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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: 2.9382
  • Mean Iou: 0.0595
  • Mean Accuracy: 0.1203
  • Overall Accuracy: 0.4686
  • Per Category Iou: [0.5449896578964825, 0.22916774507469487, 0.1744884326629391, 0.5647278781264079, 0.3732519685640748, 0.5348024858325797, 0.054881166556197326, 0.0, 0.0, 0.0, 0.3451998606972875, 0.11326187607027335, 0.0, 0.0, 0.0008800880088008801, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0014867070895522388, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.03555152529761905, nan, 0.0, 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, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 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, 0.0]
  • Per Category Accuracy: [0.8719045838810434, 0.34500709153535636, 0.7900525479652939, 0.8814744893929878, 0.962967779564299, 0.6949321574314162, 0.25664187035069075, nan, 0.0, 0.0, 0.6782998783454988, 0.12399838909025261, 0.0, 0.0, 0.0008800880088008801, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0014867070895522388, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.048450472146523864, nan, 0.0, 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, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 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, 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
4.7339 1.0 20 4.9135 0.0096 0.0398 0.1564 [0.2071901651658529, 0.05889032032665895, 0.0, 0.15819040753957142, 0.35795928593494214, 0.15672264335715064, 0.0, 0.0, 9.264549202476723e-05, 0.034904697542272364, 0.010126209197797918, 0.0, 0.0, 0.0, 0.03848226496052384, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.04595173026900662, 0.0, 0.00034256211997014816, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.05131798664122137, 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, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.00029958776723228837, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.00217542638357118, 0.0, nan, 0.0, nan, nan, 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, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 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, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0] [0.2182076916583254, 0.06305232150371722, 0.0, 0.19972246144357655, 0.5799402566285192, 0.18634739060823813, 0.0, nan, 0.0002318213430183139, 0.06551588452139337, 0.024381589618815896, 0.0, 0.0, 0.0, 0.37010145458990346, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.10170825559701492, nan, 0.0003468952871797413, nan, 0.0, nan, 0.0, nan, nan, nan, 0.054534507890233855, nan, 0.0, 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, nan, nan, nan, 0.0, 0.0012982966348151226, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0029958058717795086, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 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, 0.0]
4.355 2.0 40 4.6446 0.0203 0.0739 0.3276 [0.3449216245930818, 0.20657006793508925, 0.0, 0.3129348209777524, 0.37094051998353716, 0.3193524854969257, 0.035330916328996945, nan, 0.0, 0.0, 0.015929334368185683, 6.748731238527157e-05, 0.0, 0.0, 0.04629699518983346, 0.0, 0.0, 0.0, 0.0, 0.00032686605496019236, 0.0, 0.0, 0.0, 0.0, 0.06844852593287779, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0013573232323232324, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 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, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0] [0.46460365890425825, 0.2852979224139275, 0.0, 0.5267553937625278, 0.9163671616381349, 0.7433734225322501, 0.044013460857244065, nan, 0.0, 0.0, 0.03277068126520681, 6.943576497382272e-05, 0.0, 0.0, 0.32988632196552986, 0.0, 0.0, 0.0, nan, 0.00039302658544117236, 0.0, 0.0, nan, nan, 0.12862931436567165, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0013625705051017174, nan, 0.0, 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, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 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, 0.0]
4.43 3.0 60 4.1705 0.0306 0.0831 0.4092 [0.4797008420303605, 0.24851512342500084, 0.05724783547158829, 0.37758830414197647, 0.36551791728291666, 0.3661109374022888, 0.019021937176590627, nan, 0.0, 0.0, 0.008195370086899183, 0.00034105946712868854, 0.0, 0.0, 0.012745948335969252, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.08777272376840645, nan, 0.0, 0.0, 0.0, nan, 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, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 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, 0.0, nan, nan, nan, 0.0] [0.7804705683723062, 0.45127102133694924, 0.09615462951647726, 0.6175383307823353, 0.9700416154355433, 0.7890007169695369, 0.023113708820403825, nan, 0.0, 0.0, 0.008819951338199513, 0.00034717882486911357, 0.0, 0.0, 0.03664588681090331, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.13275419776119404, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 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, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 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, 0.0]
