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segformer-b3

Image Segmentation
Transformers
PyTorch
segformer
vision
Generated from Trainer
Inference Endpoints
Model card Files Community
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  1. README.md +151 -0
  2. config.json +144 -0
  3. pytorch_model.bin +3 -0
  4. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: other
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+ base_model: nvidia/mit-b3
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+ tags:
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+ - vision
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+ - image-segmentation
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+ - generated_from_trainer
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+ model-index:
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+ - name: segformer-b3
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # segformer-b3
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+
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+ This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the segments/sidewalk-semantic dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7826
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+ - Mean Iou: 0.3995
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+ - Mean Accuracy: 0.4977
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+ - Overall Accuracy: 0.8759
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Flat-road: 0.9069
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+ - Accuracy Flat-sidewalk: 0.9471
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+ - Accuracy Flat-crosswalk: 0.5043
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+ - Accuracy Flat-cyclinglane: 0.8684
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+ - Accuracy Flat-parkingdriveway: 0.5057
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+ - Accuracy Flat-railtrack: 0.0
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+ - Accuracy Flat-curb: 0.7351
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+ - Accuracy Human-person: 0.8662
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+ - Accuracy Human-rider: 0.2599
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+ - Accuracy Vehicle-car: 0.9494
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+ - Accuracy Vehicle-truck: 0.1607
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+ - Accuracy Vehicle-bus: 0.0044
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+ - Accuracy Vehicle-tramtrain: 0.1992
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+ - Accuracy Vehicle-motorcycle: 0.0
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+ - Accuracy Vehicle-bicycle: 0.7913
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+ - Accuracy Vehicle-caravan: 0.4628
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+ - Accuracy Vehicle-cartrailer: 0.0106
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+ - Accuracy Construction-building: 0.9117
