Mean_iou 在 tensorflow 中不是 updating/resulting 的正确值
Mean_iou in tensorflow not updating/resulting in correct value
我在 tensorflow 中实现了一个 U-NET 版本，试图从卫星图像中识别建筑物。该实施正在发挥作用，并在分类方面取得了可喜的成果。除 mean_iou 外，所有指标似乎都正常工作。不管超参数和从数据集中选择的图像如何，mean_iou 始终相同。该值类似于每个纪元后的 15 个小数点。
与 mean_iou 相比，准确率和召回率值要高得多，这是预期的，所以似乎有些地方没有按预期工作。
由于我对 tensorflow 比较陌生，所以错误可能完全不同，但我是来学习的。所有反馈将不胜感激。
这是模型训练的相关代码和打印输出。
import numpy as np
import tensorflow as tf
from unet_model import build_unet
from data import load_dataset, tf_dataset
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, CSVLogger, EarlyStopping
model_types = ['segnet-master', 'unet-master', 'simpler', 'even-simpler']
if __name__ == "__main__":
""" Hyperparamaters """
dataset_path = "building-segmentation"
input_shape = (64, 64, 3)
batch_size = 20
model = 3
epochs = 5
res = 64
lr = 1e-3
model_path = f"unet_models/unet_{epochs}_epochs_{res}.h5"
csv_path = f"csv/data_unet_{epochs}_{res}.csv"
""" Load the dataset """
(train_images, train_masks), (val_images, val_masks) = load_dataset(dataset_path)
train_dataset = tf_dataset(train_images, train_masks, batch=batch_size)
val_dataset = tf_dataset(val_images, val_masks, batch=batch_size)
model = build_unet(input_shape)
model.compile(
loss="binary_crossentropy",
optimizer=tf.keras.optimizers.Adam(lr),
metrics=[
tf.keras.metrics.MeanIoU(num_classes=2),
tf.keras.metrics.IoU(num_classes=2, target_class_ids=[0]),
tf.keras.metrics.Recall(),
tf.keras.metrics.Precision()
]
)
callbacks = [
ModelCheckpoint(model_path, monitor="val_loss", verbose=1),
ReduceLROnPlateau(monitor="val_loss", patience=10, factor=0.1, verbose=1),
CSVLogger(csv_path),
EarlyStopping(monitor="val_loss", patience=10)
]
train_steps = len(train_images)//batch_size
if len(train_images) % batch_size != 0:
train_steps += 1
test_steps = len(val_images)//batch_size
if len(val_images) % batch_size != 0:
test_steps += 1
model.fit(
train_dataset,
validation_data=val_dataset,
epochs=epochs,
steps_per_epoch=train_steps,
validation_steps=test_steps,
callbacks=callbacks
)
epoch
loss
lr
mean_io_u
precision
recall
val_loss
val_mean_io_u
val_precision
val_recall
0
0.41137945652008057
0.001
0.37184661626815796
0.695444643497467
0.5243006944656372
0.87176513671875
0.37157535552978516
0.38247567415237427
0.9118495583534241
1
0.3461640477180481
0.001
0.37182655930519104
0.7579150795936584
0.6075601577758789
0.3907579183578491
0.37157535552978516
0.8406943082809448
0.5024654865264893
2
0.3203786611557007
0.001
0.37182655930519104
0.7694798707962036
0.6599727272987366
0.3412915766239166
0.37157535552978516
0.6986522674560547
0.7543279528617859
3
0.2999393939971924
0.001
0.37182655930519104
0.7859976887702942
0.6890525221824646
0.40518054366111755
0.37157535552978516
0.6738141775131226
0.6654454469680786
4
0.28737708926200867
0.001
0.37182655930519104
0.793653130531311
0.7092126607894897
0.37544798851013184
0.37157535552978516
0.621263325214386
0.768422544002533
5
0.27629318833351135
0.001
0.37182655930519104
0.8028419613838196
0.72260981798172
0.4055494964122772
0.37157535552978516
0.8477562665939331
0.5473824143409729
6
0.2665417492389679
