087 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6289 | 0.0244 | 0.7983 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.9869 | 0.25 | 100 | 1.3690 | 0.1082 | 0.1580 | 0.6565 | nan | 0.5658 | 0.9480 | 0.0 | 0.0005 | 0.0005 | nan | 0.0005 | 0.0 | 0.0 | 0.9066 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7864 | 0.0 | 0.0055 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9378 | 0.0041 | 0.9016 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3963 | 0.6944 | 0.0 | 0.0005 | 0.0005 | nan | 0.0005 | 0.0 | 0.0 | 0.4568 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5445 | 0.0 | 0.0053 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5857 | 0.0041 | 0.7738 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.126 | 0.3 | 120 | 1.2700 | 0.1288 | 0.1754 | 0.6867 | nan | 0.8098 | 0.8984 | 0.0 | 0.0093 | 0.0002 | nan | 0.0001 | 0.0 | 0.0 | 0.8207 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8592 | 0.0 | 0.0043 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9337 | 0.3991 | 0.8793 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4231 | 0.7335 | 0.0 | 0.0093 | 0.0002 | nan | 0.0001 | 0.0 | 0.0 | 0.6281 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5333 | 0.0 | 0.0042 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6647 | 0.3577 | 0.7679 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.3711 | 0.35 | 140 | 1.1517 | 0.1452 | 0.1931 | 0.7186 | nan | 0.7518 | 0.9370 | 0.0 | 0.2270 | 0.0011 | nan | 0.0001 | 0.0 | 0.0 | 0.9359 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8504 | 0.0 | 0.0036 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9066 | 0.6993 | 0.8660 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4932 | 0.7384 | 0.0 | 0.2135 | 0.0011 | nan | 0.0001 | 0.0 | 0.0 | 0.5199 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5779 | 0.0 | 0.0035 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7254 | 0.5490 | 0.8244 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.3994 | 0.4 | 160 | 1.1185 | 0.1508 | 0.1970 | 0.7199 | nan | 0.8502 | 0.9062 | 0.0 | 0.2392 | 0.0004 | nan | 0.0001 | 0.0 | 0.0 | 0.8281 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8655 | 0.0 | 0.0017 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8850 | 0.7953 | 0.9333 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4463 | 0.7613 | 0.0 | 0.2293 | 0.0004 | nan | 0.0001 | 0.0 | 0.0 | 0.6460 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5373 | 0.0 | 0.0017 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7474 | 0.6127 | 0.8426 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 0.911 | 0.45 | 180 | 1.0558 | 0.1550 | 0.2026 | 0.7305 | nan | 0.7464 | 0.9479 | 0.0 | 0.4110 | 0.0128 | nan | 0.0013 | 0.0 | 0.0 | 0.9009 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9054 | 0.0 | 0.0008 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8071 | 0.8876 | 0.8631 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5120 | 0.7481 | 0.0 | 0.3866 | 0.0127 | nan | 0.0013 | 0.0 | 0.0 | 0.6344 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5574 | 0.0 | 0.0007 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7060 | 0.5653 | 0.8353 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.8468 | 0.5 | 200 | 1.0063 | 0.1573 | 0.2034 | 0.7369 | nan | 0.8325 | 0.9268 | 0.0 | 0.3866 | 0.0021 | nan | 0.0029 | 0.0 | 0.0 | 0.9098 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8590 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9134 | 0.7506 | 0.9239 