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segformer-b0-scene-parse-150

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segformer-b0-scene-parse-150

This model is a fine-tuned version of nvidia/mit-b0 on the scene_parse_150 dataset. It achieves the following results on the evaluation set:

  • Loss: 1.8871
  • Mean Iou: 0.1460
  • Mean Accuracy: 0.2232
  • Overall Accuracy: 0.5793
  • Per Category Iou: [0.5417922887093205, nan, nan, 0.605456283550261, nan, 0.6051961868207514, nan, nan, 0.366141159991014, nan, 0.38609129162092937, nan, nan, nan, 0.2653419956869075, nan, nan, nan, 0.21381931640902, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.23283297315187976, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.12887875050479694, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.2929226950586237, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.27411815790145844, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.029343728581220013, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan]
  • Per Category Accuracy: [0.7204037992471062, nan, nan, 0.8730764662968052, nan, 0.7047813386290621, nan, nan, 0.49876689888570863, nan, 0.5070350626923943, nan, nan, nan, 0.3890364867122187, nan, nan, nan, 0.28519434923085074, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.7522960788425246, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.21420602454444032, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.7313274624918461, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.31588862070995466, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0337932281988456, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 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: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Per Category Iou Per Category Accuracy
4.8259 1.0 20 4.9431 0.0075 0.0816 0.0986 [0.033866878957816426, 0.0, 0.0, 0.2187840751129941, 0.0, 0.15874316453216009, 0.0, 0.0, 0.10937443776762261, nan, 0.17199512469529346, 0.0, 0.0, 0.0, 0.08856035654353099, 0.0, 0.0, 0.0, 0.007011850394278918, 2.6673068203035396e-05, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.037944260621923714, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0003168839177129546, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0012988887285322558, nan, nan, nan, 0.0, 0.0, 0.0, 0.06902567674846953, 0.003795633361736032, 0.0, 0.0, 0.0, 0.021755496293271746, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] [0.0342684193458941, nan, nan, 0.22297297297297297, nan, 0.216493280133122, nan, nan, 0.1459139649451255, nan, 0.2754711314663263, nan, nan, nan, 0.17441952957833864, nan, nan, nan, 0.007226144265619443, 0.00019142419601837673, nan, nan, nan, nan, 0.0, nan, nan, 0.04833298385405745, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0003356039510562454, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0035462739496626115, nan, nan, nan, nan, nan, nan, 0.6474951433449108, 0.10350030175015087, nan, nan, nan, 0.32289628180039137, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan]
4.5279 2.0 40 4.6933 0.0328 0.1473 0.3372 [0.2859507481788653, 0.0, nan, 0.6982556302347958, 0.0, 0.285491733468006, nan, nan, 0.36053979796714714, nan, 0.26607368659958713, nan, 0.0, 0.0, 0.144348678961464, 0.0, nan, nan, 0.0018569764054762598, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0470603108858199, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0836670046341645, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.012001585493735794, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.003348940044219014, nan, nan, nan, nan, 0.0, nan, 0.05037211589910444, 0.010708862782705986, 0.0, nan, nan, 0.014184397163120567, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan] [0.3236231019038533, nan, nan, 0.8341519485587282, nan, 0.4051111070623888, nan, nan, 0.5527593262647537, nan, 0.3696824085892334, nan, nan, nan, 0.22594000811346118, nan, nan, nan, 0.0019294940185685425, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.16391276997274062, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.13248192727371677, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.19504240052185257, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.03043885140127075, nan, nan, nan, nan, nan, nan, 0.5486548537116619, 0.08086904043452021, nan, nan, nan, 0.11186162489741809, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan]
4.2521 3.0 60 4.1669 0.0675 0.1711 0.4622 [0.4469464652173291, 0.0, nan, 0.6466485682499759, 0.0, 0.31857487188100025, nan, nan, 0.401318846361439, nan, 0.28399951322587424, nan, 0.0, 0.0, 0.22989628827386488, 0.0, nan, nan, 5.267514485664836e-05, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.05571475226767522, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.07993056032571703, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.04492690408183366, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0009623763876785512, nan, nan, nan, nan, nan, nan, 0.10225176274356187, 0.0, nan, nan, nan, 0.01980585465607998, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan] [0.5582496392971111, nan, nan, 0.9072674369284539, nan, 0.4262272183455706, nan, nan, 0.5130840728939773, nan, 0.3854793903446205, nan, nan, nan, 0.38835241057295355, nan, nan, nan, 5.296650247050901e-05, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.22178653805829315, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.11610082631134977, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.5207110241356817, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.006255233216765996, nan, nan, nan, nan, nan, nan, 0.4763642785659622, 0.0, nan, nan, nan, 0.09904677734991478, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan]