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+ - Accuracy Construction-door: 0.2679
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+ - Accuracy Construction-wall: 0.6351
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+ - Accuracy Construction-fenceguardrail: 0.5893
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+ - Accuracy Construction-bridge: 0.5639
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+ - Accuracy Construction-tunnel: nan
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+ - Accuracy Construction-stairs: 0.4246
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+ - Accuracy Object-pole: 0.6323
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+ - Accuracy Object-trafficsign: 0.4266
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+ - Accuracy Object-trafficlight: 0.2431
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+ - Accuracy Nature-vegetation: 0.9540
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+ - Accuracy Nature-terrain: 0.8819
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+ - Accuracy Sky: 0.9827
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+ - Accuracy Void-ground: 0.0045
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+ - Accuracy Void-dynamic: 0.2006
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+ - Accuracy Void-static: 0.5328
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+ - Accuracy Void-unclear: 0.0
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+ - Iou Unlabeled: 0.0
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+ - Iou Flat-road: 0.7947
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+ - Iou Flat-sidewalk: 0.8656
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+ - Iou Flat-crosswalk: 0.4529
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+ - Iou Flat-cyclinglane: 0.6876
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+ - Iou Flat-parkingdriveway: 0.4461
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+ - Iou Flat-railtrack: 0.0
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+ - Iou Flat-curb: 0.5989
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+ - Iou Human-person: 0.6127
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+ - Iou Human-rider: 0.2346
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+ - Iou Vehicle-car: 0.8877
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+ - Iou Vehicle-truck: 0.0662
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+ - Iou Vehicle-bus: 0.0044
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+ - Iou Vehicle-tramtrain: 0.1985
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+ - Iou Vehicle-motorcycle: 0.0
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+ - Iou Vehicle-bicycle: 0.5765
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+ - Iou Vehicle-caravan: 0.1495
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+ - Iou Vehicle-cartrailer: 0.0106