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4926 | 0.7629 | 0.0 | 0.3684 | 0.0021 | nan | 0.0029 | 0.0 | 0.0 | 0.6087 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5709 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7512 | 0.6387 | 0.8367 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 0.8309 | 0.55 | 220 | 0.9882 | 0.1604 | 0.2073 | 0.7393 | nan | 0.7298 | 0.9406 | 0.0 | 0.3782 | 0.0239 | nan | 0.0704 | 0.0 | 0.0 | 0.9040 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8975 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9062 | 0.8872 | 0.8960 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5191 | 0.7437 | 0.0 | 0.3403 | 0.0235 | nan | 0.0654 | 0.0 | 0.0 | 0.6150 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5780 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7599 | 0.6547 | 0.8333 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 1.5283 | 0.6 | 240 | 0.9686 | 0.1657 | 0.2120 | 0.7435 | nan | 0.8319 | 0.9088 | 0.0 | 0.5127 | 0.0254 | nan | 0.1306 | 0.0 | 0.0 | 0.8869 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8993 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9219 | 0.7303 | 0.9347 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.5035 | 0.7652 | 0.0 | 0.4476 | 0.0250 | nan | 0.1151 | 0.0 | 0.0 | 0.6605 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5813 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7522 | 0.6158 | 0.8355 | 0.0 | 0.0 | 0.0000 | 0.0 |
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- | 0.5838 | 0.65 | 260 | 0.9858 | 0.1667 | 0.2164 | 0.7450 | nan | 0.7064 | 0.9452 | 0.0 | 0.4848 | 0.1076 | nan | 0.1731 | 0.0 | 0.0 | 0.9220 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8139 | 0.0 | 0.0006 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9085 | 0.9244 | 0.9371 | 0.0 | 0.0 | 0.0006 | 0.0 | nan | 0.5492 | 0.7671 | 0.0 | 0.4391 | 0.1003 | nan | 0.1451 | 0.0 | 0.0 | 0.6131 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5788 | 0.0 | 0.0006 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6824 | 0.5950 | 0.8630 | 0.0 | 0.0 | 0.0006 | 0.0 |
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- | 1.0103 | 0.7 | 280 | 0.9187 | 0.1717 | 0.2174 | 0.7573 | nan | 0.7729 | 0.9468 | 0.0 | 0.5562 | 0.1364 | nan | 0.1327 | 0.0 | 0.0 | 0.9128 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8647 | 0.0 | 0.0024 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9379 | 0.7704 | 0.9241 | 0.0 | 0.0 | 0.0001 | 0.0 | nan | 0.5545 | 0.7857 | 0.0 | 0.4650 | 0.1235 | nan | 0.1163 | 0.0 | 0.0 | 0.6424 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5814 | 0.0 | 0.0024 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7353 | 0.6449 | 0.8444 | 0.0 | 0.0 | 0.0001 | 0.0 |
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- | 0.7643 | 0.75 | 300 | 0.9432 | 0.1678 | 0.2079 | 0.7494 | nan | 0.7337 | 0.9651 | 0.0 | 0.5139 | 0.0532 | nan | 0.0619 | 0.0 | 0.0 | 0.8188 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8693 | 0.0 | 0.0020 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9373 | 0.7835 | 0.9152 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5522 | 0.7514 | 0.0 | 0.4670 | 0.0507 | nan | 0.0576 | 0.0 | 0.0 | 0.6797 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5663 | 0.0 | 0.0019 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7301 | 0.6469 | 0.8644 | 0.0 | 0.0 | 0.0 | 0.0 |
