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segformer-b0-scene-parse-150

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PyTorch
segformer
generated_from_trainer
Inference Endpoints
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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: 2.1858
  • Mean Iou: 0.0790
  • Mean Accuracy: 0.1487
  • Overall Accuracy: 0.5585
  • Per Category Iou: [0.39946083955868833, 0.6931863563152424, 0.8856013192534284, 0.2809612853485767, 0.3286335700053321, 0.38266719751692085, 0.5326348118588471, nan, 0.20548785708170428, 0.0, 0.0, 0.0, 0.4152002040295843, 0.01700425106276569, 0.0, 0.0007944862653186883, 0.0, 0.0, 0.0, 0.0010346076250581967, 0.0, nan, 0.0, 0.00684602700841143, 0.0, 0.0, 0.0, nan, 0.1402944564188534, 0.0, 0.05702028778264367, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 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, 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, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan]
  • Per Category Accuracy: [0.8725965608555194, 0.8579027044719938, 0.9515903462512638, 0.9541864367550053, 0.575926581680831, 0.6245822502784998, 0.9146689309996352, nan, 0.30043392998706553, nan, 0.0, 0.0, 0.5437541750167001, 0.01701669440852958, 0.0, 0.0008361378791362696, 0.0, 0.0, 0.0, 0.0017671747294013696, 0.0, nan, 0.0, 0.0068794831997986495, 0.0, 0.0, nan, nan, 0.8845268909979868, nan, 0.07536471561041876, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 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, 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, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 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
4.909 1.0 20 4.9286 0.0069 0.0214 0.0999 [0.14770211251096196, 0.015495906801007557, 0.5692483503520478, 0.04629655526033655, 0.006283550414048521, 0.021920465567410282, 0.03168867919012623, 0.0, 0.03431364315826601, 0.0, 0.003308311609451721, 0.0, 0.002239448751076658, 0.01245106050611559, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0017911687592478821, 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, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0010832280194981044, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.003616335303600215, 0.0, nan, 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, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 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, 0.0, 0.0001942124684404739, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 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] [0.17281396246605216, 0.016108997176874924, 0.5983816478727864, 0.14367550052077305, 0.008911709159584514, 0.025476102063551005, 0.03241025886348823, nan, 0.04262110401802478, nan, 0.0068573412612299815, 0.0, 0.002481152781753984, 0.014385324996871932, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0021708125340828056, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.00475142982842059, nan, nan, nan, nan, nan, nan, nan, 0.017850511909182538, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 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, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.000484027105517909, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan]
4.6558 2.0 40 4.7029 0.0157 0.0534 0.2529 [0.3404998035000116, 0.1697248274580773, 0.7439186751110503, 0.06009508908228201, 0.058379546501835286, 0.10828767254052196, 0.08309252217997465, 0.0, 0.04723353699932111, 0.0, 0.004242156429518339, 0.0, 0.02869154742587402, 0.010523767529525687, 0.0, 0.0004209861600799874, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.010917822315872005, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 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, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.0] [0.6982790732325114, 0.20183789533979787, 0.8424731125977123, 0.5456110403888439, 0.07227730563424614, 0.1504429473237494, 0.0882073299806854, nan, 0.05805899778862603, nan, 0.005208107287010113, 0.0, 0.03712186277316538, 0.011537834466389374, 0.0, 0.0005016827274817617, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.011682537019170268, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 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, 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, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan]
4.3915 3.0 60 4.3693 0.0244 0.0669 0.3036 [0.29231266622746016, 0.21563560110043029, 0.8273355650108397, 0.063226580092392, 0.17128934854539207, 0.19105043457218335, 0.11620672799182331, 0.0, 0.0012163230554035152, 0.0, 0.0007073993976999414, 0.0, 0.05575582792597933, 0.0005351095477763017, 0.0, 0.038575870434277515, 0.0, 0.0, 0.0, 0.0012064666613045927, 0.001256548551102863, nan, 0.0, 0.0001519741441322783, 0.0, 0.0, 0.0, nan, 0.00017071997268480437, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan] [0.8113516149350165, 0.2751605908105233, 0.899376120675382, 0.5995110519615785, 0.18636882278879446, 0.37692960585645324, 0.12509642350999517, nan, 0.001251721116535236, nan, 0.000759515646022308, 0.0, 0.07748830995323981, 0.0005421985963502072, 0.0, 0.04719998327724242, 0.0, 0.0, 0.0, 0.0026507620941020544, 0.005052467936261173, nan, 0.0, 0.00015730525609295693, 0.0, 0.0, nan, nan, 0.0007909117054932413, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 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, 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, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan]