4.0102 4.0 80 3.8267 0.0971 0.1820 0.4987 [0.47596724398091156, nan, nan, 0.5619428147900704, nan, 0.41035701436945804, nan, nan, 0.3668433867006756, nan, 0.30168991201473666, nan, 0.0, 0.0, 0.2577814394006326, nan, nan, nan, 0.0003507043935052531, 0.002423875170428723, nan, nan, nan, nan, 0.0, nan, nan, 0.06911165816574232, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.12806099146260058, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.07800422364833001, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.13230409785372335, 0.0, nan, nan, nan, 0.032294689037466885, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan] [0.6054319270228735, nan, nan, 0.9132988921124514, nan, 0.5888036632839592, nan, nan, 0.41769425092857615, nan, 0.39594564156468204, nan, nan, nan, 0.46417350079941455, nan, nan, nan, 0.00035563223087341765, 0.0030627871362940277, nan, nan, nan, nan, 0.0, nan, nan, 0.25372195428811073, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.19836914620540777, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.6204337899543378, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.34002472478954493, 0.0, nan, nan, nan, 0.11312417145382236, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan]
3.9433 5.0 100 3.6300 0.1000 0.1748 0.5022 [0.4872690838150047, nan, nan, 0.6555789432075341, nan, 0.38099104575104104, nan, nan, 0.24776818798526948, nan, 0.30680168814058906, nan, 0.0, nan, 0.27203756255203343, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.05785636345600635, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.15511482024540554, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0785878593127184, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.15512151372089836, 0.0, nan, nan, nan, 0.0023821497578147745, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan] [0.6564144017971698, nan, nan, 0.8441492339797425, nan, 0.51008435512963, nan, nan, 0.26079938554961507, nan, 0.4126285757188978, nan, nan, nan, 0.48344297111766904, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.1956804361501363, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.25524041034385797, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.7298597521200261, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3665450050038265, 0.0, nan, nan, nan, 0.005681459503819203, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan]
3.0593 6.0 120 3.5037 0.1023 0.1943 0.5056 [0.4663608994225103, nan, nan, 0.5734450078313733, nan, 0.4849910283791525, nan, nan, 0.2706073942634338, nan, 0.2537184463268833, nan, 0.0, nan, 0.3070884590287418, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0519561042854305, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.16124857719065408, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.105524886084823, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.18109321903656017, 0.0, nan, nan, nan, 0.007973791568797132, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan] [0.5746882916077245, nan, nan, 0.9162170645221492, nan, 0.7764315776048973, nan, nan, 0.27818881148254154, nan, 0.537908251370223, nan, nan, nan, 0.5577726162730577, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.18098133780666806, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.3096717430543588, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.7855512067840835, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.31215046800494495, 0.0, nan, nan, nan, 0.016286850577615048, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan]
3.4131 7.0 140 3.1892 0.0993 0.1704 0.5287 [0.5206754377553956, nan, nan, 0.4880050474988691, nan, 0.38862310329351324, nan, nan, 0.182552429372003, nan, 0.26886392795758796, nan, 0.0, nan, 0.3062099525008767, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.06397918777051294, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.1734145309134096, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.19739053078975982, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.18350072967636707, 0.0, nan, nan, nan, 0.007101862174325942, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 9.853996616794495e-05, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan] [0.7196762838438003, nan, nan, 0.8693736108990346, nan, 0.4943125573147254, nan, nan, 0.18960354749089425, nan, 0.3335535700878444, nan, nan, nan, 0.5348401568602497, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.23308869784021807, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.24899092055256736, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.6513372472276582, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3145935126861718, 0.0, nan, nan, nan, 0.0108579003850767, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 9.86663597046587e-05, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan]
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1.0468 50.0 1000 1.8871 0.1460 0.2232 0.5793 [0.5417922887093205, nan, nan, 0.605456283550261, nan, 0.6051961868207514, nan, nan, 0.366141159991014, nan, 0.38609129162092937, nan, nan, nan, 0.2653419956869075, nan, nan, nan, 0.21381931640902, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.23283297315187976, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.12887875050479694, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.2929226950586237, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.27411815790145844, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.029343728581220013, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan] [0.7204037992471062, nan, nan, 0.8730764662968052, nan, 0.7047813386290621, nan, nan, 0.49876689888570863, nan, 0.5070350626923943, nan, nan, nan, 0.3890364867122187, nan, nan, nan, 0.28519434923085074, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.7522960788425246, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.21420602454444032, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.7313274624918461, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.31588862070995466, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0337932281988456, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan]

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.2.0
  • Datasets 2.17.1
  • Tokenizers 0.15.2
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Safetensors
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Finetuned from

Dataset used to train dlrlseong/segformer-b0-scene-parse-150

Evaluation results

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