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segformer-b0-scene-parse-150

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PyTorch
segformer
generated_from_trainer
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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: 3.0184
  • Mean Iou: 0.0640
  • Mean Accuracy: 0.1277
  • Overall Accuracy: 0.4046
  • Per Category Iou: [0.34201525710959674, 0.025996616878625384, 0.7898521891625035, 0.4880628111048391, 0.6304014883644438, 0.2600362857635349, 0.0, 0.0, 0.20877671078813045, 0.0, 0.0, 0.0, 0.5262114201454212, 0.0, 0.0, 0.058912017109042966, 0.03817073170731707, 0.0, 0.0, 0.02204585537918871, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0]
  • Per Category Accuracy: [0.7327903499042605, 0.8452460476508573, 0.9948029771516602, 0.9114927547343402, 0.7048267540079297, 0.27804903954590465, nan, nan, 0.282045288531775, 0.0, nan, 0.0, 0.6049924003273706, 0.0, 0.0, 0.33099129372231556, 0.03817073170731707, 0.0, 0.0, 0.022592176078399872, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 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, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0]

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
4.9072 1.0 20 4.9606 0.0050 0.0344 0.0694 [0.1906469231131409, 0.00029699510831586305, 0.2444145404639474, 0.029686890090893883, 0.0, 0.08313867556924476, 0.0, 0.0, 0.05966918425448813, 0.0, 0.0, 7.385673136963951e-05, 0.014832859204631723, 0.00032013218361129754, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0005753556017260668, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.013377071782163508, 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, 0.0, 0.0, 0.0, 0.0, nan, 7.705703761924576e-05, 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, 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, nan, nan, 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, nan, 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, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 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] [0.223437129251025, 0.0037853484747272324, 0.2545241939974386, 0.03265353062945641, 0.0, 0.1005126378827212, nan, nan, 0.07409788166544923, 0.0, nan, 9.237487403426268e-05, 0.01732335632721462, 0.00032807010117258606, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0007229496345087958, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.8424401197604791, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 7.803233660028715e-05, 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, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0]
4.7934 2.0 40 4.7759 0.0165 0.0565 0.1851 [0.311921114599686, 0.0, 0.7675367243410455, 0.1272097800078871, 0.18671618940235565, 0.05900296147856205, 0.0, 0.0, 0.03291417217850514, 0.0, 0.0, 0.0005156578819004084, 0.08754368486803611, 0.0, 0.0, 0.0, 0.01352250297167908, 0.0, 0.0, 0.00017418264792461375, 0.0, 0.0, 0.0, 0.0020930927937805243, 0.12920699118879098, nan, 0.0, 0.0, 0.0, nan, 0.014315345642490394, 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, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 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, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, 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, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0] [0.5262800668796662, 0.0, 0.8888208326373035, 0.20156405734876945, 0.1941159570189048, 0.08296548988000962, nan, nan, 0.03678597516435354, 0.0, nan, 0.000621430970775949, 0.10787637865856035, 0.0, 0.0, 0.0, 0.013747228381374724, 0.0, 0.0, 0.00020081934291910997, nan, 0.0, 0.0, 0.015173410404624277, 0.16057804505879186, nan, nan, nan, nan, nan, 0.31511976047904194, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 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, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0]
4.1223 3.0 60 4.5469 0.0218 0.0677 0.2787 [0.3408503958804526, 0.002009036875303251, 0.6807422606605685, 0.18752806062627195, 0.3057785044715604, 0.1379944821120929, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1869221504831071, 0.0, 4.973639709539441e-05, 0.0, 0.0010866554449960582, 0.0, 0.0, 0.000533843859875191, 0.0, 0.0, 0.0, 0.0, 0.0031025243266111975, nan, nan, 0.0, 0.0, nan, 0.007676746719228176, 0.0, nan, 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, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 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, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0] [0.5979218743964552, 0.05900690269427744, 0.9883252593871226, 0.6100513685501802, 0.31808308912256505, 0.20968795004201693, nan, nan, 0.0, 0.0, nan, 0.0, 0.20111851592033983, 0.0, 0.00025097251850922326, 0.0, 0.001130820399113082, 0.0, 0.0, 0.0005823760944654189, nan, 0.0, 0.0, 0.0, 0.003949376178080962, nan, nan, nan, nan, nan, 0.05538922155688623, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 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, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0]