0.001
0.37182655930519104
0.809609055519104
0.7353982329368591
0.33294594287872314
0.37157535552978516
0.7307689785957336
0.6933897733688354
7
0.25887876749038696
0.001
0.37182655930519104
0.8132126927375793
0.744954526424408
0.28797024488449097
0.37157535552978516
0.7534120082855225
0.7735632061958313
8
0.25271594524383545
0.001
0.37182655930519104
0.8179733753204346
0.7538670897483826
0.30249008536338806
0.37157535552978516
0.8644329905509949
0.6237345337867737
9
0.24556593596935272
0.001
0.37182655930519104
0.8207928538322449
0.7622584104537964
0.3576349914073944
0.37157535552978516
0.6576451063156128
0.8346141576766968
10
0.23954670131206512
0.001
0.37182655930519104
0.8256030082702637
0.769091010093689
0.2541409134864807
0.37157535552978516
0.8100516200065613
0.7633218765258789
11
0.2349284589290619
0.001
0.37182655930519104
0.8274455070495605
0.7762861847877502
0.24383187294006348
0.37157535552978516
0.795067310333252
0.8124401569366455
12
0.22480393946170807
0.001
0.37182655930519104
0.8354562520980835
0.787416398525238
0.3778316378593445
0.37157535552978516
0.6533672213554382
0.8588836789131165
13
0.22573505342006683
0.001
0.37182655930519104
0.8342418670654297
0.7852107882499695
0.3342073857784271
0.37157535552978516
0.6768029928207397
0.7917631268501282
14
0.21639415621757507
0.001
0.37182655930519104
0.8411555886268616
0.7972605228424072
0.2792396545410156
0.37157535552978516
0.7611830234527588
0.7955203652381897
15
0.21154287457466125
0.001
0.37182655930519104
0.8441442251205444
0.8019176125526428
0.27426305413246155
0.37157535552978516
0.8764772415161133
0.6708933115005493
16
0.20740143954753876
0.001
0.37182655930519104
0.8469985127449036
0.8068550825119019
0.367437481880188
0.37157535552978516
0.646026611328125
0.8527452945709229
17
0.2005360722541809
0.001
0.37182655930519104
0.8522992134094238
0.8129924535751343
0.22591133415699005
0.37157535552978516
0.8203750252723694
0.8089460730552673
18
0.1976771354675293
0.001
0.37182655930519104
0.853760302066803
0.8163849115371704
0.2331937551498413
0.37157535552978516
0.807687520980835
0.8157453536987305
19
0.19583451747894287
0.001
0.37182655930519104
0.8560215830802917
0.8190248012542725
0.2519392669200897
0.37157535552978516
0.7935053110122681
0.8000433444976807
20
0.1872621327638626
0.001
0.37182655930519104
0.8615736365318298
0.8263705372810364
0.22855037450790405
0.37157535552978516
0.7948822975158691
0.8500961065292358
21
0.1852150857448578
0.001
0.37182655930519104
0.8620718717575073
0.8289932012557983
0.2352440059185028
0.37157535552978516
0.7972174286842346
0.8323403000831604
22
0.17845036089420319
0.001
0.37182655930519104
0.8677510023117065
0.8351714611053467
0.21090157330036163
0.37157535552978516
0.8470866084098816
0.8098670244216919
23
0.1732502579689026
0.001
0.37182655930519104
0.8711428046226501
0.8414102792739868
0.32612740993499756
0.37157535552978516
0.8412857055664062
0.695543646812439
24
0.17396509647369385
0.001
0.37182655930519104
0.8704758882522583
0.840953528881073
0.2149643898010254
0.37157535552978516
0.8315027952194214
0.8180400729179382
25
0.1740695685148239
