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mit-b0-finetuned-sidewalks

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segformer
generated_from_keras_callback
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mit-b0-finetuned-sidewalks / README.md
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metadata
license: other
base_model: nvidia/mit-b0
tags:
  - generated_from_keras_callback
model-index:
  - name: luca-g97/mit-b0-finetuned-sidewalks
    results: []

luca-g97/mit-b0-finetuned-sidewalks

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

  • Train Loss: 0.1941
  • Validation Loss: 0.5077
  • Validation Mean Iou: 0.3541
  • Validation Mean Accuracy: 0.4368
  • Validation Overall Accuracy: 0.8760
  • Validation Accuracy Unlabeled: 0.0
  • Validation Accuracy Flat-road: 0.8810
  • Validation Accuracy Flat-sidewalk: 0.9600
  • Validation Accuracy Flat-crosswalk: 0.5080
  • Validation Accuracy Flat-cyclinglane: 0.7956
  • Validation Accuracy Flat-parkingdriveway: 0.4939
  • Validation Accuracy Flat-railtrack: nan
  • Validation Accuracy Flat-curb: 0.5978
  • Validation Accuracy Human-person: 0.7554
  • Validation Accuracy Human-rider: 0.1367
  • Validation Accuracy Vehicle-car: 0.9179
  • Validation Accuracy Vehicle-truck: 0.0599
  • Validation Accuracy Vehicle-bus: nan
  • Validation Accuracy Vehicle-tramtrain: nan
  • Validation Accuracy Vehicle-motorcycle: 0.0
  • Validation Accuracy Vehicle-bicycle: 0.7221
  • Validation Accuracy Vehicle-caravan: 0.0
  • Validation Accuracy Vehicle-cartrailer: 0.0
  • Validation Accuracy Construction-building: 0.8944
  • Validation Accuracy Construction-door: 0.1175
  • Validation Accuracy Construction-wall: 0.6129
  • Validation Accuracy Construction-fenceguardrail: 0.4032
  • Validation Accuracy Construction-bridge: 0.0
  • Validation Accuracy Construction-tunnel: nan
  • Validation Accuracy Construction-stairs: 0.2636
  • Validation Accuracy Object-pole: 0.4817
  • Validation Accuracy Object-trafficsign: 0.3849
  • Validation Accuracy Object-trafficlight: 0.0
  • Validation Accuracy Nature-vegetation: 0.9453
  • Validation Accuracy Nature-terrain: 0.9461
  • Validation Accuracy Sky: 0.9760
  • Validation Accuracy Void-ground: 0.0649
  • Validation Accuracy Void-dynamic: 0.1096
  • Validation Accuracy Void-static: 0.5128
  • Validation Accuracy Void-unclear: 0.0
  • Validation Iou Unlabeled: 0.0
  • Validation Iou Flat-road: 0.7899
  • Validation Iou Flat-sidewalk: 0.8798
  • Validation Iou Flat-crosswalk: 0.3854
  • Validation Iou Flat-cyclinglane: 0.7444
  • Validation Iou Flat-parkingdriveway: 0.3950
  • Validation Iou Flat-railtrack: nan
  • Validation Iou Flat-curb: 0.5344
  • Validation Iou Human-person: 0.4893
  • Validation Iou Human-rider: 0.1164
  • Validation Iou Vehicle-car: 0.7795
  • Validation Iou Vehicle-truck: 0.0515
  • Validation Iou Vehicle-bus: nan
  • Validation Iou Vehicle-tramtrain: 0.0
  • Validation Iou Vehicle-motorcycle: 0.0
  • Validation Iou Vehicle-bicycle: 0.5232
  • Validation Iou Vehicle-caravan: 0.0
  • Validation Iou Vehicle-cartrailer: 0.0
  • Validation Iou Construction-building: 0.7498
  • Validation Iou Construction-door: 0.1094
  • Validation Iou Construction-wall: 0.4920
  • Validation Iou Construction-fenceguardrail: 0.3325
  • Validation Iou Construction-bridge: 0.0
  • Validation Iou Construction-tunnel: nan
  • Validation Iou Construction-stairs: 0.2083
  • Validation Iou Object-pole: 0.3678
  • Validation Iou Object-trafficsign: 0.2519
  • Validation Iou Object-trafficlight: 0.0
  • Validation Iou Nature-vegetation: 0.8789
  • Validation Iou Nature-terrain: 0.8304
  • Validation Iou Sky: 0.9409
  • Validation Iou Void-ground: 0.0447
  • Validation Iou Void-dynamic: 0.0765
  • Validation Iou Void-static: 0.3601
  • Validation Iou Void-unclear: 0.0
  • Epoch: 30

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:

  • optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 6e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
  • training_precision: float32

