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metadata
library_name: transformers
license: other
base_model: nvidia/mit-b2
tags:
  - image-segmentation
  - vision
  - generated_from_trainer
model-index:
  - name: segformer-finetuned-sidewalk-10k-steps
    results: []

segformer-finetuned-sidewalk-10k-steps

This model is a fine-tuned version of nvidia/mit-b2 on the segments/sidewalk-semantic dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6343
  • Mean Iou: 0.3675
  • Mean Accuracy: 0.4554
  • Overall Accuracy: 0.8499
  • Accuracy Unlabeled: nan
  • Accuracy Flat-road: 0.9433
  • Accuracy Flat-sidewalk: 0.7564
  • Accuracy Flat-crosswalk: 0.8913
  • Accuracy Flat-cyclinglane: 0.3568
  • Accuracy Flat-parkingdriveway: nan
  • Accuracy Flat-railtrack: 0.6624
  • Accuracy Flat-curb: 0.6380
  • Accuracy Human-person: 0.6670
  • Accuracy Human-rider: 0.9602
  • Accuracy Vehicle-car: 0.0
  • Accuracy Vehicle-truck: 0.7202
  • Accuracy Vehicle-bus: 0.0
  • Accuracy Vehicle-tramtrain: 0.0
  • Accuracy Vehicle-motorcycle: 0.8125
  • Accuracy Vehicle-bicycle: 0.0
  • Accuracy Vehicle-caravan: 0.0394
  • Accuracy Vehicle-cartrailer: 0.8794
  • Accuracy Construction-building: 0.0198
  • Accuracy Construction-door: 0.6460
  • Accuracy Construction-wall: 0.4446
  • Accuracy Construction-fenceguardrail: 0.15
  • Accuracy Construction-bridge: nan
  • Accuracy Construction-tunnel: 0.0324
  • Accuracy Construction-stairs: 0.6618
  • Accuracy Object-pole: 0.3908
  • Accuracy Object-trafficsign: 0.0155
  • Accuracy Object-trafficlight: 0.9407
  • Accuracy Nature-vegetation: 0.7856
  • Accuracy Nature-terrain: 0.9523
  • Accuracy Sky: 0.0001
  • Accuracy Void-ground: 0.4305
  • Accuracy Void-dynamic: 0.3187
  • Accuracy Void-static: 0.0
  • Accuracy Void-unclear: nan
  • Iou Unlabeled: 0.0
  • Iou Flat-road: 0.8597
  • Iou Flat-sidewalk: 0.7477
  • Iou Flat-crosswalk: 0.7475
  • Iou Flat-cyclinglane: 0.3261
  • Iou Flat-parkingdriveway: nan
  • Iou Flat-railtrack: 0.5300
  • Iou Flat-curb: 0.5338
  • Iou Human-person: 0.3441
  • Iou Human-rider: 0.8786
  • Iou Vehicle-car: 0.0
  • Iou Vehicle-truck: 0.6854
  • Iou Vehicle-bus: 0.0
  • Iou Vehicle-tramtrain: 0.0
  • Iou Vehicle-motorcycle: 0.6862
  • Iou Vehicle-bicycle: 0.0
  • Iou Vehicle-caravan: 0.0302
  • Iou Vehicle-cartrailer: 0.7283
  • Iou Construction-building: 0.0198
  • Iou Construction-door: 0.4970
  • Iou Construction-wall: 0.3062
  • Iou Construction-fenceguardrail: 0.0139
  • Iou Construction-bridge: nan
  • Iou Construction-tunnel: 0.0288