0.001
0.37182655930519104
0.8702647089958191
0.8410759568214417
0.2138184905052185
0.37157535552978516
0.8604387044906616
0.7878146171569824
26
0.16104143857955933
0.001
0.37182655930519104
0.8794053196907043
0.8530260324478149
0.23256370425224304
0.37157535552978516
0.8179659843444824
0.8145195841789246
27
0.15866029262542725
0.001
0.37182655930519104
0.8813797831535339
0.8556373119354248
0.21111807227134705
0.37157535552978516
0.8566364049911499
0.805817723274231
28
0.15867507457733154
0.001
0.37182655930519104
0.8811318874359131
0.8551875352859497
0.2091868668794632
0.37157535552978516
0.8498891592025757
0.8088852763175964
29
0.15372247993946075
0.001
0.37182655930519104
0.884833574295044
0.8602938055992126
0.2100905030965805
0.37157535552978516
0.8543928265571594
0.8121073246002197
30
0.1550114005804062
0.001
0.37182655930519104
0.8840479850769043
0.85946124792099
0.21207265555858612
0.37157535552978516
0.8512551784515381
0.814805269241333
31
0.14192143082618713
0.001
0.37182655930519104
0.8927850127220154
0.8717316389083862
0.21726688742637634
0.37157535552978516
0.8147332072257996
0.8602878451347351
32
0.1401694267988205
0.001
0.37182655930519104
0.8940809965133667
0.8732201457023621
0.21714988350868225
0.37157535552978516
0.8370103240013123
0.8307888507843018
33
0.13880570232868195
0.001
0.37182655930519104
0.8950505256652832
0.8743049502372742
0.23316830396652222
0.37157535552978516
0.8291308283805847
0.8264546990394592
34
0.14308543503284454
0.001
0.37182655930519104
0.892676830291748
0.8704872131347656
0.2735193967819214
0.37157535552978516
0.7545790076255798
0.8698106408119202
35
0.14015090465545654
0.001
0.37182655930519104
0.8939213752746582
0.8743175864219666
0.20235474407672882
0.37157535552978516
0.8535885810852051
0.8286886215209961
36
0.1288939267396927
0.001
0.37182655930519104
0.9015076756477356
0.8844809532165527
0.22387968003749847
0.37157535552978516
0.8760555982589722
0.7937673926353455
37
0.12568938732147217
0.001
0.37182655930519104
0.9041174054145813
0.8872519731521606
0.21494744718074799
0.37157535552978516
0.8468613028526306
0.8249993324279785
38
0.12176792323589325
0.001
0.37182655930519104
0.9065613746643066
0.8911336064338684
0.23827765882015228
0.37157535552978516
0.8391880989074707
0.8176671862602234
39
0.11993639171123505
0.001
0.37182655930519104
0.9084023237228394
0.8925207257270813
0.22297391295433044
0.37157535552978516
0.8404833674430847
0.8346469402313232
40
0.11878598481416702
0.001
0.37182655930519104
0.9090615510940552
0.8941413164138794
0.22415445744991302
0.37157535552978516
0.8580552339553833
0.8152300715446472
41
0.1256236732006073
0.001
0.37182655930519104
0.9046309590339661
0.8880045413970947
0.20100584626197815
0.37157535552978516
0.8520526885986328
0.8423823714256287
42
0.10843898355960846
0.001
0.37182655930519104
0.9163806438446045
0.903978168964386
0.21887923777103424
0.37157535552978516
0.86836838722229
0.8237167596817017
43
0.10670299828052521
0.001
0.37182655930519104
0.9178842902183533
0.9054436683654785
0.21005834639072418
0.37157535552978516
0.8679876327514648
0.8253417611122131
