Hugging Face's logo

dwang-LI
/
segformer-b-finetuned-segments-sidewalk-2

Image Segmentation
Transformers
PyTorch
segformer
vision
Generated from Trainer
Inference Endpoints
Model card Files Community
1

segformer-b-finetuned-segments-sidewalk-2

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: 0.7733
  • Mean Iou: 0.2394
  • Mean Accuracy: 0.2885
  • Overall Accuracy: 0.8145
  • Accuarcy Unlabeled: nan
  • Accuarcy Flat-road: 0.9002
  • Accuarcy Flat-sidewalk: 0.9256
  • Accuarcy Flat-crosswalk: 0.6731
  • Accuarcy Flat-cyclinglane: 0.7624
  • Accuarcy Flat-parkingdriveway: 0.3720
  • Accuarcy Flat-railtrack: nan
  • Accuarcy Flat-curb: 0.3753
  • Accuarcy Human-person: 0.0482
  • Accuarcy Human-rider: 0.0
  • Accuarcy Vehicle-car: 0.9125
  • Accuarcy Vehicle-truck: 0.0
  • Accuarcy Vehicle-bus: 0.0
  • Accuarcy Vehicle-tramtrain: nan
  • Accuarcy Vehicle-motorcycle: 0.0
  • Accuarcy Vehicle-bicycle: 0.0
  • Accuarcy Vehicle-caravan: 0.0
  • Accuarcy Vehicle-cartrailer: 0.0
  • Accuarcy Construction-building: 0.8988
  • Accuarcy Construction-door: 0.0
  • Accuarcy Construction-wall: 0.3240
  • Accuarcy Construction-fenceguardrail: 0.0009
  • Accuarcy Construction-bridge: 0.0
  • Accuarcy Construction-tunnel: nan
  • Accuarcy Construction-stairs: 0.0
  • Accuarcy Object-pole: 0.0228
  • Accuarcy Object-trafficsign: 0.0
  • Accuarcy Object-trafficlight: 0.0
  • Accuarcy Nature-vegetation: 0.9283
  • Accuarcy Nature-terrain: 0.8528
  • Accuarcy Sky: 0.9460
  • Accuarcy Void-ground: 0.0
  • Accuarcy Void-dynamic: 0.0
  • Accuarcy Void-static: 0.0014
  • Accuarcy Void-unclear: 0.0
  • Iou Unlabeled: nan
  • Iou Flat-road: 0.7132
  • Iou Flat-sidewalk: 0.8399
  • Iou Flat-crosswalk: 0.5677
  • Iou Flat-cyclinglane: 0.6711
  • Iou Flat-parkingdriveway: 0.2585
  • Iou Flat-railtrack: nan
  • Iou Flat-curb: 0.3157
  • Iou Human-person: 0.0474
  • Iou Human-rider: 0.0
  • Iou Vehicle-car: 0.7025
  • Iou Vehicle-truck: 0.0
  • Iou Vehicle-bus: 0.0
  • Iou Vehicle-tramtrain: nan
  • Iou Vehicle-motorcycle: 0.0
  • Iou Vehicle-bicycle: 0.0
  • Iou Vehicle-caravan: 0.0
  • Iou Vehicle-cartrailer: 0.0
  • Iou Construction-building: 0.6194
  • Iou Construction-door: 0.0
  • Iou Construction-wall: 0.2615
  • Iou Construction-fenceguardrail: 0.0009
  • Iou Construction-bridge: 0.0
  • Iou Construction-tunnel: nan
  • Iou Construction-stairs: 0.0
  • Iou Object-pole: 0.0227
  • Iou Object-trafficsign: 0.0
  • Iou Object-trafficlight: 0.0
  • Iou Nature-vegetation: 0.7851
  • Iou Nature-terrain: 0.7352
  • Iou Sky: 0.8791
  • Iou Void-ground: 0.0
  • Iou Void-dynamic: 0.0
  • Iou Void-static: 0.0014
  • 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: 0.0001
  • train_batch_size: 24
  • eval_batch_size: 24
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 12