  • Iou Construction-stairs: 0.4300
  • Iou Object-pole: 0.3498
  • Iou Object-trafficsign: 0.0155
  • Iou Object-trafficlight: 0.8365
  • Iou Nature-vegetation: 0.7142
  • Iou Nature-terrain: 0.9339
  • Iou Sky: 0.0001
  • Iou Void-ground: 0.2573
  • Iou Void-dynamic: 0.2598
  • Iou Void-static: 0.0
  • 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 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
1.6022 1.0 107 1.1070 0.1500 0.2002 0.7201 nan 0.9618 0.0 0.2737 0.1123 nan 0.0529 0.0265 0.0 0.9254 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8882 0.0 0.0713 0.0416 0.0 nan 0.0 0.3574 0.0 0.0 0.7896 0.7885 0.9139 0.0 0.0 0.0028 0.0 nan 0.0 0.7325 0.0 0.2694 0.0973 nan 0.0494 0.0262 0.0 0.7046 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5598 0.0 0.0635 0.0402 0.0 nan 0.0 0.2539 0.0 0.0 0.6949 0.4728 0.8315 0.0 0.0 0.0028 0.0 nan
0.9301 2.0 214 0.9070 0.1912 0.2461 0.7569 nan 0.9635 0.0897 0.4954 0.1958 nan 0.2859 0.2844 0.0 0.9124 0.0 0.0 0.0 0.0 0.0429 0.0 0.0 0.8211 0.0 0.4868 0.0700 0.0 nan 0.0 0.3948 0.0 0.0 0.8468 0.8058 0.8470 0.0 0.0 0.0883 0.0 nan 0.0 0.7896 0.0893 0.4704 0.1734 nan 0.2320 0.2401 0.0 0.7492 0.0 0.0 0.0 0.0 0.0427 0.0 0.0 0.6045 0.0 0.2655 0.0677 0.0 nan 0.0 0.2813 0.0 0.0 0.7204 0.5083 0.8128 0.0 0.0 0.0709 0.0 nan
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0.1697 82.0 8774 0.6282 0.3631 0.4433 0.8553 nan 0.9649 0.7444 0.8983 0.3198 nan 0.6134 0.6597 0.6757 0.9608 0.0 0.5509 0.0 0.0 0.8053 0.0 0.0387 0.8854 0.0480 0.6178 0.4345 0.0 nan 0.0250 0.6563 0.4026 0.0152 0.9360 0.7849 0.9592 0.0035 0.4236 0.3191 0.0 nan 0.0 0.8690 0.7383 0.8198 0.2968 nan 0.5073 0.5432 0.3594 0.8761 0.0 0.5307 0.0 0.0 0.6783 0.0 0.0284 0.7266 0.0461 0.4823 0.3155 0.0 nan 0.0215 0.4262 0.3552 0.0151 0.8401 0.7128 0.9359 0.0035 0.2291 0.2627 0.0 nan
0.1759 83.0 8881 0.6254 0.3687 0.4556 0.8546 nan 0.9487 0.7577 0.9105 0.3828 nan 0.6561 0.6781 0.6462 0.9595 0.0 0.5947 0.0 0.0 0.8039 0.0 0.0540 0.8859 0.0652 0.6671 0.3923 0.1562 nan 0.0424 0.6636 0.4088 0.0095 0.9365 0.7900 0.9583 0.0006 0.4436 0.3098 0.0 nan 0.0 0.8678 0.7495 0.7744 0.3465 nan 0.5252 0.5508 0.3706 0.8805 0.0 0.5768 0.0 0.0 0.6856 0.0 0.0305 0.7298 0.0635 0.4991 0.2812 0.0136 nan 0.0361 0.4310 0.3592 0.0095 0.8398 0.7151 0.9358 0.0006 0.2675 0.2583 0.0 nan
0.1845 84.0 8988 0.6191 0.3661 0.4453 0.8550 nan 0.9603 0.6939 0.8936 0.3612 nan 0.6130 0.6616 0.6170 0.9583 0.0 0.6178 0.0 0.0 0.8085 0.0 0.0566 0.8824 0.0209 0.6377 0.4212 0.0625 nan 0.0178 0.6805 0.3949 0.0 0.9412 0.8005 0.9584 0.0037 0.4240 0.3153 0.0 nan 0.0 0.8686 0.6899 0.8021 0.3289 nan 0.5149 0.5529 0.3929 0.8815 0.0 0.5950 0.0 0.0 0.6950 0.0 0.0302 0.7284 0.0209 0.4950 0.3162 0.0064 nan 0.0163 0.4234 0.3515 0.0 0.8405 0.7180 0.9361 0.0037 0.2467 0.2589 0.0 nan