44
0.10276217758655548
0.001
0.37182655930519104
0.9207708239555359
0.909300684928894
0.2151617556810379
0.37157535552978516
0.8735089302062988
0.8225894570350647
45
0.10141195356845856
0.001
0.3718271255493164
0.9218501448631287
0.9108821749687195
0.22106514871120453
0.37157535552978516
0.8555923700332642
0.8328163623809814
46
0.09918847680091858
0.001
0.37182655930519104
0.9235833883285522
0.9129346609115601
0.23230132460594177
0.37157535552978516
0.8555824756622314
0.8224022388458252
47
0.10588783025741577
0.001
0.37182655930519104
0.9191931486129761
0.9068878293037415
0.22423967719078064
0.37157535552978516
0.8427634239196777
0.825032114982605
48
0.103585384786129
0.001
0.37182655930519104
0.9209527969360352
0.9087461233139038
0.2110774666070938
0.37157535552978516
0.8639764785766602
0.8252225518226624
49
0.09157560020685196
0.001
0.37182655930519104
0.9292182922363281
0.9203035831451416
0.22161123156547546
0.37157535552978516
0.8649827837944031
0.8406093120574951
50
0.08616402745246887
0.001
0.37182655930519104
0.9334553480148315
0.9252204298973083
0.2387685328722
0.37157535552978516
0.8806527256965637
0.811405599117279
51
0.0846954956650734
0.001
0.37182655930519104
0.9345796704292297
0.9265674352645874
0.22581790387630463
0.37157535552978516
0.8756505846977234
0.8313769698143005
对于二进制问题，还有一个名为 tf.keras.metrics.BinaryIoU(name='IoU') 的 IOU。这可能会解决问题。
我在从 tf.keras.metrics.MeanIoU 移动后解决了多 class 分段的相同问题
到 tf.keras.metrics.OneHotMeanIoU 因为我正在使用一个热编码标签。
我在 tensorflow 中实现了一个 U-NET 版本，试图从卫星图像中识别建筑物。该实施正在发挥作用，并在分类方面取得了可喜的成果。除 mean_iou 外，所有指标似乎都正常工作。不管超参数和从数据集中选择的图像如何，mean_iou 始终相同。该值类似于每个纪元后的 15 个小数点。
与 mean_iou 相比，准确率和召回率值要高得多，这是预期的，所以似乎有些地方没有按预期工作。
由于我对 tensorflow 比较陌生，所以错误可能完全不同，但我是来学习的。所有反馈将不胜感激。
这是模型训练的相关代码和打印输出。
import numpy as np
import tensorflow as tf
from unet_model import build_unet
from data import load_dataset, tf_dataset
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, CSVLogger, EarlyStopping
model_types = ['segnet-master', 'unet-master', 'simpler', 'even-simpler']
if __name__ == "__main__":
""" Hyperparamaters """
dataset_path = "building-segmentation"
input_shape = (64, 64, 3)
batch_size = 20
model = 3
epochs = 5
res = 64
lr = 1e-3
model_path = f"unet_models/unet_{epochs}_epochs_{res}.h5"
csv_path = f"csv/data_unet_{epochs}_{res}.csv"
""" Load the dataset """
(train_images, train_masks), (val_images, val_masks) = load_dataset(dataset_path)
train_dataset = tf_dataset(train_images, train_masks, batch=batch_size)
val_dataset = tf_dataset(val_images, val_masks, batch=batch_size)
model = build_unet(input_shape)
model.compile(
loss="binary_crossentropy",
optimizer=tf.keras.optimizers.Adam(lr),
metrics=[
tf.keras.metrics.MeanIoU(num_classes=2),
tf.keras.metrics.IoU(num_classes=2, target_class_ids=[0]),
tf.keras.metrics.Recall(),
tf.keras.metrics.Precision()
]
)
callbacks = [
ModelCheckpoint(model_path, monitor="val_loss", verbose=1),
ReduceLROnPlateau(monitor="val_loss", patience=10, factor=0.1, verbose=1),
CSVLogger(csv_path),
EarlyStopping(monitor="val_loss", patience=10)
]
train_steps = len(train_images)//batch_size