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuarcy Unlabeled Accuarcy Flat-road Accuarcy Flat-sidewalk Accuarcy Flat-crosswalk Accuarcy Flat-cyclinglane Accuarcy Flat-parkingdriveway Accuarcy Flat-railtrack Accuarcy Flat-curb Accuarcy Human-person Accuarcy Human-rider Accuarcy Vehicle-car Accuarcy Vehicle-truck Accuarcy Vehicle-bus Accuarcy Vehicle-tramtrain Accuarcy Vehicle-motorcycle Accuarcy Vehicle-bicycle Accuarcy Vehicle-caravan Accuarcy Vehicle-cartrailer Accuarcy Construction-building Accuarcy Construction-door Accuarcy Construction-wall Accuarcy Construction-fenceguardrail Accuarcy Construction-bridge Accuarcy Construction-tunnel Accuarcy Construction-stairs Accuarcy Object-pole Accuarcy Object-trafficsign Accuarcy Object-trafficlight Accuarcy Nature-vegetation Accuarcy Nature-terrain Accuarcy Sky Accuarcy Void-ground Accuarcy Void-dynamic Accuarcy Void-static Accuarcy 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
2.2728 0.59 20 2.3946 0.1035 0.1549 0.6540 nan 0.6440 0.9384 0.0 0.0006 0.0001 nan 0.0001 0.0 0.0 0.9243 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.6269 0.0 0.0000 0.0002 0.0 nan 0.0 0.0 0.0 0.0 0.9320 0.0116 0.7234 0.0 0.0 0.0 0.0 nan 0.4920 0.6851 0.0 0.0006 0.0001 nan 0.0001 0.0 0.0 0.3557 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.4837 0.0 0.0000 0.0002 0.0 0.0 0.0 0.0 0.0 0.0 0.5828 0.0115 0.7007 0.0 0.0 0.0 0.0
1.9006 1.18 40 1.7230 0.1153 0.1706 0.6814 nan 0.8635 0.8762 0.0 0.0003 0.0003 nan 0.0 0.0 0.0 0.8614 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.8115 0.0 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9315 0.0405 0.9034 0.0 0.0 0.0 0.0 nan 0.4876 0.7405 0.0 0.0003 0.0003 nan 0.0 0.0 0.0 0.5225 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.5210 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6069 0.0399 0.7696 0.0 0.0 0.0 0.0
1.6721 1.76 60 1.4574 0.1289 0.1783 0.6968 nan 0.8799 0.8822 0.0 0.0528 0.0003 nan 0.0 0.0 0.0 0.8812 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.8573 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9298 0.1473 0.8959 0.0 0.0 0.0 0.0 nan 0.4937 0.7555 0.0 0.0519 0.0003 nan 0.0 0.0 0.0 0.5454 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.5547 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.6303 0.1427 0.8205 0.0 0.0 0.0 0.0
1.4066 2.35 80 1.3422 0.1589 0.2055 0.7457 nan 0.8230 0.9475 0.0 0.3015 0.0047 nan 0.0000 0.0 0.0 0.8977 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.8695 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9091 0.6841 0.9322 0.0 0.0 0.0 0.0 nan 0.6093 0.7599 0.0 0.2787 0.0046 nan 0.0000 0.0 0.0 0.5489 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.5596 0.0 0.0000 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7275 0.6092 0.8285 0.0 0.0 0.0 0.0
1.3429 2.94 100 1.1795 0.1653 0.2103 0.7562 nan 0.8569 0.9495 0.0 0.3507 0.0066 nan 0.0000 0.0 0.0 0.8981 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.8869 0.0 0.0001 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9026 0.7728 0.8950 0.0 0.0 0.0 0.0 nan 0.6153 0.7730 0.0 0.3326 0.0065 nan 0.0000 0.0 0.0 0.5899 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.5742 0.0 0.0001 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7403 0.6481 0.8448 0.0 0.0 0.0 0.0
1.2661 3.53 120 1.1008 0.1712 0.2174 0.7629 nan 0.8484 0.9495 0.0 0.4917 0.0181 nan 0.0001 0.0 0.0 0.8996 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9043 0.0 0.0001 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.8869 0.8036 0.9371 0.0 0.0 0.0 0.0 nan 0.6100 0.7894 0.0 0.4346 0.0175 nan 0.0001 0.0 0.0 0.6153 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.5608 0.0 0.0001 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7533 0.6752 0.8508 0.0 0.0 0.0 0.0