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+ - Iou Construction-building: 0.8060
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+ - Iou Construction-door: 0.2190
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+ - Iou Construction-wall: 0.5015
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+ - Iou Construction-fenceguardrail: 0.4923
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+ - Iou Construction-bridge: 0.3467
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+ - Iou Construction-tunnel: nan
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+ - Iou Construction-stairs: 0.3908
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+ - Iou Object-pole: 0.4693
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+ - Iou Object-trafficsign: 0.3698
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+ - Iou Object-trafficlight: 0.2052
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+ - Iou Nature-vegetation: 0.8832
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+ - Iou Nature-terrain: 0.7906
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+ - Iou Sky: 0.9519
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+ - Iou Void-ground: 0.0038
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+ - Iou Void-dynamic: 0.1774
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+ - Iou Void-static: 0.3885
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+ - Iou Void-unclear: 0.0
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 6e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 50
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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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+ | 0.7275 | 2.5 | 500 | 0.5765 | 0.3050 | 0.3654 | 0.8441 | nan | 0.9561 | 0.9153 | 0.3719 | 0.7164 | 0.4360 | 0.0 | 0.3475 | 0.8270 | 0.0 | 0.9318 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6843 | 0.0 | 0.0 | 0.9160 | 0.0667 | 0.3893 | 0.6512 | 0.0 | nan | 0.0 | 0.5447 | 0.0525 | 0.0 | 0.9581 | 0.8185 | 0.9737 | 0.0 | 0.0262 | 0.4752 | 0.0 | nan | 0.7208 | 0.8407 | 0.3582 | 0.6393 | 0.3693 | 0.0 | 0.2705 | 0.5291 | 0.0 | 0.8548 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5158 | 0.0 | 0.0 | 0.7684 | 0.0638 | 0.3606 | 0.4620 | 0.0 | nan | 0.0 | 0.3805 | 0.0522 | 0.0 | 0.8572 | 0.7657 | 0.9225 | 0.0 | 0.0256 | 0.3078 | 0.0 |
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+ | 0.3654 | 5.0 | 1000 | 0.5265 | 0.3531 | 0.4276 | 0.8622 | nan | 0.9116 | 0.9476 | 0.4986 | 0.8194 | 0.4632 | 0.0 | 0.5613 | 0.8672 | 0.1407 | 0.9399 | 0.2129 | 0.0 | 0.0 | 0.0 | 0.7640 | 0.0 | 0.0 | 0.8915 | 0.1468 | 0.5813 | 0.5719 | 0.0 | nan | 0.3336 | 0.5583 | 0.4068 | 0.0 | 0.9470 | 0.8512 | 0.9780 | 0.0004 | 0.1935 | 0.5228 | 0.0 | nan | 0.7904 | 0.8491 | 0.4455 | 0.6935 | 0.4013 | 0.0 | 0.4607 | 0.5435 | 0.1227 | 0.8663 | 0.1019 | 0.0 | 0.0 | 0.0 | 0.5464 | 0.0 | 0.0 | 0.7740 | 0.1198 | 0.4746 | 0.4587 | 0.0 | nan | 0.2403 | 0.4036 | 0.2949 | 0.0 | 0.8663 | 0.7614 | 0.9334 | 0.0003 | 0.1492 | 0.3544 | 0.0 |