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- | 0.6425 | 0.8 | 320 | 0.9611 | 0.1722 | 0.2218 | 0.7449 | nan | 0.6811 | 0.9292 | 0.0 | 0.5486 | 0.1368 | nan | 0.2891 | 0.0 | 0.0 | 0.8899 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9512 | 0.0 | 0.0013 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8459 | 0.9144 | 0.9087 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5319 | 0.7647 | 0.0 | 0.4224 | 0.1236 | nan | 0.2104 | 0.0 | 0.0 | 0.6475 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5370 | 0.0 | 0.0013 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7571 | 0.6566 | 0.8577 | 0.0 | 0.0 | 0.0 | 0.0 |
141
- | 0.9292 | 0.85 | 340 | 0.8890 | 0.1759 | 0.2213 | 0.7594 | nan | 0.6721 | 0.9524 | 0.0 | 0.6827 | 0.1609 | nan | 0.1927 | 0.0 | 0.0 | 0.8767 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8994 | 0.0 | 0.0025 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9567 | 0.7380 | 0.9474 | 0.0 | 0.0 | 0.0005 | 0.0 | nan | 0.5323 | 0.7863 | 0.0 | 0.4764 | 0.1441 | nan | 0.1562 | 0.0 | 0.0 | 0.6933 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5952 | 0.0 | 0.0025 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7359 | 0.6399 | 0.8669 | 0.0 | 0.0 | 0.0005 | 0.0 |
142
- | 0.6201 | 0.9 | 360 | 0.8572 | 0.1871 | 0.2354 | 0.7712 | nan | 0.6941 | 0.9488 | 0.0 | 0.6405 | 0.4223 | nan | 0.2294 | 0.0 | 0.0 | 0.9290 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8868 | 0.0 | 0.0532 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.9245 | 0.8871 | 0.9148 | 0.0 | 0.0 | 0.0014 | 0.0 | nan | 0.5658 | 0.7930 | 0.0 | 0.5446 | 0.2794 | nan | 0.1875 | 0.0 | 0.0 | 0.6262 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6075 | 0.0 | 0.0508 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.7662 | 0.6993 | 0.8658 | 0.0 | 0.0 | 0.0014 | 0.0 |
143
- | 1.0028 | 0.95 | 380 | 0.8642 | 0.1790 | 0.2248 | 0.7648 | nan | 0.7265 | 0.9510 | 0.0 | 0.6286 | 0.1598 | nan | 0.1719 | 0.0 | 0.0 | 0.9213 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9119 | 0.0 | 0.0156 | 0.0 | 0.0 | nan | 0.0 | 0.0007 | 0.0 | 0.0 | 0.9108 | 0.8756 | 0.9200 | 0.0 | 0.0 | 0.0011 | 0.0 | nan | 0.5445 | 0.7817 | 0.0 | 0.4995 | 0.1473 | nan | 0.1486 | 0.0 | 0.0 | 0.6385 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5978 | 0.0 | 0.0153 | 0.0 | 0.0 | nan | 0.0 | 0.0007 | 0.0 | 0.0 | 0.7795 | 0.6997 | 0.8726 | 0.0 | 0.0 | 0.0011 | 0.0 |
144
- | 0.8125 | 1.0 | 400 | 0.8575 | 0.1836 | 0.2336 | 0.7707 | nan | 0.8044 | 0.9387 | 0.0 | 0.5612 | 0.2633 | nan | 0.2876 | 0.0 | 0.0 | 0.9481 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8694 | 0.0 | 0.0216 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.8947 | 0.9314 | 0.9469 | 0.0 | 0.0 | 0.0072 | 0.0 | nan | 0.5789 | 0.7979 | 0.0 | 0.4938 | 0.2217 | nan | 0.2231 | 0.0 | 0.0 | 0.6085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6185 | 0.0 | 0.0214 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.7762 | 0.6458 | 0.8823 | 0.0 | 0.0 | 0.0069 | 0.0 |
145
- | 0.7077 | 1.05 | 420 | 0.8753 | 0.1832 | 0.2253 | 0.7673 | nan | 0.7668 | 0.9672 | 0.0 | 0.4280 | 0.2171 | nan | 0.2647 | 0.0 | 0.0 | 0.8745 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9391 | 0.0 | 0.0469 | 0.0 | 0.0 | nan | 0.0 | 0.0010 | 0.0 | 0.0 | 0.8847 | 0.8916 | 0.9224 | 0.0 | 0.0 | 0.0064 | 0.0 | nan | 0.5851 | 0.7829 | 0.0 | 0.4063 | 0.1772 | nan | 0.2167 | 0.0 | 0.0 | 0.6977 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5724 | 0.0 | 0.0444 | 0.0 | 0.0 | nan | 0.0 | 0.0010 | 0.0 | 0.0 | 0.7869 | 0.7071 | 0.8785 | 0.0 | 0.0 | 0.0060 | 0.0 |
146