if len(train_images) % batch_size != 0:
train_steps += 1
test_steps = len(val_images)//batch_size
if len(val_images) % batch_size != 0:
test_steps += 1
model.fit(
train_dataset,
validation_data=val_dataset,
epochs=epochs,
steps_per_epoch=train_steps,
validation_steps=test_steps,
callbacks=callbacks
)
| epoch | loss | lr | mean_io_u | precision | recall | val_loss | val_mean_io_u | val_precision | val_recall |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.41137945652008057 | 0.001 | 0.37184661626815796 | 0.695444643497467 | 0.5243006944656372 | 0.87176513671875 | 0.37157535552978516 | 0.38247567415237427 | 0.9118495583534241 |
| 1 | 0.3461640477180481 | 0.001 | 0.37182655930519104 | 0.7579150795936584 | 0.6075601577758789 | 0.3907579183578491 | 0.37157535552978516 | 0.8406943082809448 | 0.5024654865264893 |
| 2 | 0.3203786611557007 | 0.001 | 0.37182655930519104 | 0.7694798707962036 | 0.6599727272987366 | 0.3412915766239166 | 0.37157535552978516 | 0.6986522674560547 | 0.7543279528617859 |
| 3 | 0.2999393939971924 | 0.001 | 0.37182655930519104 | 0.7859976887702942 | 0.6890525221824646 | 0.40518054366111755 | 0.37157535552978516 | 0.6738141775131226 | 0.6654454469680786 |
| 4 | 0.28737708926200867 | 0.001 | 0.37182655930519104 | 0.793653130531311 | 0.7092126607894897 | 0.37544798851013184 | 0.37157535552978516 | 0.621263325214386 | 0.768422544002533 |
| 5 | 0.27629318833351135 | 0.001 | 0.37182655930519104 | 0.8028419613838196 | 0.72260981798172 | 0.4055494964122772 | 0.37157535552978516 | 0.8477562665939331 | 0.5473824143409729 |
| 6 | 0.2665417492389679 | 0.001 | 0.37182655930519104 | 0.809609055519104 | 0.7353982329368591 | 0.33294594287872314 | 0.37157535552978516 | 0.7307689785957336 | 0.6933897733688354 |
| 7 | 0.25887876749038696 | 0.001 | 0.37182655930519104 | 0.8132126927375793 | 0.744954526424408 | 0.28797024488449097 | 0.37157535552978516 | 0.7534120082855225 | 0.7735632061958313 |
| 8 | 0.25271594524383545 | 0.001 | 0.37182655930519104 | 0.8179733753204346 | 0.7538670897483826 | 0.30249008536338806 | 0.37157535552978516 | 0.8644329905509949 | 0.6237345337867737 |
| 9 | 0.24556593596935272 | 0.001 | 0.37182655930519104 | 0.8207928538322449 | 0.7622584104537964 | 0.3576349914073944 | 0.37157535552978516 | 0.6576451063156128 | 0.8346141576766968 |
| 10 | 0.23954670131206512 | 0.001 | 0.37182655930519104 | 0.8256030082702637 | 0.769091010093689 | 0.2541409134864807 | 0.37157535552978516 | 0.8100516200065613 | 0.7633218765258789 |
| 11 | 0.2349284589290619 | 0.001 | 0.37182655930519104 | 0.8274455070495605 | 0.7762861847877502 | 0.24383187294006348 | 0.37157535552978516 | 0.795067310333252 | 0.8124401569366455 |
| 12 | 0.22480393946170807 | 0.001 | 0.37182655930519104 | 0.8354562520980835 | 0.787416398525238 | 0.3778316378593445 | 0.37157535552978516 | 0.6533672213554382 | 0.8588836789131165 |
| 13 | 0.22573505342006683 | 0.001 | 0.37182655930519104 | 0.8342418670654297 | 0.7852107882499695 | 0.3342073857784271 | 0.37157535552978516 | 0.6768029928207397 | 0.7917631268501282 |
