Hugging Face's logo

iammartian0
/
RoadSense_High_Definition_Street_Segmentation

Image Segmentation Transformers PyTorch TensorBoard
segformer generated_from_trainer image_segmentation Inference Endpoints
Model card Files Metrics Community
2
Edit model card

segformer-b0-finetuned-segments-sidewalk

This model is a fine-tuned version of nvidia/mit-b0 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5449
  • Mean Iou: 0.3292
  • Mean Accuracy: 0.3907
  • Overall Accuracy: 0.8555
  • Accuracy Unlabeled: nan
  • Accuracy Flat-road: 0.8585
  • Accuracy Flat-sidewalk: 0.9611
  • Accuracy Flat-crosswalk: 0.7673
  • Accuracy Flat-cyclinglane: 0.8223
  • Accuracy Flat-parkingdriveway: 0.5127
  • Accuracy Flat-railtrack: nan
  • Accuracy Flat-curb: 0.4937
  • Accuracy Human-person: 0.7164
  • Accuracy Human-rider: 0.0
  • Accuracy Vehicle-car: 0.9332
  • Accuracy Vehicle-truck: 0.0
  • Accuracy Vehicle-bus: nan
  • Accuracy Vehicle-tramtrain: nan
  • Accuracy Vehicle-motorcycle: 0.0
  • Accuracy Vehicle-bicycle: 0.3858
  • Accuracy Vehicle-caravan: 0.0
  • Accuracy Vehicle-cartrailer: 0.0
  • Accuracy Construction-building: 0.9040
  • Accuracy Construction-door: 0.0
  • Accuracy Construction-wall: 0.5848
  • Accuracy Construction-fenceguardrail: 0.4417
  • Accuracy Construction-bridge: 0.0
  • Accuracy Construction-tunnel: nan
  • Accuracy Construction-stairs: 0.0
  • Accuracy Object-pole: 0.3156
  • Accuracy Object-trafficsign: 0.0
  • Accuracy Object-trafficlight: 0.0
  • Accuracy Nature-vegetation: 0.9413
  • Accuracy Nature-terrain: 0.8456
  • Accuracy Sky: 0.9600
  • Accuracy Void-ground: 0.0
  • Accuracy Void-dynamic: 0.0
  • Accuracy Void-static: 0.2780
  • Accuracy Void-unclear: 0.0
  • Iou Unlabeled: nan
  • Iou Flat-road: 0.7447
  • Iou Flat-sidewalk: 0.8755
  • Iou Flat-crosswalk: 0.6244
  • Iou Flat-cyclinglane: 0.7325
  • Iou Flat-parkingdriveway: 0.3997
  • Iou Flat-railtrack: nan
  • Iou Flat-curb: 0.3974
  • Iou Human-person: 0.4985
  • Iou Human-rider: 0.0
  • Iou Vehicle-car: 0.7798
  • Iou Vehicle-truck: 0.0
  • Iou Vehicle-bus: nan
  • Iou Vehicle-tramtrain: nan
  • Iou Vehicle-motorcycle: 0.0
  • Iou Vehicle-bicycle: 0.2904
  • Iou Vehicle-caravan: 0.0
  • Iou Vehicle-cartrailer: 0.0
  • Iou Construction-building: 0.7233
  • Iou Construction-door: 0.0
  • Iou Construction-wall: 0.4555
  • Iou Construction-fenceguardrail: 0.3734
  • Iou Construction-bridge: 0.0
  • Iou Construction-tunnel: nan
  • Iou Construction-stairs: 0.0
  • Iou Object-pole: 0.2484
  • Iou Object-trafficsign: 0.0
  • Iou Object-trafficlight: 0.0
  • Iou Nature-vegetation: 0.8451
  • Iou Nature-terrain: 0.7346
  • Iou Sky: 0.9161
  • Iou Void-ground: 0.0
  • Iou Void-dynamic: 0.0
  • Iou Void-static: 0.2359
  • 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: 6e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