- | 0.5107 | 1.1 | 440 | 0.8497 | 0.1901 | 0.2344 | 0.7732 | nan | 0.7275 | 0.9648 | 0.0 | 0.6019 | 0.1918 | nan | 0.2115 | 0.0 | 0.0 | 0.8908 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8728 | 0.0 | 0.2648 | 0.0 | 0.0 | nan | 0.0 | 0.0023 | 0.0 | 0.0 | 0.9263 | 0.9003 | 0.9358 | 0.0 | 0.0 | 0.0106 | 0.0 | nan | 0.5929 | 0.7820 | 0.0 | 0.4854 | 0.1647 | nan | 0.1832 | 0.0 | 0.0 | 0.7128 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6123 | 0.0 | 0.1969 | 0.0 | 0.0 | nan | 0.0 | 0.0023 | 0.0 | 0.0 | 0.7653 | 0.6963 | 0.8804 | 0.0 | 0.0 | 0.0098 | 0.0 |
147
- | 0.864 | 1.15 | 460 | 0.8285 | 0.1871 | 0.2330 | 0.7743 | nan | 0.8138 | 0.9395 | 0.0 | 0.6150 | 0.2174 | nan | 0.2799 | 0.0 | 0.0 | 0.9398 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8939 | 0.0 | 0.0380 | 0.0 | 0.0 | nan | 0.0 | 0.0071 | 0.0 | 0.0 | 0.9182 | 0.8546 | 0.9326 | 0.0 | 0.0 | 0.0072 | 0.0 | nan | 0.5785 | 0.7945 | 0.0 | 0.4986 | 0.1889 | nan | 0.2285 | 0.0 | 0.0 | 0.6345 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6203 | 0.0 | 0.0356 | 0.0 | 0.0 | nan | 0.0 | 0.0071 | 0.0 | 0.0 | 0.7858 | 0.7263 | 0.8831 | 0.0 | 0.0 | 0.0070 | 0.0 |
148
- | 1.1038 | 1.2 | 480 | 0.8424 | 0.1934 | 0.2389 | 0.7756 | nan | 0.6686 | 0.9679 | 0.0 | 0.6309 | 0.3414 | nan | 0.3363 | 0.0 | 0.0 | 0.8914 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9260 | 0.0 | 0.0435 | 0.0 | 0.0 | nan | 0.0 | 0.0439 | 0.0 | 0.0 | 0.8998 | 0.9057 | 0.9432 | 0.0 | 0.0 | 0.0451 | 0.0 | nan | 0.5839 | 0.7944 | 0.0 | 0.5166 | 0.2481 | nan | 0.2274 | 0.0 | 0.0 | 0.7174 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5978 | 0.0 | 0.0388 | 0.0 | 0.0 | nan | 0.0 | 0.0429 | 0.0 | 0.0 | 0.7874 | 0.7051 | 0.8895 | 0.0 | 0.0 | 0.0383 | 0.0 |
149
- | 0.7316 | 1.25 | 500 | 0.8044 | 0.2013 | 0.2515 | 0.7831 | nan | 0.7636 | 0.9435 | 0.0 | 0.5808 | 0.4689 | nan | 0.4001 | 0.0 | 0.0 | 0.9282 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8854 | 0.0 | 0.2230 | 0.0 | 0.0 | nan | 0.0 | 0.0374 | 0.0 | 0.0 | 0.9060 | 0.9175 | 0.9477 | 0.0 | 0.0 | 0.0447 | 0.0 | nan | 0.5973 | 0.8132 | 0.0 | 0.5180 | 0.3150 | nan | 0.2651 | 0.0 | 0.0 | 0.6912 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6288 | 0.0 | 0.1796 | 0.0 | 0.0 | nan | 0.0 | 0.0365 | 0.0 | 0.0 | 0.7830 | 0.6928 | 0.8835 | 0.0 | 0.0 | 0.0364 | 0.0 |
150
- | 0.8354 | 1.3 | 520 | 0.8133 | 0.1914 | 0.2358 | 0.7725 | nan | 0.8282 | 0.9275 | 0.0 | 0.4710 | 0.3078 | nan | 0.2966 | 0.0 | 0.0 | 0.9124 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9318 | 0.0 | 0.1146 | 0.0 | 0.0 | nan | 0.0 | 0.0343 | 0.0 | 0.0 | 0.9260 | 0.8574 | 0.9263 | 0.0 | 0.0 | 0.0108 | 0.0 | nan | 0.5677 | 0.7906 | 0.0 | 0.4449 | 0.2616 | nan | 0.2338 | 0.0 | 0.0 | 0.7037 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6056 | 0.0 | 0.1018 | 0.0 | 0.0 | nan | 0.0 | 0.0340 | 0.0 | 0.0 | 0.7789 | 0.7060 | 0.8866 | 0.0 | 0.0 | 0.0101 | 0.0 |
151
- | 0.393 | 1.35 | 540 | 0.7798 | 0.2001 | 0.2497 | 0.7829 | nan | 0.7838 | 0.9291 | 0.0 | 0.6871 | 0.3828 | nan | 0.3642 | 0.0 | 0.0 | 0.9158 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8658 | 0.0 | 0.2278 | 0.0 | 0.0 | nan | 0.0 | 0.0377 | 0.0 | 0.0 | 0.9394 | 0.8976 | 0.9466 | 0.0 | 0.0 | 0.0123 | 0.0 | nan | 0.6044 | 0.8051 | 0.0 | 0.5457 | 0.2971 | nan | 0.2586 | 0.0 | 0.0 | 0.6959 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6314 | 0.0 | 0.1836 | 0.0 | 0.0 | nan | 0.0 | 0.0368 | 0.0 | 0.0 | 0.7648 | 0.6827 | 0.8847 | 0.0 | 0.0 | 0.0115 | 0.0 |
152