1.2166 4.12 140 1.0514 0.1771 0.2232 0.7695 nan 0.8815 0.9342 0.0 0.5539 0.0713 nan 0.0030 0.0 0.0 0.9014 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9029 0.0 0.0016 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9068 0.8398 0.9225 0.0 0.0 0.0 0.0 nan 0.6195 0.7981 0.0 0.5017 0.0642 nan 0.0030 0.0 0.0 0.6222 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.5694 0.0 0.0016 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7585 0.6979 0.8546 0.0 0.0 0.0 0.0
1.0262 4.71 160 1.0025 0.1782 0.2236 0.7665 nan 0.9188 0.9111 0.0 0.5462 0.1006 nan 0.0031 0.0 0.0 0.8814 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.8881 0.0 0.0027 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9260 0.8130 0.9404 0.0 0.0 0.0 0.0 nan 0.5776 0.8071 0.0 0.5005 0.0888 nan 0.0031 0.0 0.0 0.6651 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.5803 0.0 0.0027 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.7415 0.7028 0.8558 0.0 0.0 0.0 0.0
1.0928 5.29 180 0.9698 0.1852 0.2308 0.7778 nan 0.8513 0.9428 0.0 0.6760 0.1497 nan 0.0419 0.0 0.0 0.8856 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9132 0.0 0.0056 0.0 0.0 nan 0.0 0.0002 0.0 0.0 0.9134 0.8535 0.9219 0.0 0.0 0.0 0.0 nan 0.6410 0.8062 0.0 0.5617 0.1228 nan 0.0405 0.0 0.0 0.6597 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.5705 0.0 0.0056 0.0 0.0 nan 0.0 0.0002 0.0 0.0 0.7603 0.7081 0.8642 0.0 0.0 0.0 0.0
0.8736 5.88 200 0.9250 0.1906 0.2370 0.7850 nan 0.9149 0.9249 0.0001 0.7226 0.1944 nan 0.0715 0.0027 0.0 0.8853 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.8917 0.0 0.0153 0.0 0.0 nan 0.0 0.0005 0.0 0.0 0.9353 0.8470 0.9402 0.0 0.0 0.0 0.0 nan 0.6511 0.8250 0.0001 0.5978 0.1516 nan 0.0682 0.0027 0.0 0.6817 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.5862 0.0 0.0152 0.0 0.0 nan 0.0 0.0005 0.0 0.0 0.7477 0.7159 0.8635 0.0 0.0 0.0 0.0
0.7832 6.47 220 0.8852 0.1961 0.2421 0.7875 nan 0.8962 0.9385 0.0642 0.6975 0.2064 nan 0.1581 0.0003 0.0 0.8995 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9011 0.0 0.0392 0.0 0.0 nan 0.0 0.0009 0.0 0.0 0.8974 0.8728 0.9342 0.0 0.0 0.0 0.0 nan 0.6576 0.8222 0.0624 0.6239 0.1577 nan 0.1421 0.0003 0.0 0.6802 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.5989 0.0 0.0383 0.0 0.0 nan 0.0 0.0009 0.0 0.0 0.7547 0.6706 0.8700 0.0 0.0 0.0 0.0
0.7822 7.06 240 0.8621 0.2145 0.2598 0.7992 nan 0.8827 0.9398 0.4415 0.7426 0.2656 nan 0.2218 0.0023 0.0 0.8967 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9092 0.0 0.0558 0.0000 0.0 nan 0.0 0.0020 0.0 0.0 0.9249 0.8259 0.9429 0.0 0.0 0.0 0.0 nan 0.6911 0.8250 0.3902 0.6320 0.2017 nan 0.1950 0.0023 0.0 0.6915 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.5886 0.0 0.0540 0.0000 0.0 nan 0.0 0.0020 0.0 0.0 0.7732 0.7329 0.8703 0.0 0.0 0.0 0.0
0.6742 7.65 260 0.8371 0.2193 0.2667 0.8027 nan 0.8766 0.9312 0.3983 0.7724 0.2975 nan 0.2975 0.0055 0.0 0.9111 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9086 0.0 0.1602 0.0001 0.0 nan 0.0 0.0034 0.0 0.0 0.9371 0.8321 0.9353 0.0 0.0 0.0000 0.0 nan 0.6894 0.8388 0.3591 0.6398 0.2119 nan 0.2519 0.0055 0.0 0.6754 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.6033 0.0 0.1492 0.0001 0.0 nan 0.0 0.0034 0.0 0.0 0.7671 0.7293 0.8750 0.0 0.0 0.0000 0.0
