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segformer-trainer-test-bis

This model is a fine-tuned version of nvidia/mit-b0 on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set:

  • Loss: 1.3784
  • Mean Iou: 0.1424
  • Mean Accuracy: 0.1896
  • Overall Accuracy: 0.7288
  • Accuracy Unlabeled: nan
  • Accuracy Flat-road: 0.6651
  • Accuracy Flat-sidewalk: 0.9129
  • Accuracy Flat-crosswalk: 0.0
  • Accuracy Flat-cyclinglane: 0.5829
  • Accuracy Flat-parkingdriveway: 0.0184
  • Accuracy Flat-railtrack: 0.0
  • Accuracy Flat-curb: 0.0
  • Accuracy Human-person: 0.0
  • Accuracy Human-rider: 0.0
  • Accuracy Vehicle-car: 0.8322
  • Accuracy Vehicle-truck: 0.0
  • Accuracy Vehicle-bus: 0.0
  • Accuracy Vehicle-tramtrain: 0.0
  • Accuracy Vehicle-motorcycle: 0.0
  • Accuracy Vehicle-bicycle: 0.0
  • Accuracy Vehicle-caravan: 0.0
  • Accuracy Vehicle-cartrailer: 0.0
  • Accuracy Construction-building: 0.8930
  • Accuracy Construction-door: 0.0
  • Accuracy Construction-wall: 0.0025
  • Accuracy Construction-fenceguardrail: 0.0
  • Accuracy Construction-bridge: 0.0
  • Accuracy Construction-tunnel: 0.0
  • Accuracy Construction-stairs: 0.0
  • Accuracy Object-pole: 0.0008
  • Accuracy Object-trafficsign: 0.0
  • Accuracy Object-trafficlight: 0.0
  • Accuracy Nature-vegetation: 0.8552
  • Accuracy Nature-terrain: 0.8507
  • Accuracy Sky: 0.8336
  • Accuracy Void-ground: 0.0
  • Accuracy Void-dynamic: 0.0
  • Accuracy Void-static: 0.0
  • Accuracy Void-unclear: 0.0
  • Iou Unlabeled: nan
  • Iou Flat-road: 0.4712
  • Iou Flat-sidewalk: 0.7651
  • Iou Flat-crosswalk: 0.0
  • Iou Flat-cyclinglane: 0.5216
  • Iou Flat-parkingdriveway: 0.0178
  • Iou Flat-railtrack: 0.0
  • Iou Flat-curb: 0.0
  • Iou Human-person: 0.0
  • Iou Human-rider: 0.0
  • Iou Vehicle-car: 0.5696
  • Iou Vehicle-truck: 0.0
  • Iou Vehicle-bus: 0.0
  • Iou Vehicle-tramtrain: 0.0
  • Iou Vehicle-motorcycle: 0.0
  • Iou Vehicle-bicycle: 0.0
  • Iou Vehicle-caravan: 0.0
  • Iou Vehicle-cartrailer: 0.0
  • Iou Construction-building: 0.4716
  • Iou Construction-door: 0.0
  • Iou Construction-wall: 0.0024
  • Iou Construction-fenceguardrail: 0.0
  • Iou Construction-bridge: 0.0
  • Iou Construction-tunnel: 0.0
  • Iou Construction-stairs: 0.0
  • Iou Object-pole: 0.0008
  • Iou Object-trafficsign: 0.0
  • Iou Object-trafficlight: 0.0
  • Iou Nature-vegetation: 0.6813
  • Iou Nature-terrain: 0.5513
  • Iou Sky: 0.7873
  • Iou Void-ground: 0.0
  • Iou Void-dynamic: 0.0
  • Iou Void-static: 0.0
  • Iou Void-unclear: 0.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10.0

Training results

Framework versions

  • Transformers 4.19.0.dev0
  • Pytorch 1.11.0+cu113
  • Datasets 2.0.0
  • Tokenizers 0.11.6
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