Training results

Train Loss Validation Loss Validation Mean Iou Validation Mean Accuracy Validation Overall Accuracy Validation Accuracy Unlabeled Validation Accuracy Flat-road Validation Accuracy Flat-sidewalk Validation Accuracy Flat-crosswalk Validation Accuracy Flat-cyclinglane Validation Accuracy Flat-parkingdriveway Validation Accuracy Flat-railtrack Validation Accuracy Flat-curb Validation Accuracy Human-person Validation Accuracy Human-rider Validation Accuracy Vehicle-car Validation Accuracy Vehicle-truck Validation Accuracy Vehicle-bus Validation Accuracy Vehicle-tramtrain Validation Accuracy Vehicle-motorcycle Validation Accuracy Vehicle-bicycle Validation Accuracy Vehicle-caravan Validation Accuracy Vehicle-cartrailer Validation Accuracy Construction-building Validation Accuracy Construction-door Validation Accuracy Construction-wall Validation Accuracy Construction-fenceguardrail Validation Accuracy Construction-bridge Validation Accuracy Construction-tunnel Validation Accuracy Construction-stairs Validation Accuracy Object-pole Validation Accuracy Object-trafficsign Validation Accuracy Object-trafficlight Validation Accuracy Nature-vegetation Validation Accuracy Nature-terrain Validation Accuracy Sky Validation Accuracy Void-ground Validation Accuracy Void-dynamic Validation Accuracy Void-static Validation Accuracy Void-unclear Validation Iou Unlabeled Validation Iou Flat-road Validation Iou Flat-sidewalk Validation Iou Flat-crosswalk Validation Iou Flat-cyclinglane Validation Iou Flat-parkingdriveway Validation Iou Flat-railtrack Validation Iou Flat-curb Validation Iou Human-person Validation Iou Human-rider Validation Iou Vehicle-car Validation Iou Vehicle-truck Validation Iou Vehicle-bus Validation Iou Vehicle-tramtrain Validation Iou Vehicle-motorcycle Validation Iou Vehicle-bicycle Validation Iou Vehicle-caravan Validation Iou Vehicle-cartrailer Validation Iou Construction-building Validation Iou Construction-door Validation Iou Construction-wall Validation Iou Construction-fenceguardrail Validation Iou Construction-bridge Validation Iou Construction-tunnel Validation Iou Construction-stairs Validation Iou Object-pole Validation Iou Object-trafficsign Validation Iou Object-trafficlight Validation Iou Nature-vegetation Validation Iou Nature-terrain Validation Iou Sky Validation Iou Void-ground Validation Iou Void-dynamic Validation Iou Void-static Validation Iou Void-unclear Epoch
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0.2286 0.5119 0.3485 0.4300 0.8739 0.0 0.9060 0.9429 0.4847 0.8465 0.4336 nan 0.6466 0.7115 0.1329 0.9134 0.0078 nan nan 0.0 0.7153 0.0 0.0 0.9073 0.0941 0.5938 0.4739 0.0 nan 0.2267 0.4684 0.3284 0.0 0.9536 0.9211 0.9768 0.0472 0.1654 0.4325 0.0 0.0 0.7818 0.8776 0.3993 0.7343 0.3657 nan 0.5462 0.5018 0.1147 0.7900 0.0070 nan 0.0 0.0 0.5133 0.0 0.0 0.7477 0.0884 0.4861 0.3631 0.0 nan 0.1971 0.3552 0.2041 0.0 0.8765 0.8029 0.9379 0.0403 0.0721 0.3499 0.0 23