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
1.4172 1.87 200 1.2183 0.1696 0.2214 0.7509 nan 0.8882 0.9199 0.0 0.4200 0.0164 nan 0.0 0.0 0.0 0.8778 0.0 nan nan 0.0 0.0 0.0 0.0 0.8448 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9430 0.8044 0.9274 0.0 0.0 0.0 0.0 nan 0.5435 0.8135 0.0 0.3743 0.0160 nan 0.0 0.0 0.0 0.6044 0.0 nan nan 0.0 0.0 0.0 0.0 0.5373 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7516 0.6550 0.7928 0.0 0.0 0.0 0.0
1.1152 3.74 400 0.8946 0.1947 0.2441 0.7852 nan 0.8535 0.9471 0.0 0.7379 0.2453 nan 0.0398 0.0 0.0 0.8882 0.0 nan nan 0.0 0.0 0.0 0.0 0.8746 0.0 0.0061 0.0 0.0 nan 0.0 0.0014 0.0 0.0 0.9526 0.8285 0.9448 0.0 0.0 0.0019 0.0 nan 0.6355 0.8321 0.0 0.5529 0.1940 nan 0.0392 0.0 0.0 0.6807 0.0 nan nan 0.0 0.0 0.0 0.0 0.5913 0.0 0.0061 0.0 0.0 nan 0.0 0.0014 0.0 0.0 0.7701 0.6777 0.8567 0.0 0.0 0.0019 0.0
0.6637 5.61 600 0.7447 0.2349 0.2841 0.8104 nan 0.8589 0.9451 0.4455 0.8008 0.3753 nan 0.3267 0.0380 0.0 0.8920 0.0 nan nan 0.0 0.0 0.0 0.0 0.9227 0.0 0.0938 0.0 0.0 nan 0.0 0.0167 0.0 0.0 0.9291 0.8677 0.9557 0.0 0.0 0.0562 0.0 nan 0.6768 0.8543 0.4064 0.6414 0.2914 nan 0.2749 0.0376 0.0 0.7268 0.0 nan nan 0.0 0.0 0.0 0.0 0.6078 0.0 0.0879 0.0 0.0 nan 0.0 0.0164 0.0 0.0 0.8005 0.6817 0.8918 0.0 0.0 0.0525 0.0
0.673 7.48 800 0.6631 0.2691 0.3202 0.8278 nan 0.8387 0.9575 0.6176 0.7938 0.4208 nan 0.3575 0.3977 0.0 0.9264 0.0 nan nan 0.0 0.0 0.0 0.0 0.9068 0.0 0.4035 0.0 0.0 nan 0.0 0.1137 0.0 0.0 0.9495 0.8165 0.9453 0.0 0.0 0.1599 0.0 nan 0.7042 0.8567 0.5239 0.6600 0.3246 nan 0.3003 0.3212 0.0 0.7246 0.0 nan nan 0.0 0.0 0.0 0.0 0.6749 0.0 0.3113 0.0 0.0 nan 0.0 0.1038 0.0 0.0 0.8147 0.7070 0.9008 0.0 0.0 0.1445 0.0
0.502 9.35 1000 0.6249 0.2818 0.3371 0.8345 nan 0.8332 0.9538 0.7158 0.8344 0.4079 nan 0.4420 0.4941 0.0 0.9275 0.0 nan nan 0.0 0.0172 0.0 0.0 0.9102 0.0 0.4787 0.0253 0.0 nan 0.0 0.1454 0.0 0.0 0.9460 0.8350 0.9588 0.0 0.0 0.1887 0.0 nan 0.7176 0.8635 0.6035 0.6519 0.3246 nan 0.3545 0.3720 0.0 0.7524 0.0 nan nan 0.0 0.0172 0.0 0.0 0.6861 0.0 0.3286 0.0250 0.0 nan 0.0 0.1309 0.0 0.0 0.8335 0.7300 0.9037 0.0 0.0 0.1584 0.0
