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MF21377197
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segformer-b0-finetuned

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End of training

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  1. README.md +21 -14
  2. model.safetensors +1 -1
  3. runs/Apr13_18-15-26_b1fdc3b2e8ea/events.out.tfevents.1713032133.b1fdc3b2e8ea.3243.1 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -15,12 +15,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.9804
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- - Mean Iou: 0.0270
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- - Mean Accuracy: 0.0832
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- - Overall Accuracy: 0.5068
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- - Per Category Iou: [0.8021452931267776, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.00757909387963703, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.000748031223377361, 0.0, 0.15502171352863242, nan, 0.0, nan, 0.056221004367996964, nan, 0.0, nan, 0.0, nan, 0.040391599317767694, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.07047672462142457, 0.0, nan, nan, 0.05062781753814023, nan, 0.0, 0.03166986564299424, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.08493484197676761, 0.0, nan, 0.04267983360952349, nan, 0.0003925481280361144, 0.0, nan, nan, 0.00012044806680852773, 0.0, 0.0, 0.002362410370862814, nan, nan, nan, nan, 0.0022562095679258846, 0.0, 0.0]
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- - Per Category Accuracy: [0.8694985349631393, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.008096131396611705, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0010361203050798676, nan, 0.8231816690745898, nan, 0.0, nan, 0.11201351351351352, nan, 0.0, nan, nan, nan, 0.07139561448697561, nan, nan, nan, nan, nan, nan, nan, 0.10334561484308179, 0.0, nan, nan, 0.12090652522991582, nan, 0.0, 0.037541019470575365, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.8687054097111588, 0.0, nan, 0.05824436171194732, nan, 0.00040998308819761186, nan, nan, nan, 0.00012146732528949713, 0.0, 0.0, 0.0023685118842166654, nan, nan, nan, nan, 0.0027728020023065785, nan, 0.0]
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  ## Model description
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@@ -40,20 +40,27 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-05
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- - train_batch_size: 1
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- - eval_batch_size: 1
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 3
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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- | 4.0562 | 1.0 | 80 | 3.4450 | 0.0153 | 0.0686 | 0.4812 | [0.7793520968900188, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.02432875250844616, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0032801739760782893, 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, 0.12812692281805033, nan, 0.0, 0.0, 0.05380656279838841, nan, 0.0, nan, nan, nan, 0.0003018650007218511, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.008481858011825258, 0.0003758691975192633, nan, 0.0, 0.010826130083604932, 0.0, 0.0, 0.005665119052689867, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.07572116691493873, 0.0009061488673139158, nan, 0.00028572287888357543, 0.0, 0.0005691934965994337, 0.0, nan, nan, 0.0, 0.005145141665218147, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0016498912362201822, 0.0, 0.0] | [0.8320357969624665, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.044499683472624736, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0033331798720132593, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.843680423236109, nan, 0.0, nan, 0.1561081081081081, nan, 0.0, nan, nan, nan, 0.00030909131632584497, nan, nan, nan, nan, nan, nan, nan, 0.016399105204289756, 0.0004157427937915743, nan, nan, 0.01858790326504793, nan, 0.0, 0.005819295558958652, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.6029646048347995, 0.0009201324990798675, nan, 0.00029229869850158456, nan, 0.0006662225183211193, nan, nan, nan, 0.0, 0.0084153067606755, 0.0, 0.0, nan, nan, nan, nan, 0.002196157338110078, nan, 0.0] |
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- | 3.4584 | 2.0 | 160 | 3.1139 | 0.0241 | 0.0797 | 0.4821 | [0.7782469073708838, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.010029985991945368, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0017241143388842517, 0.0, 0.1557832833948993, nan, 0.0, nan, 0.07580358123695485, nan, 0.0, nan, 0.0, nan, 0.028521816696650947, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.021143006947175254, 0.0, nan, nan, 0.018894200915169017, nan, 0.0, 0.0064420428056791695, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.06671080655150016, 0.0, nan, 0.016629320979883684, nan, 0.0010633250993799996, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.000702695105998854, 0.0, 0.0] | [0.8244354154654462, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.01064577392395212, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.002417614045186358, nan, 0.8166523587177286, nan, 0.0, nan, 0.23312162162162162, nan, 0.0, nan, nan, nan, 0.050991108025175265, nan, nan, nan, nan, nan, nan, nan, 0.026531350746759656, 0.0, nan, nan, 0.03812466546640066, nan, 0.0, 0.0066506234959527455, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.9177460691279352, 0.0, nan, 0.02032245161687333, nan, 0.0011103708638685321, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0007974873015483523, nan, 0.0] |
