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segformer-b0-scene-parse-150_epoch_900_230609

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PyTorch
segformer
generated_from_trainer
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segformer-b0-scene-parse-150_epoch_900_230609

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: 3.4853
  • Mean Iou: 0.0669
  • Mean Accuracy: 0.1929
  • Overall Accuracy: 0.4365
  • Per Category Iou: [0.41420435726910076, 0.19922418699867508, 0.9114084098598356, 0.31411144995841417, 0.17876560594261492, 0.6577098024114943, 0.0, 0.31084779968459025, 0.3282288355966484, 0.0, 0.0, 0.3181820454988723, 0.46052157525704623, 0.0, 0.013076710562666911, 0.010474772205255055, 0.12349715714059392, 0.4422356346339384, 0.004824618594340853, 0.17492076826148384, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.00854020089384486, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.1399318617361062, nan, 0.0, 0.005856997137310388, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0030559050871831746, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 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, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
  • Per Category Accuracy: [0.7324458311845484, 0.2116409644063921, 0.949747164413643, 0.8669722873293809, 0.6705212334613743, 0.9198263240268592, 0.0, 0.6028946443220439, 0.504714419706768, 0.0, 0.0, 0.8396104410250996, 0.836917652984135, nan, 0.02453351762266759, 0.03516651131260063, 0.1368339444531128, 0.8326996197718631, 0.005648184269891616, 0.49425473377569185, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.02906845394984562, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.16436015006252605, nan, 0.0, 0.006742168857240256, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.007042837020465656, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 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, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 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: 900

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Per Category Iou Per Category Accuracy
3.5564 10.0 100 3.6786 0.0763 0.1532 0.4008 [0.3237571005486236, 0.1819869012987426, 0.8388003674607197, 0.2046329426484266, 0.2064952087487558, 0.5049551654117774, 0.0, 0.28120722142592564, 0.005168448609106256, 0.0, 0.0, 0.36343356575357355, 0.2727039287854147, 0.0, 0.00034180730613116855, 0.0, 0.3289629240769431, 0.2608504559938468, 0.0, 0.04142997803529501, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 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, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] [0.691248765798428, 0.23268209649145094, 0.8535326470173459, 0.8523852199090032, 0.8383882842509603, 0.9458019891957389, 0.0, 0.40925817414742766, 0.0059591393451293734, 0.0, 0.0, 0.8384755469925308, 0.6418534374213044, nan, 0.0003455425017277125, 0.0, 0.3758240365111562, 0.3008871989860583, 0.0, 0.0590171009332686, 0.0, 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, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 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, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
2.8466 20.0 200 3.3233 0.0749 0.1449 0.3714 [0.30315342230959896, 0.22056613719150006, 0.8587456682436991, 0.1970055610153647, 0.12114191994259649, 0.5098158231689757, 0.0, 0.23231994986276092, 0.0042415031776733905, 0.0, 0.0, 0.34624794805575015, 0.3322935779816514, nan, 0.0, 0.0, 0.040977845553562986, 0.23341790503805726, 0.0, 0.1211531443863277, 0.0, 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, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] [0.6297835838881302, 0.2849063794110922, 0.9076092431049418, 0.9065352268027024, 0.47287131882202305, 0.9682519647935915, 0.0, 0.4691784835345131, 0.004330072849596965, 0.0, 0.0, 0.8740135659426219, 0.6384789725509947, nan, 0.0, 0.0, 0.04690669371196755, 0.29928179129700044, 0.0, 0.16119113125101148, 0.0, 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, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 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, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
1.6728 30.0 300 3.1809 0.0752 0.1579 0.3684 [0.2784787110327984, 0.2357294967018127, 0.854388001071333, 0.22850119095923574, 0.12023585373104746, 0.4615369313886966, 0.0, 0.21540863687891687, 0.031175907117672236, 0.0, 0.0, 0.3227431805207377, 0.4472786060714927, 0.0, 0.0, 0.0, 0.0, 0.36917152999217856, 0.0, 0.2709572901325479, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] [0.5516913420597755, 0.27673077411651126, 0.9254386848520257, 0.9278367572039157, 0.48547535211267606, 0.9761784554282156, 0.0, 0.59012070784015, 0.03236271059739491, 0.0, 0.0, 0.8579666921797884, 0.627046084109796, nan, 0.0, 0.0, 0.0, 0.5184621884241656, 0.0, 0.4962507417597238, 0.0, 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, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 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, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
1.4223 40.0 400 3.0832 0.0830 0.1784 0.4031 [0.31830718923273776, 0.23510948587349267, 0.8612539745336791, 0.3269989049786643, 0.12847868753459815, 0.46652837563999566, 0.0, 0.2665767615530096, 0.19205171500055462, 0.0026236780179059263, 0.0, 0.29266868335299406, 0.45844117429564446, 0.0, 0.0, 0.0, 0.0, 0.5318154954549292, 0.0, 0.23473278085975846, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 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, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] [0.6092122619383897, 0.27441884098473107, 0.9473255837379374, 0.9181442161864056, 0.5107634443021767, 0.9821780178724693, 0.0, 0.7714168522207899, 0.21978293217864725, 0.002633507995547471, 0.0, 0.9920293488875399, 0.6244774615965751, nan, 0.0, 0.0, 0.0, 0.7983523447401775, 0.0, 0.5546744349139558, 0.0, 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, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 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, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
1.1878 50.0 500 3.0045 0.0892 0.1807 0.4012 [0.2977591245271764, 0.20317578834714653, 0.882798761979787, 0.2607702791743012, 0.1681775712734527, 0.5410172664069066, 0.0, 0.19395998096194345, 0.40091726756185186, 0.004298246803433174, 0.0, 0.31899822811810846, 0.5175554819476648, 0.0, 0.0, 0.0, 0.0013351475465600816, 0.4831250449758929, 0.0, 0.18647497084097023, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 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, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] [0.5381481663846251, 0.2073981860216966, 0.952736869673832, 0.7966634496070591, 0.7106274007682458, 0.9533834839534845, 0.0, 0.6972694245868979, 0.5548276080931459, 0.004303206363858497, 0.0, 0.9907097046636227, 0.6295643414756988, nan, 0.0, 0.0, 0.0015310500858168202, 0.8509083227714407, 0.0, 0.42261423099746454, 0.0, 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, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 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, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
1.0422 60.0 600 2.9537 0.0883 0.1746 0.3912 [0.2950262693914258, 0.20375533779515217, 0.8653710668646557, 0.2939437241666827, 0.14325114160428354, 0.5361965079972557, 0.0, 0.2410961368164711, 0.26736489393842294, 0.030339526638429166, 0.0, 0.36825240568011225, 0.5217141594875193, 0.0, 0.0, 0.01824134705332086, 0.0, 0.4934554658310233, 0.0, 0.09229012717642955, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.04364343958487769, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 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, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] [0.5168978716015725, 0.2179619420238307, 0.9650865281946934, 0.7388046325658348, 0.6740556978233034, 0.9667121051480116, 0.0, 0.8168053439587484, 0.4119069951128005, 0.030434664567100155, 0.0, 0.9873050225659162, 0.5947620246789221, nan, 0.0, 0.019828828065418184, 0.0, 0.8648500211237854, 0.0, 0.15698333063602524, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.07093907673770615, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 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, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
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Framework versions

  • Transformers 4.29.2
  • Pytorch 2.0.1
  • Datasets 2.12.0
  • Tokenizers 0.13.3
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Dataset used to train manadopeee/segformer-b0-scene-parse-150_epoch_900_230609

Evaluation results

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