3.7634 4.0 80 3.9478 0.0428 0.0910 0.4298 [0.5494059153206581, 0.2832034378239203, 0.049460463893502865, 0.4490736296438514, 0.33416510652625175, 0.4259513551264706, 0.047468870643249415, nan, 0.0, 0.0, 0.0, 0.01606017762281918, 0.0, 0.0, 0.05731780904577745, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.014588505652789461, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 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, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 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, 0.0, nan, nan, nan, 0.0] [0.8254121392650836, 0.7303034553700847, 0.08430078618273656, 0.6685144165861253, 0.969998266023519, 0.6805765944486073, 0.07731137088204039, nan, 0.0, 0.0, 0.0, 0.016247969003874516, 0.0, 0.0, 0.20435154626573768, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.02072644589552239, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 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, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 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, 0.0]
3.9882 5.0 100 3.6562 0.0398 0.0819 0.4212 [0.48804932686779273, 0.191044788551723, 0.05292868115633108, 0.42569780098144117, 0.3245562653199613, 0.5062213641030356, 0.0009389671361502347, nan, 0.0, 0.0, 0.0, 0.01955241460541814, 0.0, 0.0, 0.006847294791842238, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.05408647774915391, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 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, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 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, 0.0, nan, nan, nan, 0.0] [0.8680816680937278, 0.3574679322329764, 0.14666585196953033, 0.6942337416183733, 0.9695017182130584, 0.7123442747556643, 0.0009741409847679773, nan, 0.0, 0.0, 0.0, 0.01959477287561277, 0.0, 0.0, 0.015890477936682558, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.06242712220149254, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 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, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 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, 0.0]
3.3745 6.0 120 3.4523 0.0372 0.0822 0.4051 [0.4362211359263539, 0.18428022888746368, 0.0770453293117949, 0.3697803157788261, 0.33396150929264873, 0.48734096790689746, 0.0, nan, 0.0, 0.0, 0.0, 0.025214279770325372, 0.0, 0.0, 0.007562561074053618, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.010736712267324512, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 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, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 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, 0.0, nan, nan, nan, 0.0] [0.7666646567214578, 0.34482785492744816, 0.2705405515499613, 0.6049061897115179, 0.9770169299158233, 0.8476574502838229, 0.0, nan, 0.0, 0.0, 0.0, 0.02524684414448194, 0.0, 0.0, 0.013054638797213055, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.011164878731343284, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 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, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 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, 0.0]
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1.6721 49.0 980 2.9800 0.0598 0.1200 0.4688 [0.5447235923116732, 0.22935933819787968, 0.17353781882146, 0.5705002885015691, 0.36564098600885186, 0.5398893776765983, 0.06269893176367997, 0.0, 0.0, 0.0, 0.34475995011950533, 0.11444038776281003, 0.0, 0.0, 0.0020779855763354113, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0030025652985074627, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.03896587691849203, nan, 0.0, 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, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 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, 0.0] [0.8707733867174781, 0.34072879163354686, 0.7715996578272027, 0.8812375662350653, 0.9695411267694442, 0.6993040543818698, 0.2546935883811548, nan, 0.0, 0.0, 0.6726733576642335, 0.1262342207224097, 0.0, 0.0, 0.0020779855763354113, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0030025652985074627, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.04971797959313011, nan, 0.0, 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, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 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, 0.0]
1.6237 50.0 1000 2.9382 0.0595 0.1203 0.4686 [0.5449896578964825, 0.22916774507469487, 0.1744884326629391, 0.5647278781264079, 0.3732519685640748, 0.5348024858325797, 0.054881166556197326, 0.0, 0.0, 0.0, 0.3451998606972875, 0.11326187607027335, 0.0, 0.0, 0.0008800880088008801, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0014867070895522388, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.03555152529761905, nan, 0.0, 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, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 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, 0.0] [0.8719045838810434, 0.34500709153535636, 0.7900525479652939, 0.8814744893929878, 0.962967779564299, 0.6949321574314162, 0.25664187035069075, nan, 0.0, 0.0, 0.6782998783454988, 0.12399838909025261, 0.0, 0.0, 0.0008800880088008801, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0014867070895522388, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.048450472146523864, nan, 0.0, 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, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 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, 0.0]

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu117
  • Datasets 2.13.1
  • Tokenizers 0.13.3
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Finetuned from

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

Evaluation results

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