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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: 2.9377
  • Mean Iou: 0.0813
  • Mean Accuracy: 0.1460
  • Overall Accuracy: 0.5452
  • Per Category Iou: [0.11342061252695343, 0.3749730725623583, 0.5774403196083672, 0.9288641780654561, 0.23293471161858434, 0.7587797335920333, 0.0008910121518891353, 0.6250372002863417, 0.0, 0.046263658794501235, 0.0, 0.0, 0.0793881800301155, 0.6207545467434876, 0.019476639980881824, 0.0, 0.0, nan, 0.0, nan, 0.04454475262814041, 0.450503655979956, 0.0, 0.07486365889935548, 0.10853181799862975, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.018363489062921956, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.12465003043213634, 0.24793103448275863, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan]
  • Per Category Accuracy: [0.27903931430612183, 0.6179546707274919, 0.8842276457790319, 0.9815555501424987, 0.478820424980325, 0.8378819536634075, 0.0008953404198575075, 0.9579748021400922, nan, 0.08237878550133375, 0.0, 0.0, 0.0852678985978271, 0.9019064384433874, 0.06511318242343542, 0.0, 0.0, nan, 0.0, nan, 0.05619240278714318, 0.608628074053606, nan, 0.30184907546226886, 0.12223684807770868, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.018363489062921956, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.1372562160713089, 0.3201246660730187, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 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: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 500

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Per Category Iou Per Category Accuracy
4.749 2.0 20 4.8478 0.0133 0.0488 0.2077 [0.03605998247018025, 0.1598600016520784, 0.26309572583176816, 0.42677133598758227, 0.004334765079448734, 0.03766358996336679, 0.11137947103417706, 0.40654904556805044, 0.0, 0.0, 0.0, 0.0, 0.003533170456681075, 0.022207326209534323, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0013571249057552149, 0.08865518225944609, 0.0, 0.0, 0.0, 0.012949569976544175, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.011035178131301815, 0.0, 0.0, 0.0015187938161957736, nan, 0.0, 0.0, 0.0, nan, nan, nan, 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, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0008229662446678646, nan, nan, 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, 0.0, 0.0, 0.0, 0.0, 0.0] [0.08448409273533526, 0.2440792608301647, 0.5325700622536242, 0.4433585852434658, 0.005076925450982208, 0.03814515104990361, 0.18667847754029032, 0.7974678862891097, nan, 0.0, 0.0, 0.0, 0.004364379236697929, 0.02636802876512229, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0020229265003371545, 0.12869577231279358, nan, 0.0, 0.0, 0.06288561936402468, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.013202868440453052, 0.0, nan, 0.0055981907833759685, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.010582632723852079, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan]
4.2479 4.0 40 4.4642 0.0393 0.0909 0.3926 [0.048296056521642615, 0.26848427006320125, 0.3199142099452915, 0.8635165938689997, 0.02573846187835539, 0.3490832004795767, 0.2892577670028557, 0.42794877498720457, 0.0, 0.0, 0.0, 0.0, 0.00410545222477351, 0.1762741690045363, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.29757707032037073, 0.0, 0.0, 0.0, 0.03416066186282359, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0005441949054984616, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan] [0.17806017678045297, 0.4277296125711189, 0.8458468862417337, 0.9467615034224052, 0.03595512553431168, 0.37106213997945214, 0.31934125804853886, 0.9723571422701086, nan, 0.0, 0.0, 0.0, 0.004828674900176432, 0.22886624967227237, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.40808925117435757, nan, 0.0, 0.0, 0.07593735168485999, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0009639268898528158, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan]
3.9114 6.0 60 3.9699 0.0562 0.0957 0.4499 [0.0632231061011427, 0.3103263500221297, 0.34493970591837236, 0.8384362824860161, 0.035886448848419926, 0.4797375503022615, 0.16711988705811012, 0.47516419429872414, 0.0, 0.0, 0.0, 0.0, 0.000886973222205118, 0.20755120637058755, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.3336138241923366, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan] [0.23735900717240557, 0.48964629714403163, 0.9100930573207247, 0.9865539668233747, 0.04032220730521735, 0.6181767809112633, 0.16800015239836935, 0.989694025986834, nan, 0.0, 0.0, 0.0, 0.0009363295880149813, 0.24527135847784562, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.38342774247029565, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan]
3.6225 8.0 80 3.5706 0.0614 0.0990 0.4825 [0.061064832061792265, 0.33486164116890116, 0.36463358124974643, 0.8872377545742265, 0.04090360617678056, 0.5194226558611199, 0.041557130338705374, 0.5961814243751916, 0.0, 0.0, 0.0, 0.0, 0.00011350222464360302, 0.259991711663396, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.33446830801466737, nan, 0.0, 0.0, 0.000437636761487965, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan] [0.20610993720424986, 0.5688884425302927, 0.919581888288133, 0.9860521764548267, 0.0413252472879342, 0.7372135708266476, 0.041557130338705374, 0.9908281762370867, nan, 0.0, 0.0, 0.0, 0.00011607391586962578, 0.39946252668639276, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.3528599060513954, nan, 0.0, 0.0, 0.00047460844803037496, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan]
3.6965 10.0 100 3.3396 0.0599 0.0931 0.4703 [0.05811722694049783, 0.27211371649224914, 0.465174711675016, 0.8423600241815938, 0.018373360836894063, 0.5740602401287669, 0.04802233066859085, 0.5581545499824753, 0.0, 0.0, 0.0, 0.0, 0.0, 0.17266987871739492, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.2650796852935243, nan, 0.0, 0.0, 0.023042374536223393, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan] [0.26663640963066576, 0.4542643335606648, 0.8025924649419469, 0.9910846953937593, 0.01924293628381402, 0.8604707539219874, 0.04817693450680078, 0.9946662064317825, nan, 0.0, 0.0, 0.0, 0.0, 0.1946327577811903, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.2723127935893893, nan, 0.0, 0.0, 0.02800189843379212, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan]
2.973 12.0 120 3.1130 0.0663 0.1040 0.4961 [0.06475754342746794, 0.29382144819411427, 0.4827151585125319, 0.8473280218453675, 0.15150318191792847, 0.5366322618873052, 0.08197702590998489, 0.5845325000543338, 0.0, 0.0, 0.0, 0.0, 0.0, 0.368808247514402, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.23417742860033045, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan] [0.21631796672718073, 0.5158400209269518, 0.8105518816104086, 0.9916790490341754, 0.21307655509775783, 0.8280120516698027, 0.08218082066521888, 0.994678534151894, nan, 0.0, 0.0, 0.0, 0.0, 0.6210438593205738, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.2398798010500138, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan]
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Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu124
  • Datasets 3.0.0
  • Tokenizers 0.19.1
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