- | 0.8005 | 1.4 | 560 | 0.7905 | 0.1999 | 0.2438 | 0.7834 | nan | 0.7293 | 0.9711 | 0.0 | 0.6267 | 0.2994 | nan | 0.2955 | 0.0 | 0.0 | 0.9193 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9042 | 0.0 | 0.3408 | 0.0 | 0.0 | nan | 0.0 | 0.0460 | 0.0 | 0.0 | 0.9256 | 0.8096 | 0.9264 | 0.0 | 0.0 | 0.0067 | 0.0 | nan | 0.6192 | 0.7916 | 0.0 | 0.5595 | 0.2382 | nan | 0.2245 | 0.0 | 0.0 | 0.6994 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6221 | 0.0 | 0.2395 | 0.0 | 0.0 | nan | 0.0 | 0.0452 | 0.0 | 0.0 | 0.7818 | 0.6788 | 0.8901 | 0.0 | 0.0 | 0.0065 | 0.0 |
153
- | 1.1368 | 1.45 | 580 | 0.8022 | 0.1974 | 0.2481 | 0.7734 | nan | 0.8556 | 0.8998 | 0.0 | 0.5845 | 0.3805 | nan | 0.2213 | 0.0 | 0.0 | 0.9201 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8873 | 0.0 | 0.3416 | 0.0 | 0.0 | nan | 0.0 | 0.0725 | 0.0 | 0.0 | 0.9076 | 0.9167 | 0.9397 | 0.0 | 0.0 | 0.0117 | 0.0 | nan | 0.5673 | 0.7996 | 0.0 | 0.5118 | 0.2996 | nan | 0.1588 | 0.0 | 0.0 | 0.6774 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6371 | 0.0 | 0.2584 | 0.0 | 0.0 | nan | 0.0 | 0.0693 | 0.0 | 0.0 | 0.7568 | 0.6834 | 0.8876 | 0.0 | 0.0 | 0.0112 | 0.0 |
154
- | 0.6787 | 1.5 | 600 | 0.7612 | 0.2084 | 0.2588 | 0.7929 | nan | 0.7932 | 0.9457 | 0.0 | 0.6633 | 0.4719 | nan | 0.3431 | 0.0 | 0.0 | 0.9169 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8916 | 0.0 | 0.3567 | 0.0 | 0.0 | nan | 0.0 | 0.0888 | 0.0 | 0.0 | 0.9000 | 0.9375 | 0.9516 | 0.0 | 0.0 | 0.0214 | 0.0 | nan | 0.6295 | 0.8204 | 0.0 | 0.5823 | 0.3363 | nan | 0.2593 | 0.0 | 0.0 | 0.7102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6424 | 0.0 | 0.2590 | 0.0 | 0.0 | nan | 0.0 | 0.0830 | 0.0 | 0.0 | 0.7745 | 0.6670 | 0.8865 | 0.0 | 0.0 | 0.0198 | 0.0 |
155
- | 1.1082 | 1.55 | 620 | 0.7512 | 0.2110 | 0.2590 | 0.7960 | nan | 0.7743 | 0.9544 | 0.0 | 0.6595 | 0.5236 | nan | 0.3373 | 0.0 | 0.0 | 0.9282 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8915 | 0.0 | 0.3487 | 0.0 | 0.0 | nan | 0.0 | 0.0861 | 0.0 | 0.0 | 0.9251 | 0.8795 | 0.9459 | 0.0 | 0.0 | 0.0328 | 0.0 | nan | 0.6434 | 0.8168 | 0.0 | 0.5709 | 0.3471 | nan | 0.2607 | 0.0 | 0.0 | 0.7052 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6444 | 0.0 | 0.2489 | 0.0 | 0.0 | nan | 0.0 | 0.0802 | 0.0 | 0.0 | 0.7869 | 0.7279 | 0.8912 | 0.0 | 0.0 | 0.0287 | 0.0 |
156
- | 0.4478 | 1.6 | 640 | 0.7734 | 0.2059 | 0.2576 | 0.7835 | nan | 0.7156 | 0.9429 | 0.0 | 0.6800 | 0.4737 | nan | 0.3374 | 0.0 | 0.0 | 0.9302 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8480 | 0.0 | 0.4120 | 0.0 | 0.0 | nan | 0.0 | 0.0924 | 0.0 | 0.0 | 0.9317 | 0.8875 | 0.9497 | 0.0 | 0.0 | 0.0435 | 0.0 | nan | 0.5951 | 0.8091 | 0.0 | 0.4900 | 0.3152 | nan | 0.2683 | 0.0 | 0.0 | 0.7103 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6391 | 0.0 | 0.2632 | 0.0 | 0.0 | nan | 0.0 | 0.0853 | 0.0 | 0.0 | 0.7759 | 0.7108 | 0.8898 | 0.0 | 0.0 | 0.0372 | 0.0 |
157
- | 0.4418 | 1.65 | 660 | 0.7509 | 0.2086 | 0.2561 | 0.7908 | nan | 0.7932 | 0.9408 | 0.0 | 0.6657 | 0.4764 | nan | 0.3407 | 0.0 | 0.0 | 0.8987 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9229 | 0.0 | 0.3130 | 0.0 | 0.0 | nan | 0.0 | 0.0713 | 0.0 | 0.0 | 0.9002 | 0.8990 | 0.9457 | 0.0 | 0.0 | 0.0273 | 0.0 | nan | 0.6236 | 0.8125 | 0.0 | 0.5500 | 0.3411 | nan | 0.2735 | 0.0 | 0.0 | 0.7468 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6110 | 0.0 | 0.2254 | 0.0 | 0.0 | nan | 0.0 | 0.0675 | 0.0 | 0.0 | 0.7911 | 0.7172 | 0.8902 | 0.0 | 0.0 | 0.0245 | 0.0 |
158