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- | 3.1748 | 3.0 | 240 | 2.9804 | 0.0270 | 0.0832 | 0.5068 | [0.8021452931267776, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.00757909387963703, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.000748031223377361, 0.0, 0.15502171352863242, nan, 0.0, nan, 0.056221004367996964, nan, 0.0, nan, 0.0, nan, 0.040391599317767694, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.07047672462142457, 0.0, nan, nan, 0.05062781753814023, nan, 0.0, 0.03166986564299424, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.08493484197676761, 0.0, nan, 0.04267983360952349, nan, 0.0003925481280361144, 0.0, nan, nan, 0.00012044806680852773, 0.0, 0.0, 0.002362410370862814, nan, nan, nan, nan, 0.0022562095679258846, 0.0, 0.0] | [0.8694985349631393, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.008096131396611705, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0010361203050798676, nan, 0.8231816690745898, nan, 0.0, nan, 0.11201351351351352, nan, 0.0, nan, nan, nan, 0.07139561448697561, nan, nan, nan, nan, nan, nan, nan, 0.10334561484308179, 0.0, nan, nan, 0.12090652522991582, nan, 0.0, 0.037541019470575365, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.8687054097111588, 0.0, nan, 0.05824436171194732, nan, 0.00040998308819761186, nan, nan, nan, 0.00012146732528949713, 0.0, 0.0, 0.0023685118842166654, nan, nan, nan, nan, 0.0027728020023065785, nan, 0.0] |
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
17
  It achieves the following results on the evaluation set:
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+ - Loss: 2.2049
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+ - Mean Iou: 0.0529
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+ - Mean Accuracy: 0.1563
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+ - Overall Accuracy: 0.5690
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+ - Per Category Iou: [0.8411342620340232, nan, nan, 0.00440539598785405, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0009979755353425907, nan, nan, nan, 0.05215870239325753, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.02585601844888956, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0023056841784332783, 0.0, 0.15720250055027643, nan, 0.0, nan, 0.010010171425736188, nan, 0.0, nan, nan, nan, 0.3278824063816368, 0.0, nan, nan, nan, nan, nan, nan, 0.16577780294023287, 0.0, nan, nan, 0.07446100952634326, nan, 0.0, 0.07748760838659272, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.19954901550549783, 0.0, nan, 0.17496882019195398, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.00033325795355812374, nan, nan, nan, nan, 0.000906274111177789, nan, 0.0]
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+ - Per Category Accuracy: [0.9215607553461405, nan, nan, 0.00444544906570223, 0.0, 0.0, nan, nan, nan, 0.0, 0.0010163723058874091, nan, nan, nan, 0.052159531836397176, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.027675301503830005, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.002321676979901185, nan, 0.46607271229855457, nan, 0.0, nan, 0.010905405405405405, nan, 0.0, nan, nan, nan, 0.8989002620556812, nan, nan, nan, nan, nan, nan, nan, 0.722448845318771, 0.0, nan, nan, 0.08852367281397498, nan, 0.0, 0.7941369503390943, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.8918750432915425, 0.0, nan, 0.8999876926863789, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.00033325795355812374, nan, nan, nan, nan, 0.000907908620224278, nan, 0.0]
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 5e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 2
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 10
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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+ | 3.0427 | 1.0 | 40 | 2.9270 | 0.0309 | 0.0973 | 0.5028 | [0.7941427394940275, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.009428203215085125, nan, nan, 0.0, 0.0013435124156353233, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.001934640522875817, 0.0, 0.16045113550578763, nan, 0.0, nan, 0.03580332494192905, nan, 0.0, nan, nan, nan, 0.061999426140432926, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0010348020260334404, 2.388401920275144e-05, nan, nan, 0.03135198802522555, nan, 0.0, 0.020500368822227687, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.07843223883410502, 0.0, nan, 0.188387511552216, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0035724679130164274, 0.0, 0.0] | [0.8432045749709368, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.009687480035543991, nan, nan, nan, 0.001351695184784683, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0024847699908859787, nan, 0.8544672208379572, nan, 0.0, nan, 0.039783783783783784, nan, 0.0, nan, nan, nan, 0.12292875221179475, nan, nan, nan, nan, nan, nan, nan, 0.0012500822422527797, 2.5983924611973392e-05, nan, nan, 0.046627901318670624, nan, 0.0, 0.02918398599868738, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.9222483895546166, 0.0, nan, 0.7212701147656995, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.004367776605403283, nan, 0.0] |