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+ | 0.2359 | 7.5 | 1500 | 0.5790 | 0.3584 | 0.4296 | 0.8649 | nan | 0.8646 | 0.9501 | 0.4466 | 0.8506 | 0.5513 | 0.0 | 0.7099 | 0.8317 | 0.2099 | 0.9442 | 0.2546 | 0.0 | 0.0 | 0.0 | 0.7862 | 0.0087 | 0.0 | 0.9079 | 0.1046 | 0.6479 | 0.5239 | 0.0 | nan | 0.1543 | 0.5674 | 0.3864 | 0.0 | 0.9461 | 0.8753 | 0.9776 | 0.0000 | 0.1974 | 0.4793 | 0.0 | nan | 0.7917 | 0.8414 | 0.4214 | 0.7012 | 0.4494 | 0.0 | 0.5333 | 0.5888 | 0.1897 | 0.8663 | 0.1009 | 0.0 | 0.0 | 0.0 | 0.5427 | 0.0042 | 0.0 | 0.7817 | 0.0945 | 0.4696 | 0.4312 | 0.0 | nan | 0.1477 | 0.4314 | 0.3152 | 0.0 | 0.8749 | 0.7719 | 0.9409 | 0.0000 | 0.1780 | 0.3593 | 0.0 |
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+ | 0.1708 | 10.0 | 2000 | 0.6066 | 0.3684 | 0.4479 | 0.8666 | nan | 0.8819 | 0.9466 | 0.5609 | 0.8324 | 0.4835 | 0.0 | 0.7200 | 0.8575 | 0.1404 | 0.9422 | 0.2656 | 0.0 | 0.0590 | 0.0 | 0.7505 | 0.2619 | 0.0 | 0.8906 | 0.2203 | 0.6425 | 0.5323 | 0.0 | nan | 0.3455 | 0.5923 | 0.4085 | 0.0 | 0.9552 | 0.8844 | 0.9791 | 0.0024 | 0.0951 | 0.5293 | 0.0 | nan | 0.7930 | 0.8537 | 0.4562 | 0.6519 | 0.4311 | 0.0 | 0.5478 | 0.5960 | 0.1301 | 0.8728 | 0.1051 | 0.0 | 0.0590 | 0.0 | 0.5390 | 0.0916 | 0.0 | 0.7864 | 0.1779 | 0.4949 | 0.4467 | 0.0 | nan | 0.3107 | 0.4336 | 0.3289 | 0.0 | 0.8757 | 0.7694 | 0.9416 | 0.0019 | 0.0893 | 0.3741 | 0.0 |
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+ | 0.1326 | 12.5 | 2500 | 0.5934 | 0.3969 | 0.4877 | 0.8753 | nan | 0.9227 | 0.9490 | 0.4762 | 0.8499 | 0.5255 | 0.0 | 0.6941 | 0.8115 | 0.3960 | 0.9430 | 0.3828 | 0.0266 | 0.0998 | 0.0 | 0.7963 | 0.6565 | 0.0003 | 0.8988 | 0.2880 | 0.6352 | 0.5442 | 0.2746 | nan | 0.3048 | 0.6133 | 0.4269 | 0.0 | 0.9483 | 0.9064 | 0.9838 | 0.0025 | 0.2297 | 0.5070 | 0.0 | nan | 0.8032 | 0.8631 | 0.4521 | 0.7678 | 0.4440 | 0.0 | 0.5593 | 0.6096 | 0.2994 | 0.8777 | 0.1308 | 0.0266 | 0.0998 | 0.0 | 0.5663 | 0.2016 | 0.0003 | 0.7931 | 0.1957 | 0.4838 | 0.4475 | 0.2094 | nan | 0.2849 | 0.4530 | 0.3424 | 0.0 | 0.8798 | 0.7777 | 0.9441 | 0.0023 | 0.1979 | 0.3857 | 0.0 |
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+ | 0.1116 | 15.0 | 3000 | 0.6267 | 0.3978 | 0.4820 | 0.8734 | nan | 0.9155 | 0.9431 | 0.5145 | 0.8423 | 0.4973 | 0.0 | 0.7284 | 0.8513 | 0.3146 | 0.9492 | 0.0753 | 0.0 | 0.2565 | 0.0 | 0.7845 | 0.3853 | 0.0220 | 0.8998 | 0.2497 | 0.6306 | 0.5571 | 0.3186 | nan | 0.4912 | 0.5902 | 0.4795 | 0.0 | 0.9501 | 0.9004 | 0.9843 | 0.0038 | 0.2017 | 0.5677 | 0.0 | nan | 0.8037 | 0.8591 | 0.4665 | 0.7147 | 0.4311 | 0.0 | 0.5698 | 0.5996 | 0.2693 | 0.8800 | 0.0366 | 0.0 | 0.2554 | 0.0 | 0.5526 | 0.1200 | 0.0216 | 0.7935 | 0.1949 | 0.4853 | 0.4704 | 0.2116 | nan | 0.3980 | 0.4450 | 0.3691 | 0.0 | 0.8815 | 0.7857 | 0.9439 | 0.0031 | 0.1751 | 0.3899 | 0.0 |
