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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: 2.6836
  • Mean Iou: 0.0785
  • Mean Accuracy: 0.1421
  • Overall Accuracy: 0.5276
  • Per Category Iou: [0.5399621456272898, 0.4230438128529876, 0.8931988601700478, 0.5700974052454356, 0.6161933963343478, 0.5518067110797286, 0.672799915550537, 0.0, 0.13224736001488627, 0.07285965352749184, 0.07873415624223346, 0.06589184516376909, 0.0, 0.0, 0.0, 0.0, 0.0, 0.10923639972706407, 0.018405220760681146, 0.04396316016693049, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan]
  • Per Category Accuracy: [0.9092699968756467, 0.9865203285004562, 0.9656447761692465, 0.8933310207267366, 0.7559688399185908, 0.7905802456103311, 0.709918047173505, 0.0, 0.2928290614431149, 1.0, 0.133178257640514, 0.1504056128042096, 0.0, nan, 0.0, 0.0, 0.0, 0.153991896697467, 0.02513052558301427, 0.05050212836302021, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 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: 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
4.812 1.0 20 4.9216 0.0108 0.0375 0.1423 [0.11659967720641855, 0.23593711431627512, 0.4006311941130212, 0.11502801657635418, 0.07316850652312745, 0.03936552970718956, 0.27709561076055306, 0.0029129956733446615, 0.0, 0.04997225305216426, 0.0, 0.0, 0.0009024107257960552, 0.0, 0.0, 0.0, 0.0, 0.00290388430464909, 0.00012582233885753317, 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, nan, 0.0, 0.0, 0.0, nan, 0.007426202116467604, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.00015842420721886305, 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, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.00032397189543807074, 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, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0] [0.13689536662460286, 0.3924921423501977, 0.6422548381171834, 0.18205843287431137, 0.09005544248719208, 0.060540483479362396, 0.2961474070773759, 0.003968331161125917, 0.0, 0.17731613665452398, 0.0, 0.0, 0.033593281343731254, nan, 0.0, 0.0, 0.0, 0.0029440055965254904, 0.0004872955099199443, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.03879101341522547, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.00020564142989340918, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.003657142857142857, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan]
4.4984 2.0 40 4.6763 0.0242 0.0713 0.3436 [0.4177555671956623, 0.3442832044316342, 0.6129024792432088, 0.22369935292322102, 0.3479776313773556, 0.05689757109933999, 0.2765786290383097, 0.0008884184261762093, 0.0, 0.0031787713357749985, 0.0, 0.0, 0.004341718809858256, 0.0, 0.0, 0.0, 0.0, 0.009323628036313078, 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, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 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, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan] [0.7184146138583329, 0.8374480381222752, 0.8843944630900499, 0.5385482612089151, 0.5100708821671697, 0.08296386220486295, 0.3110123266771977, 0.0009142723753574416, 0.0, 0.009057792655311607, 0.0, 0.0, 0.020395920815836834, nan, 0.0, 0.0, 0.0, 0.009968811029818987, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan]
4.5323 3.0 60 4.2979 0.0370 0.0847 0.4190 [0.41879151861578867, 0.40886899708008034, 0.7798855206331494, 0.2990302618524624, 0.40161380454845647, 0.06021682613706414, 0.48480536921001444, 0.0011262792007301395, 0.0, 0.007760479041916167, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02717957844956904, 0.0, 0.0006034517439755401, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 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, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan] [0.8799599108950663, 0.800724931562405, 0.9452737564494315, 0.7071144603334718, 0.6657730367043301, 0.07905802456103311, 0.532494228198793, 0.0011282510163985449, 0.0, 0.01594959141478783, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.031159821610749993, 0.0, 0.0006199115592842088, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan]