- | 0.5509 | 1.7 | 680 | 0.7444 | 0.2086 | 0.2558 | 0.7954 | nan | 0.7851 | 0.9546 | 0.0 | 0.6568 | 0.4434 | nan | 0.3502 | 0.0 | 0.0 | 0.9222 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9121 | 0.0 | 0.2909 | 0.0 | 0.0 | nan | 0.0 | 0.0788 | 0.0 | 0.0 | 0.9158 | 0.9053 | 0.9467 | 0.0 | 0.0 | 0.0242 | 0.0 | nan | 0.6368 | 0.8195 | 0.0 | 0.5580 | 0.3363 | nan | 0.2812 | 0.0 | 0.0 | 0.7142 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6319 | 0.0 | 0.2284 | 0.0 | 0.0 | nan | 0.0 | 0.0741 | 0.0 | 0.0 | 0.7875 | 0.6937 | 0.8910 | 0.0 | 0.0 | 0.0220 | 0.0 |
159
- | 0.4418 | 1.75 | 700 | 0.7429 | 0.2102 | 0.2584 | 0.7963 | nan | 0.7949 | 0.9470 | 0.0 | 0.6460 | 0.5140 | nan | 0.4080 | 0.0 | 0.0 | 0.9187 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9320 | 0.0 | 0.2289 | 0.0 | 0.0 | nan | 0.0 | 0.0749 | 0.0 | 0.0 | 0.9000 | 0.9236 | 0.9450 | 0.0 | 0.0 | 0.0354 | 0.0 | nan | 0.6329 | 0.8229 | 0.0 | 0.5702 | 0.3704 | nan | 0.3002 | 0.0 | 0.0 | 0.7263 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6290 | 0.0 | 0.1860 | 0.0 | 0.0 | nan | 0.0 | 0.0707 | 0.0 | 0.0 | 0.7934 | 0.6993 | 0.8924 | 0.0 | 0.0 | 0.0311 | 0.0 |
160
- | 1.1377 | 1.8 | 720 | 0.7557 | 0.2069 | 0.2538 | 0.7947 | nan | 0.7764 | 0.9604 | 0.0 | 0.6152 | 0.4478 | nan | 0.3852 | 0.0 | 0.0 | 0.9378 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9359 | 0.0 | 0.1908 | 0.0 | 0.0 | nan | 0.0 | 0.0709 | 0.0 | 0.0 | 0.8934 | 0.9274 | 0.9453 | 0.0 | 0.0 | 0.0354 | 0.0 | nan | 0.6402 | 0.8153 | 0.0 | 0.5580 | 0.3400 | nan | 0.2904 | 0.0 | 0.0 | 0.7131 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6356 | 0.0 | 0.1647 | 0.0 | 0.0 | nan | 0.0 | 0.0675 | 0.0 | 0.0 | 0.7903 | 0.6823 | 0.8930 | 0.0 | 0.0 | 0.0310 | 0.0 |
161
- | 0.6711 | 1.85 | 740 | 0.7428 | 0.2102 | 0.2585 | 0.7962 | nan | 0.8056 | 0.9498 | 0.0 | 0.6294 | 0.4972 | nan | 0.3879 | 0.0 | 0.0 | 0.9314 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9297 | 0.0 | 0.2605 | 0.0 | 0.0 | nan | 0.0 | 0.0755 | 0.0 | 0.0 | 0.8838 | 0.9305 | 0.9521 | 0.0 | 0.0 | 0.0399 | 0.0 | nan | 0.6366 | 0.8208 | 0.0 | 0.5724 | 0.3578 | nan | 0.2960 | 0.0 | 0.0 | 0.7290 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6394 | 0.0 | 0.2086 | 0.0 | 0.0 | nan | 0.0 | 0.0708 | 0.0 | 0.0 | 0.7883 | 0.6796 | 0.8913 | 0.0 | 0.0 | 0.0345 | 0.0 |
162
- | 0.8084 | 1.9 | 760 | 0.7372 | 0.2089 | 0.2568 | 0.7952 | nan | 0.7489 | 0.9522 | 0.0 | 0.6611 | 0.5194 | nan | 0.3998 | 0.0 | 0.0 | 0.9210 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9349 | 0.0 | 0.2080 | 0.0 | 0.0 | nan | 0.0 | 0.0750 | 0.0 | 0.0 | 0.9224 | 0.8935 | 0.9472 | 0.0 | 0.0 | 0.0341 | 0.0 | nan | 0.6278 | 0.8211 | 0.0 | 0.5544 | 0.3528 | nan | 0.3032 | 0.0 | 0.0 | 0.7320 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6346 | 0.0 | 0.1770 | 0.0 | 0.0 | nan | 0.0 | 0.0703 | 0.0 | 0.0 | 0.7895 | 0.6990 | 0.8923 | 0.0 | 0.0 | 0.0302 | 0.0 |
163
- | 0.4363 | 1.95 | 780 | 0.7384 | 0.2087 | 0.2566 | 0.7950 | nan | 0.7507 | 0.9530 | 0.0 | 0.6661 | 0.4853 | nan | 0.4066 | 0.0 | 0.0 | 0.9299 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9426 | 0.0 | 0.2098 | 0.0 | 0.0 | nan | 0.0 | 0.0822 | 0.0 | 0.0 | 0.9113 | 0.9011 | 0.9429 | 0.0 | 0.0 | 0.0301 | 0.0 | nan | 0.6230 | 0.8204 | 0.0 | 0.5522 | 0.3507 | nan | 0.3033 | 0.0 | 0.0 | 0.7166 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6316 | 0.0 | 0.1761 | 0.0 | 0.0 | nan | 0.0 | 0.0764 | 0.0 | 0.0 | 0.7975 | 0.7106 | 0.8946 | 0.0 | 0.0 | 0.0269 | 0.0 |
164