0.8116 8.24 280 0.8277 0.2314 0.2819 0.8087 nan 0.8894 0.9207 0.6812 0.7773 0.3594 nan 0.3120 0.0109 0.0 0.9016 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.8885 0.0 0.2424 0.0005 0.0 nan 0.0 0.0107 0.0 0.0 0.9398 0.8575 0.9461 0.0 0.0 0.0003 0.0 nan 0.7112 0.8407 0.5738 0.6399 0.2424 nan 0.2666 0.0108 0.0 0.6924 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.6145 0.0 0.2148 0.0005 0.0 nan 0.0 0.0106 0.0 0.0 0.7579 0.7244 0.8738 0.0 0.0 0.0003 0.0
0.7791 8.82 300 0.8059 0.2255 0.2723 0.8077 nan 0.8684 0.9414 0.4680 0.7998 0.2901 nan 0.3174 0.0107 0.0 0.8846 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9111 0.0 0.2193 0.0000 0.0 nan 0.0 0.0099 0.0 0.0 0.9290 0.8439 0.9465 0.0 0.0 0.0000 0.0 nan 0.7039 0.8383 0.4188 0.6308 0.2131 nan 0.2698 0.0106 0.0 0.7114 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.6008 0.0 0.1942 0.0000 0.0 nan 0.0 0.0099 0.0 0.0 0.7791 0.7343 0.8760 0.0 0.0 0.0000 0.0
0.7334 9.41 320 0.7962 0.2342 0.2830 0.8117 nan 0.8921 0.9332 0.6837 0.7454 0.3381 nan 0.3264 0.0298 0.0 0.9198 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9018 0.0 0.2712 0.0003 0.0 nan 0.0 0.0182 0.0 0.0 0.9194 0.8508 0.9434 0.0 0.0 0.0008 0.0 nan 0.7121 0.8388 0.5627 0.6590 0.2316 nan 0.2794 0.0296 0.0 0.6884 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.6204 0.0 0.2324 0.0003 0.0 nan 0.0 0.0182 0.0 0.0 0.7820 0.7278 0.8762 0.0 0.0 0.0008 0.0
0.7645 10.0 340 0.7783 0.2342 0.2809 0.8133 nan 0.8999 0.9347 0.5997 0.7491 0.3278 nan 0.3613 0.0164 0.0 0.9043 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9156 0.0 0.2684 0.0003 0.0 nan 0.0 0.0167 0.0 0.0 0.9235 0.8454 0.9455 0.0 0.0 0.0007 0.0 nan 0.7218 0.8409 0.5162 0.6738 0.2390 nan 0.3039 0.0162 0.0 0.7015 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.6019 0.0 0.2260 0.0003 0.0 nan 0.0 0.0167 0.0 0.0 0.7860 0.7381 0.8764 0.0 0.0 0.0007 0.0
0.6792 10.59 360 0.7774 0.2358 0.2841 0.8141 nan 0.8954 0.9341 0.6272 0.7826 0.3543 nan 0.3360 0.0300 0.0 0.9162 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.8964 0.0 0.2909 0.0005 0.0 nan 0.0 0.0199 0.0 0.0 0.9226 0.8558 0.9443 0.0 0.0 0.0010 0.0 nan 0.7198 0.8402 0.5426 0.6699 0.2489 nan 0.2900 0.0297 0.0 0.6966 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.6186 0.0 0.2450 0.0005 0.0 nan 0.0 0.0199 0.0 0.0 0.7835 0.7251 0.8784 0.0 0.0 0.0010 0.0
0.8047 11.18 380 0.7734 0.2388 0.2878 0.8147 nan 0.8924 0.9265 0.6512 0.7739 0.3846 nan 0.3762 0.0383 0.0 0.9122 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.9053 0.0 0.3142 0.0005 0.0 nan 0.0 0.0216 0.0 0.0 0.9303 0.8513 0.9427 0.0 0.0 0.0014 0.0 nan 0.7171 0.8421 0.5575 0.6761 0.2609 nan 0.3165 0.0376 0.0 0.6982 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.6155 0.0 0.2551 0.0005 0.0 nan 0.0 0.0215 0.0 0.0 0.7854 0.7377 0.8797 0.0 0.0 0.0014 0.0
0.7136 11.76 400 0.7733 0.2394 0.2885 0.8145 nan 0.9002 0.9256 0.6731 0.7624 0.3720 nan 0.3753 0.0482 0.0 0.9125 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.8988 0.0 0.3240 0.0009 0.0 nan 0.0 0.0228 0.0 0.0 0.9283 0.8528 0.9460 0.0 0.0 0.0014 0.0 nan 0.7132 0.8399 0.5677 0.6711 0.2585 nan 0.3157 0.0474 0.0 0.7025 0.0 0.0 nan 0.0 0.0 0.0 0.0 0.6194 0.0 0.2615 0.0009 0.0 nan 0.0 0.0227 0.0 0.0 0.7851 0.7352 0.8791 0.0 0.0 0.0014 0.0

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.0.1
  • Datasets 2.12.0
  • Tokenizers 0.13.2
Downloads last month
190
Inference Examples
Image Segmentation
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for dwang-LI/segformer-b-finetuned-segments-sidewalk-2

Base model

nvidia/mit-b0
Finetuned
(352)
this model

Evaluation results

Metadata error: specify a dataset to view leaderboard