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segformer-finetuned-sidewalk-10k-steps

Image Segmentation
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Safetensors
segformer
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segformer-finetuned-sidewalk-10k-steps

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: 0.6671
  • Mean Iou: 0.0087
  • Mean Accuracy: 0.0760
  • Overall Accuracy: 0.0202
  • Accuracy Unlabeled: nan
  • Accuracy Flat-road: 0.0334
  • Accuracy Flat-sidewalk: 0.0053
  • Accuracy Flat-crosswalk: 0.0295
  • Accuracy Flat-cyclinglane: nan
  • Accuracy Flat-parkingdriveway: 0.0
  • Accuracy Flat-railtrack: 0.0
  • Accuracy Flat-curb: 0.6098
  • Accuracy Human-person: 0.0
  • Accuracy Human-rider: 0.1528
  • Accuracy Vehicle-car: 0.0
  • 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: 0.8541
  • Accuracy Construction-building: 0.0
  • Accuracy Construction-door: 0.3126
  • Accuracy Construction-wall: 0.1394
  • Accuracy Construction-fenceguardrail: nan
  • Accuracy Construction-bridge: 0.0
  • Accuracy Construction-tunnel: 0.0
  • Accuracy Construction-stairs: 0.0524
  • Accuracy Object-pole: 0.0
  • Accuracy Object-trafficsign: 0.0
  • Accuracy Object-trafficlight: 0.0510
  • Accuracy Nature-vegetation: 0.0
  • Accuracy Nature-terrain: 0.0
  • Accuracy Sky: 0.0
  • Accuracy Void-ground: 0.0404
  • Accuracy Void-dynamic: 0.0
  • Accuracy Void-static: nan
  • Accuracy Void-unclear: nan
  • Iou Unlabeled: 0.0
  • Iou Flat-road: 0.0034
  • Iou Flat-sidewalk: 0.0053
  • Iou Flat-crosswalk: 0.0100
  • Iou Flat-cyclinglane: 0.0
  • Iou Flat-parkingdriveway: 0.0
  • Iou Flat-railtrack: 0.0
  • Iou Flat-curb: 0.0369
  • Iou Human-person: 0.0
  • Iou Human-rider: 0.0021
  • Iou Vehicle-car: 0.0
  • 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: 0.0300
  • Iou Construction-building: 0.0
  • Iou Construction-door: 0.1152
  • Iou Construction-wall: 0.0163
  • Iou Construction-fenceguardrail: nan
  • Iou Construction-bridge: 0.0
  • Iou Construction-tunnel: 0.0
  • Iou Construction-stairs: 0.0089
  • Iou Object-pole: 0.0
  • Iou Object-trafficsign: 0.0
  • Iou Object-trafficlight: 0.0107
  • Iou Nature-vegetation: 0.0
  • Iou Nature-terrain: 0.0
  • Iou Sky: 0.0
  • Iou Void-ground: 0.0394
  • Iou Void-dynamic: 0.0
  • Iou Void-static: nan
  • Iou Void-unclear: nan

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: 6e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 1337
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: polynomial
  • training_steps: 10000