- | 0.8956 | 2.0 | 800 | 0.7351 | 0.2097 | 0.2588 | 0.7962 | nan | 0.7781 | 0.9422 | 0.0 | 0.6678 | 0.5103 | nan | 0.3970 | 0.0 | 0.0 | 0.9361 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9283 | 0.0 | 0.2268 | 0.0000 | 0.0 | nan | 0.0 | 0.0873 | 0.0 | 0.0 | 0.9230 | 0.9084 | 0.9461 | 0.0 | 0.0 | 0.0306 | 0.0 | nan | 0.6262 | 0.8234 | 0.0 | 0.5624 | 0.3620 | nan | 0.3029 | 0.0 | 0.0 | 0.7060 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6436 | 0.0 | 0.1895 | 0.0000 | 0.0 | nan | 0.0 | 0.0808 | 0.0 | 0.0 | 0.7906 | 0.7024 | 0.8933 | 0.0 | 0.0 | 0.0273 | 0.0 |
165
 
166
 
167
  ### Framework versions
168
 
169
- - Transformers 4.46.2
170
  - Pytorch 2.5.1+cu121
171
  - Datasets 3.1.0
172
  - Tokenizers 0.20.3
 
18
 
19
  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
20
  It achieves the following results on the evaluation set:
21
+ - Loss: 1.4669
22
+ - Mean Iou: 0.1972
23
+ - Mean Accuracy: 0.2562
24
+ - Overall Accuracy: 0.7152
25
  - Accuracy Unlabeled: nan
26
+ - Accuracy Flat-road: 0.9357
27
+ - Accuracy Flat-sidewalk: 0.9011
28
  - Accuracy Flat-crosswalk: 0.0
29
+ - Accuracy Flat-cyclinglane: 0.9814
30
+ - Accuracy Flat-parkingdriveway: 0.0
31
  - Accuracy Flat-railtrack: nan
32
+ - Accuracy Flat-curb: 0.3500
33
  - Accuracy Human-person: 0.0
34
  - Accuracy Human-rider: 0.0
35
+ - Accuracy Vehicle-car: 0.8336
36
+ - Accuracy Vehicle-truck: nan
37
  - Accuracy Vehicle-bus: 0.0
38
+ - Accuracy Vehicle-tramtrain: nan
39
  - Accuracy Vehicle-motorcycle: 0.0
40
  - Accuracy Vehicle-bicycle: 0.0
41
+ - Accuracy Vehicle-caravan: nan
42
+ - Accuracy Vehicle-cartrailer: nan
43
+ - Accuracy Construction-building: 0.7899
44
  - Accuracy Construction-door: 0.0
45
+ - Accuracy Construction-wall: 0.2041
46
+ - Accuracy Construction-fenceguardrail: 0.0
47
+ - Accuracy Construction-bridge: nan
48
  - Accuracy Construction-tunnel: nan
49
  - Accuracy Construction-stairs: 0.0
50
+ - Accuracy Object-pole: 0.0
51
  - Accuracy Object-trafficsign: 0.0
52
+ - Accuracy Object-trafficlight: nan
53
+ - Accuracy Nature-vegetation: 0.8356
54
+ - Accuracy Nature-terrain: 0.0
55
+ - Accuracy Sky: 0.5731
56
  - Accuracy Void-ground: 0.0
57
  - Accuracy Void-dynamic: 0.0
58
+ - Accuracy Void-static: 0.0
59
+ - Accuracy Void-unclear: nan
60
  - Iou Unlabeled: nan
61
+ - Iou Flat-road: 0.6487
62
+ - Iou Flat-sidewalk: 0.6719
63
  - Iou Flat-crosswalk: 0.0
64
+ - Iou Flat-cyclinglane: 0.9208
65
+ - Iou Flat-parkingdriveway: 0.0
66
  - Iou Flat-railtrack: nan
67
+ - Iou Flat-curb: 0.2442
68
  - Iou Human-person: 0.0
69
  - Iou Human-rider: 0.0
70
+ - Iou Vehicle-car: 0.5671
71
+ - Iou Vehicle-truck: nan
72
  - Iou Vehicle-bus: 0.0
73
+ - Iou Vehicle-tramtrain: nan
74
  - Iou Vehicle-motorcycle: 0.0
75
  - Iou Vehicle-bicycle: 0.0
76
+ - Iou Vehicle-caravan: nan
77
+ - Iou Vehicle-cartrailer: nan
78
+ - Iou Construction-building: 0.5253
79
  - Iou Construction-door: 0.0
80
+ - Iou Construction-wall: 0.1883
81
+ - Iou Construction-fenceguardrail: 0.0
82
+ - Iou Construction-bridge: nan
83
  - Iou Construction-tunnel: nan
84
  - Iou Construction-stairs: 0.0
85
+ - Iou Object-pole: 0.0
86
  - Iou Object-trafficsign: 0.0
87
+ - Iou Object-trafficlight: nan
88
+ - Iou Nature-vegetation: 0.5974
89
+ - Iou Nature-terrain: 0.0
90
+ - Iou Sky: 0.5671
91
  - Iou Void-ground: 0.0
92
  - Iou Void-dynamic: 0.0
93
+ - Iou Void-static: 0.0
94
+ - Iou Void-unclear: nan
95
 
96
  ## Model description
97
 
 
110
  ### Training hyperparameters
111
 
112
  The following hyperparameters were used during training:
113
+ - learning_rate: 6e-05
114