| 14 | 0.21639415621757507 | 0.001 | 0.37182655930519104 | 0.8411555886268616 | 0.7972605228424072 | 0.2792396545410156 | 0.37157535552978516 | 0.7611830234527588 | 0.7955203652381897 |
| 15 | 0.21154287457466125 | 0.001 | 0.37182655930519104 | 0.8441442251205444 | 0.8019176125526428 | 0.27426305413246155 | 0.37157535552978516 | 0.8764772415161133 | 0.6708933115005493 |
| 16 | 0.20740143954753876 | 0.001 | 0.37182655930519104 | 0.8469985127449036 | 0.8068550825119019 | 0.367437481880188 | 0.37157535552978516 | 0.646026611328125 | 0.8527452945709229 |
| 17 | 0.2005360722541809 | 0.001 | 0.37182655930519104 | 0.8522992134094238 | 0.8129924535751343 | 0.22591133415699005 | 0.37157535552978516 | 0.8203750252723694 | 0.8089460730552673 |
| 18 | 0.1976771354675293 | 0.001 | 0.37182655930519104 | 0.853760302066803 | 0.8163849115371704 | 0.2331937551498413 | 0.37157535552978516 | 0.807687520980835 | 0.8157453536987305 |
| 19 | 0.19583451747894287 | 0.001 | 0.37182655930519104 | 0.8560215830802917 | 0.8190248012542725 | 0.2519392669200897 | 0.37157535552978516 | 0.7935053110122681 | 0.8000433444976807 |
| 20 | 0.1872621327638626 | 0.001 | 0.37182655930519104 | 0.8615736365318298 | 0.8263705372810364 | 0.22855037450790405 | 0.37157535552978516 | 0.7948822975158691 | 0.8500961065292358 |
| 21 | 0.1852150857448578 | 0.001 | 0.37182655930519104 | 0.8620718717575073 | 0.8289932012557983 | 0.2352440059185028 | 0.37157535552978516 | 0.7972174286842346 | 0.8323403000831604 |
| 22 | 0.17845036089420319 | 0.001 | 0.37182655930519104 | 0.8677510023117065 | 0.8351714611053467 | 0.21090157330036163 | 0.37157535552978516 | 0.8470866084098816 | 0.8098670244216919 |
| 23 | 0.1732502579689026 | 0.001 | 0.37182655930519104 | 0.8711428046226501 | 0.8414102792739868 | 0.32612740993499756 | 0.37157535552978516 | 0.8412857055664062 | 0.695543646812439 |
| 24 | 0.17396509647369385 | 0.001 | 0.37182655930519104 | 0.8704758882522583 | 0.840953528881073 | 0.2149643898010254 | 0.37157535552978516 | 0.8315027952194214 | 0.8180400729179382 |
| 25 | 0.1740695685148239 | 0.001 | 0.37182655930519104 | 0.8702647089958191 | 0.8410759568214417 | 0.2138184905052185 | 0.37157535552978516 | 0.8604387044906616 | 0.7878146171569824 |
| 26 | 0.16104143857955933 | 0.001 | 0.37182655930519104 | 0.8794053196907043 | 0.8530260324478149 | 0.23256370425224304 | 0.37157535552978516 | 0.8179659843444824 | 0.8145195841789246 |
| 27 | 0.15866029262542725 | 0.001 | 0.37182655930519104 | 0.8813797831535339 | 0.8556373119354248 | 0.21111807227134705 | 0.37157535552978516 | 0.8566364049911499 | 0.805817723274231 |
| 28 | 0.15867507457733154 | 0.001 | 0.37182655930519104 | 0.8811318874359131 | 0.8551875352859497 | 0.2091868668794632 | 0.37157535552978516 | 0.8498891592025757 | 0.8088852763175964 |
| 29 | 0.15372247993946075 | 0.001 | 0.37182655930519104 | 0.884833574295044 | 0.8602938055992126 | 0.2100905030965805 | 0.37157535552978516 | 0.8543928265571594 | 0.8121073246002197 |
| 30 | 0.1550114005804062 | 0.001 | 0.37182655930519104 | 0.8840479850769043 | 0.85946124792099 | 0.21207265555858612 | 0.37157535552978516 | 0.8512551784515381 | 0.814805269241333 |