Training results

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
2.5995 1.0 107 1.8385 0.0021 0.0554 0.0176 nan 0.5073 0.0 0.0084 nan 0.0 0.0 0.0 0.0 0.0978 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9620 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.0 0.0870 0.0 0.0 0.0 0.0 0.0 nan nan 0.0 0.0249 0.0 0.0063 0.0 0.0 0.0 0.0 0.0 0.0013 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0198 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.0 0.0158 0.0 0.0 0.0 0.0 0.0 nan nan
1.8272 2.0 214 1.4737 0.0020 0.0493 0.0133 nan 0.4689 0.0 0.0236 nan 0.0 0.0 0.0 0.0 0.0036 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9609 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.0 0.0227 0.0001 0.0 0.0 0.0 0.0 nan nan 0.0 0.0284 0.0 0.0099 0.0 0.0 0.0 0.0 0.0 0.0001 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0207 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.0 0.0048 0.0000 0.0 0.0 0.0 0.0 nan nan
1.5133 3.0 321 1.2785 0.0021 0.0521 0.0136 nan 0.4276 0.0 0.0252 nan 0.0 0.0 0.0 0.0 0.1276 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9517 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.0 0.0297 0.0000 0.0 0.0 0.0 0.0 nan nan 0.0 0.0284 0.0 0.0096 0.0 0.0 0.0 0.0 0.0 0.0015 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0224 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.0 0.0064 0.0000 0.0 0.0 0.0 0.0 nan nan
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1.1886 5.0 535 1.0609 0.0022 0.0593 0.0158 nan 0.6557 0.0 0.0145 nan 0.0 0.0 0.0 0.0 0.1333 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9366 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.0 0.0399 0.0001 0.0 0.0 0.0 0.0 nan nan 0.0 0.0306 0.0 0.0059 0.0 0.0 0.0 0.0 0.0 0.0014 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0251 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.0 0.0086 0.0000 0.0 0.0 0.0 0.0 nan nan
1.0999 6.0 642 1.0146 0.0018 0.0452 0.0136 nan 0.2631 0.0 0.0214 nan 0.0 0.0 0.0 0.0 0.0332 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9858 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.0 0.0530 0.0000 0.0 0.0 0.0 0.0 nan nan 0.0 0.0184 0.0 0.0080 0.0 0.0 0.0 0.0 0.0 0.0005 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0191 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.0 0.0122 0.0000 0.0 0.0 0.0 0.0 nan nan
1.0238 7.0 749 0.9696 0.0015 0.0400 0.0103 nan 0.0896 0.0 0.0119 nan 0.0 0.0 0.0 0.0 0.0933 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9646 0.0 0.0051 0.0 nan 0.0 0.0 0.0 0.0 0.0 0.0368 0.0000 0.0 0.0 0.0 0.0 nan nan 0.0 0.0072 0.0 0.0039 0.0 0.0 0.0 0.0 0.0 0.0011 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0233 0.0 0.0047 0.0 nan 0.0 0.0 0.0 0.0 0.0 0.0081 0.0000 0.0 0.0 0.0 0.0 nan nan
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0.9012 9.0 963 0.9197 0.0022 0.0397 0.0101 nan 0.0588 0.0024 0.0154 nan 0.0 0.0 0.0 0.0 0.1213 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9276 0.0 0.0424 0.0 nan 0.0 0.0 0.0 0.0 0.0 0.0231 0.0 0.0 0.0 0.0 0.0 nan nan 0.0 0.0039 0.0024 0.0056 0.0 0.0 0.0 0.0 0.0 0.0012 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0258 0.0 0.0262 0.0 nan 0.0 0.0 0.0 0.0 0.0 0.0052 0.0 0.0 0.0 0.0 0.0 nan nan
0.8737 10.0 1070 0.8625 0.0035 0.0469 0.0249 nan 0.0627 0.0030 0.0101 nan 0.0 0.0 0.0386 0.0 0.0878 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9399 0.0 0.0296 0.0 nan 0.0 0.0 0.0 0.0 0.0 0.2364 0.0 0.0 0.0 0.0 0.0 nan nan 0.0 0.0041 0.0029 0.0036 0.0 0.0 0.0 0.0109 0.0 0.0011 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0250 0.0 0.0169 0.0 nan 0.0 0.0 0.0 0.0 0.0 0.0461 0.0 0.0 0.0 0.0 0.0 nan nan