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+ | 0.098 | 17.5 | 3500 | 0.6334 | 0.3922 | 0.5006 | 0.8729 | nan | 0.8961 | 0.9419 | 0.5747 | 0.8862 | 0.4977 | 0.0 | 0.7428 | 0.8491 | 0.3477 | 0.9464 | 0.0952 | 0.0 | 0.2937 | 0.0 | 0.7908 | 0.7738 | 0.0 | 0.8934 | 0.2479 | 0.6445 | 0.6108 | 0.4273 | nan | 0.4435 | 0.6190 | 0.4308 | 0.0015 | 0.9486 | 0.9026 | 0.9818 | 0.0099 | 0.2216 | 0.4994 | 0.0 | 0.0 | 0.7961 | 0.8651 | 0.5005 | 0.6765 | 0.4413 | 0.0 | 0.5751 | 0.6176 | 0.2944 | 0.8811 | 0.0373 | 0.0 | 0.2919 | 0.0 | 0.5578 | 0.2307 | 0.0 | 0.7961 | 0.1835 | 0.4901 | 0.4814 | 0.2506 | nan | 0.3771 | 0.4560 | 0.3562 | 0.0015 | 0.8810 | 0.7806 | 0.9474 | 0.0077 | 0.1823 | 0.3784 | 0.0 |
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+ | 0.0894 | 20.0 | 4000 | 0.6973 | 0.3988 | 0.4923 | 0.8722 | nan | 0.8952 | 0.9456 | 0.5309 | 0.8357 | 0.4777 | 0.0 | 0.7630 | 0.8291 | 0.2785 | 0.9467 | 0.1712 | 0.0047 | 0.1377 | 0.0 | 0.7854 | 0.8237 | 0.0 | 0.9282 | 0.1899 | 0.5904 | 0.6020 | 0.4761 | nan | 0.3323 | 0.6192 | 0.4047 | 0.1381 | 0.9522 | 0.8851 | 0.9767 | 0.0050 | 0.1916 | 0.5277 | 0.0 | nan | 0.8062 | 0.8543 | 0.4692 | 0.6675 | 0.4204 | 0.0 | 0.5710 | 0.6083 | 0.2366 | 0.8855 | 0.0677 | 0.0046 | 0.1374 | 0.0 | 0.5492 | 0.2453 | 0.0 | 0.7996 | 0.1571 | 0.4887 | 0.4847 | 0.2656 | nan | 0.3148 | 0.4670 | 0.3491 | 0.1264 | 0.8815 | 0.7829 | 0.9496 | 0.0043 | 0.1718 | 0.3936 | 0.0 |
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+ | 0.0819 | 22.5 | 4500 | 0.6867 | 0.4098 | 0.5001 | 0.8778 | nan | 0.9344 | 0.9410 | 0.5690 | 0.8783 | 0.4856 | 0.0 | 0.7065 | 0.8495 | 0.2085 | 0.9415 | 0.1530 | 0.0018 | 0.2354 | 0.0 | 0.7829 | 0.7796 | 0.0 | 0.9044 | 0.2261 | 0.6171 | 0.6045 | 0.4780 | nan | 0.4156 | 0.6265 | 0.4288 | 0.1457 | 0.9563 | 0.8877 | 0.9804 | 0.0064 | 0.2136 | 0.5447 | 0.0 | nan | 0.8016 | 0.8702 | 0.4902 | 0.7597 | 0.4279 | 0.0 | 0.5780 | 0.6123 | 0.1998 | 0.8889 | 0.0577 | 0.0018 | 0.2348 | 0.0 | 0.5898 | 0.2436 | 0.0 | 0.7992 | 0.1842 | 0.4829 | 0.4918 | 0.2855 | nan | 0.3732 | 0.4658 | 0.3650 | 0.1297 | 0.8823 | 0.7837 | 0.9500 | 0.0053 | 0.1841 | 0.3839 | 0.0 |
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+ | 0.0767 | 25.0 | 5000 | 0.7377 | 0.4096 | 0.5109 | 0.8720 | nan | 0.8599 | 0.9464 | 0.5724 | 0.9354 | 0.4838 | 0.0 | 0.7392 | 0.8475 | 0.2679 | 0.9530 | 0.2438 | 0.0 | 0.2405 | 0.0 | 0.7879 | 0.8364 | 0.0 | 0.9155 | 0.2107 | 0.5924 | 0.5901 | 0.5525 | nan | 0.3980 | 0.6229 | 0.4648 | 0.2165 | 0.9550 | 0.8865 | 0.9823 | 0.0047 | 0.1970 | 0.5557 | 0.0 | nan | 0.7881 | 0.8643 | 0.5042 | 0.6317 | 0.4280 | 0.0 | 0.5817 | 0.6075 | 0.2397 | 0.8857 | 0.1052 | 0.0 | 0.2384 | 0.0 | 0.5664 | 0.2501 | 0.0 | 0.8056 | 0.1813 | 0.4878 | 0.4863 | 0.2871 | nan | 0.3652 | 0.4725 | 0.3883 | 0.1660 | 0.8804 | 0.7905 | 0.9503 | 0.0040 | 0.1704 | 0.3891 | 0.0 |