0.1596 85.0 9095 0.6263 0.3657 0.4544 0.8493 nan 0.9426 0.7419 0.8969 0.3595 nan 0.6549 0.6157 0.6517 0.9617 0.0 0.6764 0.0 0.0 0.8125 0.0 0.0647 0.8916 0.0132 0.6306 0.4466 0.2 nan 0.0382 0.6448 0.4031 0.0201 0.9325 0.7857 0.9600 0.0 0.4219 0.3200 0.0 nan 0.0 0.8624 0.7354 0.7443 0.3285 nan 0.5277 0.5261 0.3375 0.8764 0.0 0.6417 0.0 0.0 0.6802 0.0 0.0334 0.7263 0.0132 0.4879 0.3006 0.0171 nan 0.0325 0.4294 0.3556 0.0201 0.8405 0.7180 0.9360 0.0 0.2714 0.2606 0.0 nan
0.1553 86.0 9202 0.6448 0.3621 0.4523 0.8471 nan 0.9444 0.7536 0.8986 0.2873 nan 0.6561 0.5925 0.6642 0.9597 0.0 0.7311 0.0 0.0 0.8175 0.0 0.0600 0.8902 0.0174 0.6205 0.4392 0.15 nan 0.0332 0.6709 0.3955 0.0189 0.9333 0.7839 0.9570 0.0 0.4379 0.3088 0.0 nan 0.0 0.8583 0.7470 0.7471 0.2693 nan 0.5274 0.5115 0.3220 0.8794 0.0 0.6809 0.0 0.0 0.6799 0.0 0.0433 0.7252 0.0173 0.4812 0.2932 0.0141 nan 0.0291 0.4259 0.3535 0.0189 0.8370 0.7127 0.9355 0.0 0.2215 0.2550 0.0 nan
0.1557 87.0 9309 0.6366 0.3634 0.4460 0.8525 nan 0.9470 0.7521 0.9110 0.3741 nan 0.6356 0.6082 0.6606 0.9557 0.0 0.6639 0.0 0.0 0.8120 0.0 0.0422 0.8898 0.0074 0.6472 0.4156 0.0312 nan 0.0134 0.6581 0.3902 0.0030 0.9408 0.7783 0.9553 0.0 0.4195 0.3130 0.0 nan 0.0 0.8618 0.7468 0.7629 0.3438 nan 0.5229 0.5228 0.3351 0.8831 0.0 0.6489 0.0 0.0 0.6834 0.0 0.0362 0.7254 0.0074 0.4954 0.2944 0.0043 nan 0.0123 0.4301 0.3508 0.0030 0.8376 0.7135 0.9350 0.0 0.2167 0.2568 0.0 nan
0.1717 88.0 9416 0.6363 0.3677 0.4602 0.8513 nan 0.9474 0.7618 0.8904 0.3745 nan 0.6498 0.6481 0.6823 0.9592 0.0 0.7203 0.0 0.0 0.8057 0.0 0.0503 0.8838 0.0094 0.6700 0.4280 0.2437 nan 0.0436 0.6507 0.3993 0.0258 0.9316 0.7769 0.9590 0.0000 0.4301 0.3255 0.0 nan 0.0 0.8612 0.7545 0.7491 0.3423 nan 0.5243 0.5386 0.3401 0.8795 0.0 0.6688 0.0 0.0 0.6842 0.0 0.0354 0.7296 0.0094 0.5004 0.2950 0.0182 nan 0.0381 0.4270 0.3540 0.0257 0.8405 0.7115 0.9357 0.0000 0.2432 0.2617 0.0 nan
0.1364 89.0 9523 0.6415 0.3675 0.4577 0.8498 nan 0.9461 0.7699 0.8952 0.3342 nan 0.6375 0.6155 0.6783 0.9598 0.0 0.7679 0.0 0.0 0.8117 0.0 0.0416 0.8834 0.0204 0.6517 0.4052 0.225 nan 0.0323 0.6599 0.3950 0.0216 0.9374 0.7838 0.9562 0.0001 0.4317 0.3280 0.0 nan 0.0 0.8580 0.7599 0.7437 0.3094 nan 0.5260 0.5210 0.3331 0.8797 0.0 0.7298 0.0 0.0 0.6873 0.0 0.0339 0.7272 0.0204 0.4901 0.2975 0.0178 nan 0.0288 0.4305 0.3513 0.0216 0.8409 0.7161 0.9354 0.0001 0.2373 0.2623 0.0 nan