0.8142 11.0 1177 0.8071 0.0041 0.0451 0.0127 nan 0.0467 0.0000 0.0075 nan 0.0 0.0 0.0834 0.0 0.1078 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9049 0.0 0.1574 0.0 nan 0.0 0.0 0.0071 0.0 0.0 0.0389 0.0 0.0 0.0 0.0 0.0 nan nan 0.0 0.0038 0.0000 0.0029 0.0 0.0 0.0 0.0143 0.0 0.0012 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0252 0.0 0.0733 0.0 nan 0.0 0.0 0.0025 0.0 0.0 0.0083 0.0 0.0 0.0 0.0 0.0 nan nan
0.8075 12.0 1284 0.7903 0.0048 0.0581 0.0165 nan 0.0643 0.0034 0.0132 nan 0.0 0.0 0.3681 0.0 0.1294 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9284 0.0 0.1181 0.0 nan 0.0 0.0 0.0316 0.0 0.0 0.0856 0.0 0.0 0.0 0.0 0.0 nan nan 0.0 0.0049 0.0034 0.0051 0.0 0.0 0.0 0.0299 0.0 0.0016 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0280 0.0 0.0558 0.0 nan 0.0 0.0 0.0092 0.0 0.0 0.0170 0.0 0.0 0.0 0.0 0.0 nan nan
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0.7355 14.0 1498 0.8104 0.0064 0.0672 0.0161 nan 0.0468 0.0082 0.0125 nan 0.0 0.0000 0.5252 0.0 0.1321 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8908 0.0 0.2535 0.0054 nan 0.0 0.0 0.1050 0.0 0.0 0.0375 0.0 0.0 0.0 0.0 0.0 nan nan 0.0 0.0044 0.0079 0.0040 0.0 0.0 0.0000 0.0323 0.0 0.0015 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0279 0.0 0.1002 0.0037 nan 0.0 0.0 0.0155 0.0 0.0 0.0082 0.0 0.0 0.0 0.0 0.0 nan nan
0.6964 15.0 1605 0.8245 0.0067 0.0748 0.0177 nan 0.0813 0.0062 0.0057 nan 0.0 0.0000 0.6631 0.0 0.1364 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8824 0.0 0.3576 0.0013 nan 0.0 0.0 0.0763 0.0 0.0 0.0322 0.0 0.0 0.0 0.0 0.0 nan nan 0.0 0.0052 0.0060 0.0022 0.0 0.0 0.0000 0.0306 0.0 0.0017 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0282 0.0 0.1206 0.0013 nan 0.0 0.0 0.0120 0.0 0.0 0.0072 0.0 0.0 0.0 0.0 0.0 nan nan
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0.6732 17.0 1819 0.7195 0.0088 0.0819 0.0227 nan 0.0591 0.0009 0.0050 nan 0.0 0.0 0.6473 0.0 0.1374 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8892 0.0 0.4902 0.0121 nan 0.0 0.0 0.1447 0.0 0.0 0.0711 0.0 0.0 0.0 0.0 0.0 nan nan 0.0 0.0046 0.0009 0.0020 0.0 0.0 0.0 0.0321 0.0 0.0016 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0292 0.0 0.1665 0.0059 nan 0.0 0.0 0.0244 0.0 0.0 0.0148 0.0 0.0 0.0 0.0 0.0 nan nan
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0.3429 92.0 9844 0.6707 0.0086 0.0760 0.0200 nan 0.0369 0.0059 0.0274 nan 0.0 0.0 0.6039 0.0 0.1397 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8562 0.0 0.3154 0.1514 nan 0.0 0.0 0.0562 0.0 0.0 0.0488 0.0 0.0 0.0 0.0379 0.0001 nan nan 0.0 0.0036 0.0059 0.0093 0.0 0.0 0.0 0.0375 0.0 0.0020 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0295 0.0 0.1139 0.0164 nan 0.0 0.0 0.0097 0.0 0.0 0.0103 0.0 0.0 0.0 0.0369 0.0000 nan nan
0.363 93.0 9951 0.6679 0.0088 0.0761 0.0202 nan 0.0343 0.0060 0.0267 nan 0.0 0.0 0.6071 0.0 0.1508 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8634 0.0 0.3179 0.1394 nan 0.0 0.0 0.0453 0.0 0.0 0.0507 0.0 0.0 0.0 0.0417 0.0 nan nan 0.0 0.0034 0.0059 0.0091 0.0 0.0 0.0 0.0368 0.0 0.0021 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0292 0.0 0.1179 0.0163 nan 0.0 0.0 0.0085 0.0 0.0 0.0107 0.0 0.0 0.0 0.0406 0.0 nan nan
0.3332 93.4579 10000 0.6671 0.0087 0.0760 0.0202 nan 0.0334 0.0053 0.0295 nan 0.0 0.0 0.6098 0.0 0.1528 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8541 0.0 0.3126 0.1394 nan 0.0 0.0 0.0524 0.0 0.0 0.0510 0.0 0.0 0.0 0.0404 0.0 nan nan 0.0 0.0034 0.0053 0.0100 0.0 0.0 0.0 0.0369 0.0 0.0021 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0300 0.0 0.1152 0.0163 nan 0.0 0.0 0.0089 0.0 0.0 0.0107 0.0 0.0 0.0 0.0394 0.0 nan nan

Framework versions

  • Transformers 4.54.0.dev0
  • Pytorch 2.7.1+xpu
  • Datasets 3.6.0
  • Tokenizers 0.21.2
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