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+ | 3.1428 | 2.0 | 80 | 2.6637 | 0.0401 | 0.1177 | 0.5226 | [0.8074618180190414, nan, nan, 0.0016479812230018227, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.012663610529154308, nan, nan, nan, 0.00022241639526570815, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0017822941051287508, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.1701888230556834, nan, 0.0, nan, 0.09306181364230402, nan, 0.0, nan, nan, nan, 0.16484283173158512, 0.0, nan, nan, nan, nan, nan, 0.0, 0.15211849507735584, 0.0, nan, nan, 8.05134457454395e-05, nan, 0.0, 0.07005691912505234, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.09765688073394495, 0.0, nan, 0.14866396338688687, nan, 0.0002550933641712867, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0030113184036551174, 0.0, 0.0] | [0.863908907671061, nan, nan, 0.0016576250753465944, 0.0, 0.0, nan, nan, nan, 0.0, 0.014182749548440303, nan, nan, nan, 0.00022263214808218308, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0017976201655420362, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.6607759793965535, nan, 0.0, nan, 0.12894594594594594, nan, 0.0, nan, nan, nan, 0.4699263108383542, nan, nan, nan, nan, nan, nan, nan, 0.2277123494966774, 0.0, nan, nan, 8.515400710427716e-05, nan, 0.0, 0.2708816451542332, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.9216423079587172, 0.0, nan, 0.7890680286760408, nan, 0.0002562394301235074, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0032022182416018455, nan, 0.0] |
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+ | 2.3713 | 3.0 | 120 | 2.4315 | 0.0467 | 0.1308 | 0.5506 | [0.816217909207174, nan, nan, 0.010999749436231521, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.019900776410572637, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.00397223196832239, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.1398504132771441, nan, 0.0, nan, 0.0910590813486316, nan, 0.0, nan, nan, nan, 0.22628639476729678, 0.0, nan, nan, nan, nan, nan, nan, 0.20183578232866675, 0.0, nan, nan, 0.01505267877157588, nan, 0.0, 0.10048424832689483, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.14180992148191232, 0.0, nan, 0.14801565612477238, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0] | [0.9126766191641265, nan, nan, 0.011025718304199317, 0.0, 0.0, nan, nan, nan, 0.0, 0.024741405846173504, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.004252579048334518, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, 0.4705326046748606, nan, 0.0, nan, 0.0977027027027027, nan, 0.0, nan, nan, nan, 0.6945953815485922, nan, nan, nan, nan, nan, nan, nan, 0.43040660569774325, 0.0, nan, nan, 0.016337404505863462, nan, 0.0, 0.4040253773791293, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.8950959340583223, 0.0, nan, 0.879065259530476, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0] |
57
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+ | 2.518 | 7.0 | 280 | 2.1968 | 0.0483 | 0.1448 | 0.5565 | [0.835294581918955, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0004969440795110527, nan, nan, nan, 0.0070991820852854765, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.024552328387392183, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0008260853051768012, 0.0, 0.10751554522165623, nan, 0.0, nan, 0.0497325466320919, nan, 0.0, nan, nan, nan, 0.26028636001713373, 0.0, nan, nan, nan, nan, nan, nan, 0.1383557089002794, 0.0, nan, nan, 0.056956059603313076, nan, 0.0, 0.08924809533015103, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.1890732919633464, 0.0, nan, 0.17234102039159804, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.0008926552327449743, nan, nan, nan, nan, 0.000489913897632491, nan, 0.0] | [0.918687180300745, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0005052822320697406, nan, nan, nan, 0.0071083264423382735, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.026615510510711936, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0008346524679810045, nan, 0.3170885766731576, nan, 0.0, nan, 0.05364864864864865, nan, 0.0, nan, nan, nan, 0.9064438820077497, nan, nan, nan, nan, nan, nan, nan, 0.5270083558128824, 0.0, nan, nan, 0.06909639433604205, nan, 0.0, 0.787792605556771, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.858956154325691, 0.0, nan, 0.8819267099473862, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.0008926552327449743, nan, nan, nan, nan, 0.0004907614163374475, nan, 0.0] |
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