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+ | 0.0725 | 27.5 | 5500 | 0.7085 | 0.3977 | 0.5056 | 0.8782 | nan | 0.9177 | 0.9482 | 0.4916 | 0.8966 | 0.4989 | 0.0 | 0.7119 | 0.8469 | 0.2483 | 0.9512 | 0.2387 | 0.0440 | 0.1287 | 0.0 | 0.7947 | 0.8184 | 0.0 | 0.9152 | 0.2257 | 0.6472 | 0.5963 | 0.5426 | nan | 0.3951 | 0.6422 | 0.4369 | 0.2195 | 0.9499 | 0.8824 | 0.9821 | 0.0036 | 0.1824 | 0.5266 | 0.0 | 0.0 | 0.8109 | 0.8638 | 0.4498 | 0.7314 | 0.4437 | 0.0 | 0.5797 | 0.6047 | 0.2215 | 0.8861 | 0.0855 | 0.0430 | 0.1284 | 0.0 | 0.5657 | 0.2395 | 0.0 | 0.8058 | 0.1939 | 0.5113 | 0.4913 | 0.2943 | nan | 0.3732 | 0.4773 | 0.3770 | 0.1643 | 0.8836 | 0.7864 | 0.9509 | 0.0029 | 0.1639 | 0.3905 | 0.0 |
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+ | 0.0685 | 30.0 | 6000 | 0.7388 | 0.4115 | 0.5051 | 0.8738 | nan | 0.9135 | 0.9420 | 0.5290 | 0.8405 | 0.4909 | 0.0 | 0.7408 | 0.8566 | 0.3161 | 0.9461 | 0.1138 | 0.0003 | 0.1616 | 0.0 | 0.8061 | 0.7486 | 0.0001 | 0.9074 | 0.2986 | 0.6418 | 0.5669 | 0.4769 | nan | 0.4607 | 0.6454 | 0.4717 | 0.2320 | 0.9531 | 0.8849 | 0.9802 | 0.0037 | 0.1983 | 0.5417 | 0.0 | nan | 0.7911 | 0.8647 | 0.4671 | 0.6651 | 0.4361 | 0.0 | 0.5848 | 0.6127 | 0.2642 | 0.8885 | 0.0453 | 0.0003 | 0.1613 | 0.0 | 0.5455 | 0.2421 | 0.0001 | 0.8022 | 0.2382 | 0.4975 | 0.4741 | 0.3279 | nan | 0.4050 | 0.4789 | 0.3937 | 0.1921 | 0.8825 | 0.7873 | 0.9516 | 0.0032 | 0.1809 | 0.3970 | 0.0 |
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+ | 0.0654 | 32.5 | 6500 | 0.7246 | 0.4128 | 0.5034 | 0.8789 | nan | 0.9247 | 0.9424 | 0.5865 | 0.8579 | 0.5105 | 0.0 | 0.7409 | 0.8799 | 0.2449 | 0.9462 | 0.0922 | 0.0 | 0.1728 | 0.0 | 0.7762 | 0.7085 | 0.0 | 0.9151 | 0.2459 | 0.6278 | 0.6088 | 0.5426 | nan | 0.4260 | 0.6444 | 0.4471 | 0.2230 | 0.9530 | 0.8839 | 0.9833 | 0.0040 | 0.1978 | 0.5251 | 0.0 | nan | 0.8010 | 0.8705 | 0.5132 | 0.7193 | 0.4466 | 0.0 | 0.5906 | 0.5971 | 0.2204 | 0.8884 | 0.0419 | 0.0 | 0.1724 | 0.0 | 0.5623 | 0.2184 | 0.0 | 0.8044 | 0.2015 | 0.5037 | 0.4964 | 0.3206 | nan | 0.4032 | 0.4828 | 0.3859 | 0.1802 | 0.8828 | 0.7909 | 0.9505 | 0.0033 | 0.1792 | 0.3959 | 0.0 |
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+ | 0.0629 | 35.0 | 7000 | 0.7655 | 0.4168 | 0.5105 | 0.8741 | nan | 0.8961 | 0.9470 | 0.5214 | 0.8906 | 0.4982 | 0.0 | 0.7542 | 0.8631 | 0.2754 | 0.9512 | 0.1882 | 0.0015 | 0.3457 | 0.0 | 0.7778 | 0.6418 | 0.0144 | 0.8908 | 0.2816 | 0.6612 | 0.5910 | 0.5330 | nan | 0.4434 | 0.6305 | 0.4273 | 0.2421 | 0.9516 | 0.8805 | 0.9821 | 0.0036 | 0.2172 | 0.5444 | 0.0 | nan | 0.7981 | 0.8672 | 0.4665 | 0.6765 | 0.4364 | 0.0 | 0.5934 | 0.6114 | 0.2489 | 0.8877 | 0.0831 | 0.0013 | 0.3436 | 0.0 | 0.5668 | 0.2017 | 0.0140 | 0.7928 | 0.2283 | 0.4708 | 0.4904 | 0.3458 | nan | 0.4011 | 0.4722 | 0.3699 | 0.1843 | 0.8836 | 0.7898 | 0.9516 | 0.0030 | 0.1851 | 0.3897 | 0.0 |
138
+ | 0.0607 | 37.5 | 7500 | 0.7668 | 0.4180 | 0.5139 | 0.8751 | nan | 0.8948 | 0.9480 | 0.5612 | 0.8579 | 0.4903 | 0.0 | 0.7432 | 0.8676 | 0.2619 | 0.9495 | 0.1718 | 0.0165 | 0.3359 | 0.0010 | 0.7738 | 0.7077 | 0.0304 | 0.9104 | 0.2826 | 0.6353 | 0.6045 | 0.5609 | nan | 0.4406 | 0.6293 | 0.4355 | 0.2376 | 0.9511 | 0.8940 | 0.9818 | 0.0033 | 0.2277 | 0.5530 | 0.0 | nan | 0.7933 | 0.8676 | 0.4914 | 0.6562 | 0.4327 | 0.0 | 0.5956 | 0.6059 | 0.2348 | 0.8875 | 0.0739 | 0.0161 | 0.3343 | 0.0010 | 0.5696 | 0.2086 | 0.0295 | 0.8084 | 0.2268 | 0.5014 | 0.4962 | 0.3297 | nan | 0.3948 | 0.4702 | 0.3754 | 0.1918 | 0.8836 | 0.7857 | 0.9519 | 0.0029 | 0.1886 | 0.3900 | 0.0 |