4.2254 4.0 80 4.4098 0.0305 0.0732 0.2883 [0.32455661784828344, 0.0033069343870300335, 0.660569093180853, 0.18995896693217476, 0.6200845689061766, 0.12972526282995656, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.23372471147627594, 0.0, 0.0, 0.0, 0.00011024993660628645, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0028989929813854134, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 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, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0] [0.5071818387493723, 0.08684034736138944, 0.9959908680884236, 0.6426090623322855, 0.6522093891857725, 0.153494590904973, nan, nan, 0.0, 0.0, nan, 0.0, 0.25, 0.0, 0.0, 0.0, 0.00011086474501108647, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.003410824881069922, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 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, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0]
3.8409 5.0 100 4.2706 0.0389 0.0802 0.2838 [0.25873246598207084, 0.0034625138510871426, 0.6751214484574631, 0.2442992068461699, 0.631843704839546, 0.15087493040055966, 0.0, 0.0, 0.004839477442217168, 0.0, 0.0, 0.0, 0.27329638199788336, 0.0, 0.0, 0.0, 0.0102769339925944, 0.0, 0.0, 0.0, 0.0, 0.00015803851624410177, 0.0, 0.0, 0.04321093082835183, nan, nan, 0.0, 0.0, nan, 0.0018140589569160999, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0] [0.5166454218881917, 0.3736361612113115, 0.9969560294745439, 0.5025262592961742, 0.6582313394242372, 0.19807940420294337, nan, nan, 0.005346968590211833, 0.0, nan, 0.0, 0.2968938773919482, 0.0, 0.0, 0.0, 0.010277161862527716, 0.0, 0.0, 0.0, nan, 0.002006305531670966, 0.0, 0.0, 0.04541782604793106, nan, nan, nan, nan, nan, 0.004491017964071856, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 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, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0]
3.997 6.0 120 4.1447 0.0401 0.0943 0.3500 [0.35152747608263624, 0.016774080336917335, 0.5529587321189667, 0.32007359866945373, 0.5501930818262755, 0.16381871121437605, 0.0, 0.0, 0.009591241206143823, 0.0, 0.0, 0.0, 0.268283336982702, 0.0, 0.0013210330478434136, 2.2084805653710246e-05, 0.00660687957964283, 0.0, 0.0, 0.0, 0.0, 0.0001277791975466394, 0.0, 0.0, 0.08257600523046747, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0] [0.6925190789146953, 0.4682698730794923, 0.9972901238005085, 0.8675803112780802, 0.578813997586623, 0.2088038761796763, nan, nan, 0.012388604821037253, 0.0, nan, 0.0, 0.2865076581316497, 0.0, 0.007529175555276697, 5.09139045873428e-05, 0.006607538802660754, 0.0, 0.0, 0.0, nan, 0.002006305531670966, 0.0, 0.0, 0.11336504802082398, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 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, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0]
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2.2675 49.0 980 3.0380 0.0615 0.1255 0.3992 [0.3405181616374155, 0.024026345584923118, 0.7961477321685906, 0.4840008936913515, 0.5998782256025511, 0.25116329624102557, 0.0, 0.0, 0.18894164772599412, 0.0, 0.0, 0.0, 0.5321955525663121, 0.0, 0.0, 0.05612633761165818, 0.02516629711751663, 0.0, 0.0, 0.025031240237425804, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 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, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0] [0.7304865330176966, 0.8788688488087286, 0.9946637711825083, 0.9134899946331365, 0.6680285008331897, 0.2670933963118813, nan, nan, 0.24531775018261504, 0.0, nan, 0.0, 0.6167621497330371, 0.0, 0.0, 0.2811974950358943, 0.02516629711751663, 0.0, 0.0, 0.025745039762229898, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 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, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0]
1.8612 50.0 1000 3.0184 0.0640 0.1277 0.4046 [0.34201525710959674, 0.025996616878625384, 0.7898521891625035, 0.4880628111048391, 0.6304014883644438, 0.2600362857635349, 0.0, 0.0, 0.20877671078813045, 0.0, 0.0, 0.0, 0.5262114201454212, 0.0, 0.0, 0.058912017109042966, 0.03817073170731707, 0.0, 0.0, 0.02204585537918871, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0] [0.7327903499042605, 0.8452460476508573, 0.9948029771516602, 0.9114927547343402, 0.7048267540079297, 0.27804903954590465, nan, nan, 0.282045288531775, 0.0, nan, 0.0, 0.6049924003273706, 0.0, 0.0, 0.33099129372231556, 0.03817073170731707, 0.0, 0.0, 0.022592176078399872, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 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, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 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, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0]

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.5
  • Tokenizers 0.13.3
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Finetuned from

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

Evaluation results

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