3.7103 4.0 80 4.0054 0.0528 0.1050 0.4586 [0.45956776399352367, 0.5197583595464864, 0.7120890080094743, 0.3102372932301349, 0.5014312977099237, 0.038685302529019236, 0.6536122265975282, 0.0010680233800027185, 0.0, 0.22662612281989444, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.00384401395984017, 0.0, 0.006680887028615125, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 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, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] [0.8764683446202288, 0.8426746426036703, 0.9537978593731737, 0.8668303748704864, 0.855610920064566, 0.04855026566733523, 0.6790270970877719, 0.0010698932052055168, 0.0, 0.6383774736634833, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.00443058267991955, 0.0, 0.006860354589411911, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan]
3.5864 5.0 100 3.6505 0.0501 0.0990 0.4628 [0.40192794658822223, 0.37222233525258897, 0.765344539597529, 0.4024280432121029, 0.4648372385595219, 0.019827651948448106, 0.5725238448200985, 0.0, 0.0, 0.14450347683412768, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.007402422611036339, 0.0, 0.007776556329139693, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 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, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] [0.9818442611310159, 0.9274612186961371, 0.9640000667902285, 0.7486363989063806, 0.8297845462839497, 0.02287202223864316, 0.5918155183820055, 0.0, 0.0, 0.36418233730432215, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.007695222549333955, 0.0, 0.007893540521552259, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan]
3.3341 6.0 120 3.5537 0.0578 0.1092 0.4975 [0.43842804678092534, 0.5593515133048954, 0.6558938538150463, 0.35659964836230806, 0.4840409979216551, 0.04212073850457818, 0.8206441623792398, 0.0, 0.0, 0.15763715978363527, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0023493217731785764, 0.0, 0.008642228138439484, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] [0.9477238883185705, 0.8934198519720167, 0.9830686770525472, 0.8590983572885905, 0.8597375254403817, 0.051919490481719975, 0.858722507999514, 0.0, 0.0, 0.5422861081027862, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.002448479902060804, 0.0, 0.008720089267264537, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan]
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2.0933 49.0 980 2.6558 0.0805 0.1401 0.5269 [0.5154490856937971, 0.4541122913505311, 0.8921043505067242, 0.5739374574160656, 0.6159028238332926, 0.544718983261163, 0.6631041592514566, 0.0, 0.11673884238761593, 0.06969738557606532, 0.07422505686633067, 0.07410361107566671, 0.0, nan, 0.0, 0.0, 0.0, 0.09939109476087783, 0.007983682983682983, 0.05094509450945094, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] [0.9333052006281167, 0.9861046334786576, 0.9664128637980264, 0.8920027933853365, 0.7429995087374552, 0.7491730884267568, 0.69799978397937, 0.0, 0.24805576061391613, 1.0, 0.11660103975449462, 0.16977271066286634, 0.0, nan, 0.0, 0.0, 0.0, 0.13702742880461713, 0.009537069265576053, 0.0584783237591437, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan]
1.4343 50.0 1000 2.6836 0.0785 0.1421 0.5276 [0.5399621456272898, 0.4230438128529876, 0.8931988601700478, 0.5700974052454356, 0.6161933963343478, 0.5518067110797286, 0.672799915550537, 0.0, 0.13224736001488627, 0.07285965352749184, 0.07873415624223346, 0.06589184516376909, 0.0, 0.0, 0.0, 0.0, 0.0, 0.10923639972706407, 0.018405220760681146, 0.04396316016693049, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] [0.9092699968756467, 0.9865203285004562, 0.9656447761692465, 0.8933310207267366, 0.7559688399185908, 0.7905802456103311, 0.709918047173505, 0.0, 0.2928290614431149, 1.0, 0.133178257640514, 0.1504056128042096, 0.0, nan, 0.0, 0.0, 0.0, 0.153991896697467, 0.02513052558301427, 0.05050212836302021, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan]

Framework versions

  • Transformers 4.40.1
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.0
  • Tokenizers 0.19.1
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Safetensors
Model size
3.75M params
Tensor type
F32
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Finetuned from

Dataset used to train gost-sniper/segformer-b0-scene-parse-150

Evaluation results

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