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+ | 0.0582 | 40.0 | 8000 | 0.7562 | 0.4049 | 0.5074 | 0.8780 | nan | 0.9204 | 0.9463 | 0.5052 | 0.8643 | 0.5082 | 0.0 | 0.7360 | 0.8650 | 0.2462 | 0.9501 | 0.1893 | 0.0024 | 0.2403 | 0.0 | 0.7814 | 0.6631 | 0.0242 | 0.9169 | 0.2821 | 0.6356 | 0.5984 | 0.5609 | nan | 0.4218 | 0.6299 | 0.4414 | 0.2421 | 0.9504 | 0.8835 | 0.9796 | 0.0043 | 0.2138 | 0.5395 | 0.0 | 0.0 | 0.8004 | 0.8683 | 0.4553 | 0.7073 | 0.4478 | 0.0 | 0.6007 | 0.6132 | 0.2291 | 0.8882 | 0.0839 | 0.0024 | 0.2391 | 0.0 | 0.5777 | 0.2020 | 0.0237 | 0.8060 | 0.2276 | 0.5058 | 0.4990 | 0.3415 | nan | 0.3905 | 0.4699 | 0.3815 | 0.1952 | 0.8837 | 0.7908 | 0.9522 | 0.0036 | 0.1849 | 0.3941 | 0.0 |
140
+ | 0.0565 | 42.5 | 8500 | 0.7834 | 0.4004 | 0.5024 | 0.8762 | nan | 0.8960 | 0.9474 | 0.5430 | 0.8894 | 0.4937 | 0.0 | 0.7492 | 0.8696 | 0.2727 | 0.9482 | 0.1505 | 0.0006 | 0.1868 | 0.0 | 0.7945 | 0.5042 | 0.0051 | 0.9155 | 0.2834 | 0.6369 | 0.5958 | 0.5811 | nan | 0.4136 | 0.6419 | 0.4457 | 0.2481 | 0.9510 | 0.8887 | 0.9822 | 0.0041 | 0.2054 | 0.5358 | 0.0 | 0.0 | 0.7955 | 0.8676 | 0.4613 | 0.6877 | 0.4390 | 0.0 | 0.6023 | 0.6092 | 0.2503 | 0.8879 | 0.0626 | 0.0006 | 0.1860 | 0.0 | 0.5802 | 0.1628 | 0.0051 | 0.8061 | 0.2307 | 0.5003 | 0.4961 | 0.3290 | nan | 0.3820 | 0.4752 | 0.3819 | 0.2070 | 0.8838 | 0.7914 | 0.9520 | 0.0034 | 0.1813 | 0.3944 | 0.0 |
141
+ | 0.0562 | 45.0 | 9000 | 0.7812 | 0.4015 | 0.5008 | 0.8772 | nan | 0.9064 | 0.9466 | 0.5058 | 0.8872 | 0.5059 | 0.0 | 0.7482 | 0.8642 | 0.2957 | 0.9489 | 0.1494 | 0.0059 | 0.1674 | 0.0003 | 0.8079 | 0.4484 | 0.0122 | 0.9134 | 0.2785 | 0.6303 | 0.6007 | 0.5723 | nan | 0.4337 | 0.6286 | 0.4291 | 0.2541 | 0.9521 | 0.8902 | 0.9821 | 0.0054 | 0.2097 | 0.5444 | 0.0 | 0.0 | 0.7954 | 0.8685 | 0.4597 | 0.7046 | 0.4471 | 0.0 | 0.6024 | 0.6174 | 0.2611 | 0.8881 | 0.0630 | 0.0057 | 0.1667 | 0.0003 | 0.5844 | 0.1441 | 0.0120 | 0.8059 | 0.2279 | 0.5021 | 0.4963 | 0.3439 | nan | 0.3981 | 0.4694 | 0.3714 | 0.2134 | 0.8841 | 0.7883 | 0.9522 | 0.0044 | 0.1836 | 0.3913 | 0.0 |
142
+ | 0.0547 | 47.5 | 9500 | 0.7899 | 0.3997 | 0.4971 | 0.8759 | nan | 0.9053 | 0.9472 | 0.4999 | 0.8752 | 0.5002 | 0.0 | 0.7334 | 0.8557 | 0.2947 | 0.9505 | 0.1326 | 0.0 | 0.1843 | 0.0 | 0.8065 | 0.3995 | 0.0184 | 0.9146 | 0.2650 | 0.6301 | 0.6056 | 0.5749 | nan | 0.4294 | 0.6299 | 0.4450 | 0.2461 | 0.9515 | 0.8854 | 0.9825 | 0.0044 | 0.2045 | 0.5311 | 0.0000 | 0.0 | 0.7939 | 0.8655 | 0.4530 | 0.6865 | 0.4427 | 0.0 | 0.5983 | 0.6206 | 0.2592 | 0.8881 | 0.0597 | 0.0 | 0.1837 | 0.0 | 0.5769 | 0.1272 | 0.0183 | 0.8055 | 0.2174 | 0.5004 | 0.4960 | 0.3457 | nan | 0.3926 | 0.4724 | 0.3800 | 0.2072 | 0.8841 | 0.7912 | 0.9522 | 0.0037 | 0.1789 | 0.3895 | 0.0000 |
143