3.8635 4.0 80 3.9546 0.0304 0.0734 0.3193 [0.2674083612941915, 0.21287374230057524, 0.8283058439708967, 0.08462994757938237, 0.1913816737548768, 0.180297325836609, 0.1539821016491248, 0.0, 0.003791982665222102, 0.0, 0.0, 0.0, 0.08181324225723823, 0.0, 0.0, 0.034338444650269286, 0.0, 0.0, 0.0, 0.0, 8.670394936489357e-05, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan] [0.8093706189511897, 0.2978486968618305, 0.9015426129658399, 0.7779336882305289, 0.21423512747875353, 0.43249694976393827, 0.16359407047736366, nan, 0.003942921517085993, nan, 0.0, 0.0, 0.10058211661418075, 0.0, 0.0, 0.04184870085077029, 0.0, 0.0, 0.0, 0.0, 0.00015546055188495918, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 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, 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, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan]
3.6339 5.0 100 3.7226 0.0346 0.0840 0.3509 [0.2843907989262714, 0.2811571185479297, 0.8261793546524662, 0.10319984854402713, 0.2626968685899411, 0.1830228622117734, 0.10298402778173772, 0.0, 0.010589838427097528, 0.0, 0.0, 0.0, 0.044127281235376695, 4.4784976133339805e-05, 0.0, 0.03927989772866575, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.002168089475384888, 0.0, 0.0, 0.0, nan, 0.037383098686653835, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan] [0.7846166349481555, 0.4056135182684833, 0.9477012283835149, 0.8752893183659298, 0.29351589549889834, 0.44601082170707124, 0.10799433119457517, nan, 0.011348938123252807, nan, 0.0, 0.0, 0.04499475140757706, 4.5499182910506896e-05, 0.0, 0.04688643157256631, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.002181299551155669, 0.0, 0.0, nan, nan, 0.31844981305723324, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 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, 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, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan]
3.8143 6.0 120 3.6413 0.0398 0.0977 0.3591 [0.2894843214596141, 0.2699065234482688, 0.8215728166272065, 0.09918787445285969, 0.4665258267200923, 0.22122580365360614, 0.18275256534691794, 0.0, 0.01323316145665485, 0.0, 0.0, 0.0, 0.06081981526584256, 0.00021179870046368786, 0.0, 0.022314846835696473, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.003557048380030459, 0.0, 0.0, 0.0, nan, 0.05472403035088395, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan] [0.7420980271311792, 0.34609876846282883, 0.9767075658138085, 0.9067092929059136, 0.5886547843877872, 0.6088403798206992, 0.19478445981905268, nan, 0.013706346226060834, nan, 0.0, 0.0, 0.06315010974329612, 0.0002123295202490322, 0.0, 0.025627625995526662, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.003576072821846554, 0.0, 0.0, nan, nan, 0.5107851596203624, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 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, 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, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan]
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1.8352 49.0 980 2.2442 0.0779 0.1475 0.5511 [0.38663808534880645, 0.6743764529203783, 0.8854065475431177, 0.27776441648417954, 0.32840568652311974, 0.37305948894861407, 0.5307166226839538, nan, 0.1749566223250434, 0.0, 0.0, 0.0, 0.42489548796309645, 0.015722900928287955, 0.0, 0.0049191708816820395, 0.0, 0.0, 0.0, 0.0047188360204482895, 0.0, nan, 0.0, 0.0007223542467101475, 0.0, 0.0, 0.0, nan, 0.14045876707779342, 0.0, 0.06315698776494916, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 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, 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, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan] [0.881363737953174, 0.8275209688637944, 0.9536018374672824, 0.9523058673764611, 0.5757888731507712, 0.6166781603097979, 0.9127554101811267, nan, 0.2398089039095423, nan, 0.0, 0.0, 0.5625536787861437, 0.015727550892731885, 0.0, 0.005184054850644871, 0.0, 0.0, 0.0, 0.008482438701126574, 0.0, nan, 0.0, 0.0007236041780276019, 0.0, 0.0, nan, nan, 0.8818665516249641, nan, 0.0865867343925344, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 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, 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, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan]
2.2328 50.0 1000 2.1858 0.0790 0.1487 0.5585 [0.39946083955868833, 0.6931863563152424, 0.8856013192534284, 0.2809612853485767, 0.3286335700053321, 0.38266719751692085, 0.5326348118588471, nan, 0.20548785708170428, 0.0, 0.0, 0.0, 0.4152002040295843, 0.01700425106276569, 0.0, 0.0007944862653186883, 0.0, 0.0, 0.0, 0.0010346076250581967, 0.0, nan, 0.0, 0.00684602700841143, 0.0, 0.0, 0.0, nan, 0.1402944564188534, 0.0, 0.05702028778264367, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 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, 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, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan] [0.8725965608555194, 0.8579027044719938, 0.9515903462512638, 0.9541864367550053, 0.575926581680831, 0.6245822502784998, 0.9146689309996352, nan, 0.30043392998706553, nan, 0.0, 0.0, 0.5437541750167001, 0.01701669440852958, 0.0, 0.0008361378791362696, 0.0, 0.0, 0.0, 0.0017671747294013696, 0.0, nan, 0.0, 0.0068794831997986495, 0.0, 0.0, nan, nan, 0.8845268909979868, nan, 0.07536471561041876, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, 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, 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, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan]

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3
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Finetuned from

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

Evaluation results

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