0.9687 11.21 1200 0.5786 0.3093 0.3675 0.8471 nan 0.8703 0.9504 0.7382 0.7705 0.5297 nan 0.4804 0.6250 0.0 0.9168 0.0 nan nan 0.0 0.1397 0.0 0.0 0.9228 0.0 0.5710 0.3183 0.0 nan 0.0 0.2252 0.0 0.0 0.9314 0.8840 0.9536 0.0 0.0 0.1981 0.0 nan 0.7380 0.8743 0.5825 0.7093 0.3829 nan 0.3743 0.4600 0.0 0.7727 0.0 nan nan 0.0 0.1372 0.0 0.0 0.7008 0.0 0.4315 0.2847 0.0 nan 0.0 0.1930 0.0 0.0 0.8397 0.7121 0.9109 0.0 0.0 0.1761 0.0
0.4681 13.08 1400 0.5759 0.3106 0.3665 0.8462 nan 0.8586 0.9572 0.5158 0.8121 0.5195 nan 0.4539 0.6944 0.0 0.9308 0.0 nan nan 0.0 0.2759 0.0 0.0 0.9126 0.0 0.4927 0.3145 0.0 nan 0.0 0.2566 0.0 0.0 0.9396 0.8736 0.9644 0.0 0.0 0.2226 0.0 nan 0.7134 0.8742 0.5009 0.7146 0.4018 nan 0.3726 0.4661 0.0 0.7674 0.0 nan nan 0.0 0.2501 0.0 0.0 0.6997 0.0 0.3933 0.2827 0.0 nan 0.0 0.2137 0.0 0.0 0.8377 0.7212 0.9109 0.0 0.0 0.1964 0.0
0.5374 14.95 1600 0.5534 0.3232 0.3823 0.8518 nan 0.8607 0.9545 0.7138 0.8398 0.5129 nan 0.4823 0.7055 0.0 0.9225 0.0 nan nan 0.0 0.3058 0.0 0.0 0.8999 0.0 0.5436 0.3798 0.0 nan 0.0 0.2878 0.0 0.0 0.9485 0.8388 0.9598 0.0 0.0 0.3145 0.0 nan 0.7336 0.8788 0.6094 0.7062 0.3966 nan 0.3854 0.4897 0.0 0.7823 0.0 nan nan 0.0 0.2782 0.0 0.0 0.7148 0.0 0.4182 0.3304 0.0 nan 0.0 0.2324 0.0 0.0 0.8415 0.7356 0.9130 0.0 0.0 0.2491 0.0
0.6115 16.82 1800 0.5528 0.3266 0.3849 0.8539 nan 0.8521 0.9611 0.6840 0.8291 0.5057 nan 0.5070 0.7165 0.0 0.9267 0.0 nan nan 0.0 0.3659 0.0 0.0 0.9007 0.0 0.5844 0.3961 0.0 nan 0.0 0.2827 0.0 0.0 0.9517 0.8371 0.9602 0.0 0.0 0.2848 0.0 nan 0.7414 0.8721 0.6312 0.7245 0.3979 nan 0.3987 0.4932 0.0 0.7799 0.0 nan nan 0.0 0.2788 0.0 0.0 0.7242 0.0 0.4542 0.3464 0.0 nan 0.0 0.2326 0.0 0.0 0.8384 0.7318 0.9141 0.0 0.0 0.2386 0.0
0.4766 18.69 2000 0.5449 0.3292 0.3907 0.8555 nan 0.8585 0.9611 0.7673 0.8223 0.5127 nan 0.4937 0.7164 0.0 0.9332 0.0 nan nan 0.0 0.3858 0.0 0.0 0.9040 0.0 0.5848 0.4417 0.0 nan 0.0 0.3156 0.0 0.0 0.9413 0.8456 0.9600 0.0 0.0 0.2780 0.0 nan 0.7447 0.8755 0.6244 0.7325 0.3997 nan 0.3974 0.4985 0.0 0.7798 0.0 nan nan 0.0 0.2904 0.0 0.0 0.7233 0.0 0.4555 0.3734 0.0 nan 0.0 0.2484 0.0 0.0 0.8451 0.7346 0.9161 0.0 0.0 0.2359 0.0

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.13.1
  • Tokenizers 0.13.3
Downloads last month
65

Dataset used to train iammartian0/RoadSense_High_Definition_Street_Segmentation

Evaluation results

Metadata error: specify a dataset to view leaderboard