0.158 90.0 9630 0.6370 0.3672 0.4582 0.8512 nan 0.9563 0.7639 0.8676 0.3162 nan 0.6235 0.6300 0.6667 0.9545 0.0 0.8083 0.0 0.0 0.8174 0.0026 0.0421 0.8780 0.0271 0.6564 0.4406 0.2 nan 0.0331 0.6646 0.3940 0.0178 0.9357 0.7985 0.9567 0.0001 0.4184 0.3341 0.0 nan 0.0 0.8582 0.7542 0.7574 0.2950 nan 0.5214 0.5283 0.3325 0.8836 0.0 0.6996 0.0 0.0 0.6894 0.0023 0.0369 0.7299 0.0270 0.4996 0.3114 0.0171 nan 0.0296 0.4279 0.3502 0.0178 0.8409 0.7218 0.9358 0.0001 0.2178 0.2629 0.0 nan
0.1604 91.0 9737 0.6377 0.3680 0.4566 0.8503 nan 0.9476 0.7486 0.8878 0.3495 nan 0.6397 0.6496 0.6712 0.9572 0.0 0.7654 0.0 0.0 0.8113 0.0028 0.0474 0.8835 0.0145 0.6551 0.4330 0.1625 nan 0.0285 0.6574 0.3951 0.0220 0.9372 0.7868 0.9538 0.0002 0.4259 0.3224 0.0 nan 0.0 0.8588 0.7411 0.7498 0.3202 nan 0.5264 0.5393 0.3456 0.8818 0.0 0.7120 0.0 0.0 0.6867 0.0026 0.0380 0.7266 0.0145 0.4968 0.3089 0.0167 nan 0.0254 0.4301 0.3520 0.0220 0.8391 0.7157 0.9343 0.0002 0.2307 0.2604 0.0 nan
0.1394 92.0 9844 0.6513 0.3669 0.4544 0.8487 nan 0.9485 0.7507 0.8841 0.3155 nan 0.6332 0.6399 0.6662 0.9562 0.0 0.7679 0.0 0.0 0.8061 0.0015 0.0410 0.8879 0.0153 0.6529 0.4204 0.2062 nan 0.0279 0.6589 0.3786 0.0178 0.9372 0.7815 0.9500 0.0001 0.4198 0.3204 0.0 nan 0.0 0.8569 0.7430 0.7463 0.2945 nan 0.5210 0.5389 0.3490 0.8820 0.0 0.7220 0.0 0.0 0.6915 0.0014 0.0333 0.7247 0.0153 0.4960 0.3024 0.0194 nan 0.0250 0.4290 0.3435 0.0178 0.8397 0.7124 0.9326 0.0001 0.2429 0.2603 0.0 nan
0.1517 93.0 9951 0.6587 0.3671 0.4553 0.8497 nan 0.9497 0.7316 0.8852 0.3586 nan 0.6265 0.6221 0.6655 0.9580 0.0 0.7487 0.0 0.0 0.8097 0.0003 0.0332 0.8893 0.0080 0.6474 0.4295 0.2437 nan 0.0247 0.6429 0.3869 0.0326 0.9309 0.7872 0.9543 0.0001 0.4314 0.3176 0.0 nan 0.0 0.8577 0.7276 0.7485 0.3255 nan 0.5202 0.5295 0.3450 0.8808 0.0 0.7103 0.0 0.0 0.6907 0.0003 0.0276 0.7250 0.0080 0.4970 0.3078 0.0160 nan 0.0224 0.4286 0.3480 0.0325 0.8411 0.7139 0.9342 0.0001 0.2503 0.2596 0.0 nan
0.1732 93.4579 10000 0.6343 0.3675 0.4554 0.8499 nan 0.9433 0.7564 0.8913 0.3568 nan 0.6624 0.6380 0.6670 0.9602 0.0 0.7202 0.0 0.0 0.8125 0.0 0.0394 0.8794 0.0198 0.6460 0.4446 0.15 nan 0.0324 0.6618 0.3908 0.0155 0.9407 0.7856 0.9523 0.0001 0.4305 0.3187 0.0 nan 0.0 0.8597 0.7477 0.7475 0.3261 nan 0.5300 0.5338 0.3441 0.8786 0.0 0.6854 0.0 0.0 0.6862 0.0 0.0302 0.7283 0.0198 0.4970 0.3062 0.0139 nan 0.0288 0.4300 0.3498 0.0155 0.8365 0.7142 0.9339 0.0001 0.2573 0.2598 0.0 nan

Framework versions

  • Transformers 4.46.0
  • Pytorch 2.1.0
  • Datasets 3.0.2
  • Tokenizers 0.20.1