+ | 0.0543 | 50.0 | 10000 | 0.7826 | 0.3995 | 0.4977 | 0.8759 | nan | 0.9069 | 0.9471 | 0.5043 | 0.8684 | 0.5057 | 0.0 | 0.7351 | 0.8662 | 0.2599 | 0.9494 | 0.1607 | 0.0044 | 0.1992 | 0.0 | 0.7913 | 0.4628 | 0.0106 | 0.9117 | 0.2679 | 0.6351 | 0.5893 | 0.5639 | nan | 0.4246 | 0.6323 | 0.4266 | 0.2431 | 0.9540 | 0.8819 | 0.9827 | 0.0045 | 0.2006 | 0.5328 | 0.0 | 0.0 | 0.7947 | 0.8656 | 0.4529 | 0.6876 | 0.4461 | 0.0 | 0.5989 | 0.6127 | 0.2346 | 0.8877 | 0.0662 | 0.0044 | 0.1985 | 0.0 | 0.5765 | 0.1495 | 0.0106 | 0.8060 | 0.2190 | 0.5015 | 0.4923 | 0.3467 | nan | 0.3908 | 0.4693 | 0.3698 | 0.2052 | 0.8832 | 0.7906 | 0.9519 | 0.0038 | 0.1774 | 0.3885 | 0.0 |
144
+
145
+
146
+ ### Framework versions
147
+
148
+ - Transformers 4.35.0.dev0
149
+ - Pytorch 2.0.0
150
+ - Datasets 2.14.5
151
+ - Tokenizers 0.14.1
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+ "architectures": [
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+ "id2label": {
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+ "0": "unlabeled",
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+ "1": "flat-road",
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+ "2": "flat-sidewalk",
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+ "3": "flat-crosswalk",
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+ "5": "flat-parkingdriveway",
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+ "6": "flat-railtrack",
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+ "7": "flat-curb",
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+ "13": "vehicle-tramtrain",
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+ "15": "vehicle-bicycle",
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+ "17": "vehicle-cartrailer",
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+ "26": "object-trafficsign",
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+ "28": "nature-vegetation",
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+ "29": "nature-terrain",
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+ "30": "sky",
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+ "31": "void-ground",
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+ "32": "void-dynamic",
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+ "33": "void-static",
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+ "34": "void-unclear"
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+ "sky": 30,
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+ "unlabeled": 0,
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+ "vehicle-bicycle": 15,
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+ "vehicle-bus": 12,
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+ "vehicle-car": 10,
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+ "vehicle-caravan": 16,
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+ "vehicle-cartrailer": 17,
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+ "vehicle-motorcycle": 14,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.35.0.dev0"
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