0.2149 0.5215 0.3580 0.4310 0.8725 0.0 0.8805 0.9605 0.5094 0.8149 0.4046 nan 0.6248 0.7485 0.1781 0.9305 0.0324 nan nan 0.0 0.7282 0.0 0.0 0.9008 0.1662 0.6190 0.3780 0.0 nan 0.2934 0.4534 0.3489 0.0 0.9547 0.8847 0.9790 0.0458 0.1169 0.4090 0.0 0.0 0.8043 0.8722 0.3930 0.7158 0.3413 nan 0.5459 0.4605 0.1261 0.7763 0.0277 nan nan 0.0 0.4976 0.0 0.0 0.7451 0.1357 0.4977 0.3036 0.0 nan 0.2234 0.3532 0.2191 0.0 0.8704 0.8183 0.9363 0.0401 0.0650 0.3282 0.0 24
0.2116 0.5343 0.3555 0.4263 0.8695 0.0 0.8585 0.9634 0.5020 0.7841 0.5146 nan 0.5819 0.7780 0.1352 0.9088 0.0226 nan nan 0.0 0.7205 0.0 0.0 0.9146 0.0458 0.6097 0.3694 0.0 nan 0.2702 0.4659 0.3618 0.0 0.9632 0.8248 0.9759 0.0555 0.1575 0.4305 0.0 0.0 0.7861 0.8710 0.4033 0.7300 0.3961 nan 0.5124 0.4329 0.1058 0.7851 0.0195 nan nan 0.0 0.5317 0.0 0.0 0.7423 0.0426 0.5096 0.3190 0.0 nan 0.2290 0.3618 0.2138 0.0 0.8519 0.7803 0.9394 0.0424 0.0775 0.3381 0.0 25
0.2311 0.4961 0.3478 0.4364 0.8738 0.0 0.8734 0.9679 0.5055 0.7900 0.4253 nan 0.6437 0.7229 0.1989 0.8863 0.0392 nan nan 0.0011 0.6654 0.0 0.0 0.8941 0.1576 0.6591 0.4683 0.0 nan 0.2810 0.4781 0.3731 0.0 0.9497 0.9264 0.9771 0.0650 0.2566 0.3215 0.0 0.0 0.7982 0.8820 0.4066 0.7279 0.3452 nan 0.5455 0.4411 0.1509 0.7748 0.0314 nan 0.0 0.0011 0.5164 0.0 0.0 0.7315 0.1440 0.4729 0.3721 0.0 nan 0.1511 0.3584 0.2174 0.0 0.8773 0.8355 0.9361 0.0497 0.0978 0.2643 0.0 26
0.2208 0.5502 0.3493 0.4366 0.8689 0.0 0.8383 0.9673 0.5042 0.7920 0.3196 nan 0.6306 0.7779 0.1291 0.9021 0.1445 nan nan 0.0273 0.7410 0.0 0.0 0.8969 0.1208 0.5939 0.4402 0.0 nan 0.2468 0.4770 0.3748 0.0 0.9438 0.9466 0.9747 0.1148 0.1817 0.4475 0.0 0.0 0.7682 0.8633 0.4140 0.7395 0.2747 nan 0.5426 0.4367 0.1124 0.7849 0.1196 nan 0.0 0.0272 0.5120 0.0 0.0 0.7431 0.1154 0.4755 0.3410 0.0 nan 0.1788 0.3618 0.2128 0.0 0.8779 0.8179 0.9403 0.0889 0.0963 0.3342 0.0 27
0.2110 0.5231 0.3414 0.4290 0.8722 0.0 0.8684 0.9551 0.5060 0.8743 0.3936 nan 0.6295 0.7991 0.1233 0.9409 0.0481 nan nan 0.0 0.7064 0.0 0.0 0.9129 0.1463 0.4067 0.4126 0.0 nan 0.2611 0.4535 0.3979 0.0 0.9440 0.9430 0.9801 0.1153 0.0838 0.3970 0.0 0.0 0.7971 0.8795 0.3698 0.7345 0.3286 nan 0.5408 0.4354 0.1101 0.7568 0.0463 nan 0.0 0.0 0.4965 0.0 0.0 0.7422 0.1314 0.3671 0.3160 0.0 nan 0.1858 0.3615 0.2297 0.0 0.8787 0.8337 0.9380 0.0751 0.0511 0.3184 0.0 28
0.1959 0.5240 0.3521 0.4343 0.8771 0.0 0.8744 0.9749 0.4993 0.8143 0.3366 nan 0.6376 0.7596 0.1736 0.9184 0.0424 nan nan 0.0002 0.7469 0.0 0.0 0.8957 0.1516 0.5852 0.4130 0.0 nan 0.2630 0.4594 0.3941 0.0 0.9456 0.9363 0.9789 0.1007 0.1004 0.4621 0.0 0.0 0.8044 0.8775 0.4029 0.7728 0.2994 nan 0.5577 0.4748 0.1513 0.7884 0.0378 nan 0.0 0.0002 0.5007 0.0 0.0 0.7343 0.1427 0.4677 0.3440 0.0 nan 0.1765 0.3631 0.2372 0.0 0.8800 0.8324 0.9400 0.0701 0.0558 0.3558 0.0 29
0.1941 0.5077 0.3541 0.4368 0.8760 0.0 0.8810 0.9600 0.5080 0.7956 0.4939 nan 0.5978 0.7554 0.1367 0.9179 0.0599 nan nan 0.0 0.7221 0.0 0.0 0.8944 0.1175 0.6129 0.4032 0.0 nan 0.2636 0.4817 0.3849 0.0 0.9453 0.9461 0.9760 0.0649 0.1096 0.5128 0.0 0.0 0.7899 0.8798 0.3854 0.7444 0.3950 nan 0.5344 0.4893 0.1164 0.7795 0.0515 nan 0.0 0.0 0.5232 0.0 0.0 0.7498 0.1094 0.4920 0.3325 0.0 nan 0.2083 0.3678 0.2519 0.0 0.8789 0.8304 0.9409 0.0447 0.0765 0.3601 0.0 30

Framework versions

  • Transformers 4.35.0
  • TensorFlow 2.14.0
  • Datasets 2.14.6
  • Tokenizers 0.14.1