+ - train_batch_size: 6
115
+ - eval_batch_size: 6
116
  - seed: 42
117
  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
118
  - lr_scheduler_type: linear
119
+ - num_epochs: 15
120
 
121
  ### Training results
122
 
123
+ | 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 |
124
+ |:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:|
125
+ | 1.6415 | 2.8571 | 20 | 1.6084 | 0.1694 | 0.2310 | 0.6914 | nan | 0.9531 | 0.8595 | 0.0 | 0.9741 | 0.0001 | nan | 0.2029 | 0.0 | 0.0 | 0.8208 | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.7820 | 0.0 | 0.0056 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.8447 | 0.0 | 0.3309 | 0.0 | 0.0 | 0.0 | nan | nan | 0.5908 | 0.6652 | 0.0 | 0.8715 | 0.0001 | nan | 0.1537 | 0.0 | 0.0 | 0.5398 | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.4867 | 0.0 | 0.0054 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.5918 | 0.0 | 0.3307 | 0.0 | 0.0 | 0.0 | nan |
126
+ | 1.2815 | 5.7143 | 40 | 1.5589 | 0.1752 | 0.2359 | 0.6972 | nan | 0.9423 | 0.8679 | 0.0 | 0.9794 | 0.0 | nan | 0.2620 | 0.0 | 0.0 | 0.8132 | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.7779 | 0.0 | 0.0410 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.8569 | 0.0 | 0.3579 | 0.0 | 0.0 | 0.0 | nan | nan | 0.6130 | 0.6656 | 0.0 | 0.8866 | 0.0 | nan | 0.1805 | 0.0 | 0.0 | 0.5453 | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.4985 | 0.0 | 0.0381 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.5938 | 0.0 | 0.3578 | 0.0 | 0.0 | 0.0 | nan |
127
+ | 1.2436 | 8.5714 | 60 | 1.4972 | 0.1884 | 0.2481 | 0.7089 | nan | 0.9335 | 0.8880 | 0.0 | 0.9926 | 0.0 | nan | 0.3274 | 0.0 | 0.0 | 0.8123 | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.7794 | 0.0 | 0.0690 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.8518 | 0.0 | 0.5475 | 0.0 | 0.0 | 0.0 | nan | nan | 0.6532 | 0.6654 | 0.0 | 0.8826 | 0.0 | nan | 0.2386 | 0.0 | 0.0 | 0.5566 | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.5082 | 0.0 | 0.0653 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.5957 | 0.0 | 0.5442 | 0.0 | 0.0 | 0.0 | nan |
128
+ | 1.2207 | 11.4286 | 80 | 1.4880 | 0.1931 | 0.2516 | 0.7137 | nan | 0.9329 | 0.9003 | 0.0 | 0.9711 | 0.0 | nan | 0.3744 | 0.0 | 0.0 | 0.8177 | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.8023 | 0.0 | 0.1182 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.8337 | 0.0 | 0.5407 | 0.0 | 0.0 | 0.0 | nan | nan | 0.6559 | 0.6672 | 0.0 | 0.9216 | 0.0 | nan | 0.2617 | 0.0 | 0.0 | 0.5584 | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.5209 | 0.0 | 0.1091 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.5960 | 0.0 | 0.5375 | 0.0 | 0.0 | 0.0 | nan |
129
+ | 1.2494 | 14.2857 | 100 | 1.4669 | 0.1972 | 0.2562 | 0.7152 | nan | 0.9357 | 0.9011 | 0.0 | 0.9814 | 0.0 | nan | 0.3500 | 0.0 | 0.0 | 0.8336 | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.7899 | 0.0 | 0.2041 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.8356 | 0.0 | 0.5731 | 0.0 | 0.0 | 0.0 | nan | nan | 0.6487 | 0.6719 | 0.0 | 0.9208 | 0.0 | nan | 0.2442 | 0.0 | 0.0 | 0.5671 | nan | 0.0 | nan | 0.0 | 0.0 | nan | nan | 0.5253 | 0.0 | 0.1883 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | nan | 0.5974 | 0.0 | 0.5671 | 0.0 | 0.0 | 0.0 | nan |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
130
 
131
 
132
  ### Framework versions
133
 
134
+ - Transformers 4.46.3
135
  - Pytorch 2.5.1+cu121
136
  - Datasets 3.1.0
137
  - Tokenizers 0.20.3
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