| 31 | 0.14192143082618713 | 0.001 | 0.37182655930519104 | 0.8927850127220154 | 0.8717316389083862 | 0.21726688742637634 | 0.37157535552978516 | 0.8147332072257996 | 0.8602878451347351 |
| 32 | 0.1401694267988205 | 0.001 | 0.37182655930519104 | 0.8940809965133667 | 0.8732201457023621 | 0.21714988350868225 | 0.37157535552978516 | 0.8370103240013123 | 0.8307888507843018 |
| 33 | 0.13880570232868195 | 0.001 | 0.37182655930519104 | 0.8950505256652832 | 0.8743049502372742 | 0.23316830396652222 | 0.37157535552978516 | 0.8291308283805847 | 0.8264546990394592 |
| 34 | 0.14308543503284454 | 0.001 | 0.37182655930519104 | 0.892676830291748 | 0.8704872131347656 | 0.2735193967819214 | 0.37157535552978516 | 0.7545790076255798 | 0.8698106408119202 |
| 35 | 0.14015090465545654 | 0.001 | 0.37182655930519104 | 0.8939213752746582 | 0.8743175864219666 | 0.20235474407672882 | 0.37157535552978516 | 0.8535885810852051 | 0.8286886215209961 |
| 36 | 0.1288939267396927 | 0.001 | 0.37182655930519104 | 0.9015076756477356 | 0.8844809532165527 | 0.22387968003749847 | 0.37157535552978516 | 0.8760555982589722 | 0.7937673926353455 |
| 37 | 0.12568938732147217 | 0.001 | 0.37182655930519104 | 0.9041174054145813 | 0.8872519731521606 | 0.21494744718074799 | 0.37157535552978516 | 0.8468613028526306 | 0.8249993324279785 |
| 38 | 0.12176792323589325 | 0.001 | 0.37182655930519104 | 0.9065613746643066 | 0.8911336064338684 | 0.23827765882015228 | 0.37157535552978516 | 0.8391880989074707 | 0.8176671862602234 |
| 39 | 0.11993639171123505 | 0.001 | 0.37182655930519104 | 0.9084023237228394 | 0.8925207257270813 | 0.22297391295433044 | 0.37157535552978516 | 0.8404833674430847 | 0.8346469402313232 |
| 40 | 0.11878598481416702 | 0.001 | 0.37182655930519104 | 0.9090615510940552 | 0.8941413164138794 | 0.22415445744991302 | 0.37157535552978516 | 0.8580552339553833 | 0.8152300715446472 |
| 41 | 0.1256236732006073 | 0.001 | 0.37182655930519104 | 0.9046309590339661 | 0.8880045413970947 | 0.20100584626197815 | 0.37157535552978516 | 0.8520526885986328 | 0.8423823714256287 |
| 42 | 0.10843898355960846 | 0.001 | 0.37182655930519104 | 0.9163806438446045 | 0.903978168964386 | 0.21887923777103424 | 0.37157535552978516 | 0.86836838722229 | 0.8237167596817017 |
| 43 | 0.10670299828052521 | 0.001 | 0.37182655930519104 | 0.9178842902183533 | 0.9054436683654785 | 0.21005834639072418 | 0.37157535552978516 | 0.8679876327514648 | 0.8253417611122131 |
| 44 | 0.10276217758655548 | 0.001 | 0.37182655930519104 | 0.9207708239555359 | 0.909300684928894 | 0.2151617556810379 | 0.37157535552978516 | 0.8735089302062988 | 0.8225894570350647 |
| 45 | 0.10141195356845856 | 0.001 | 0.3718271255493164 | 0.9218501448631287 | 0.9108821749687195 | 0.22106514871120453 | 0.37157535552978516 | 0.8555923700332642 | 0.8328163623809814 |
| 46 | 0.09918847680091858 | 0.001 | 0.37182655930519104 | 0.9235833883285522 | 0.9129346609115601 | 0.23230132460594177 | 0.37157535552978516 | 0.8555824756622314 | 0.8224022388458252 |
| 47 | 0.10588783025741577 | 0.001 | 0.37182655930519104 | 0.9191931486129761 | 0.9068878293037415 | 0.22423967719078064 | 0.37157535552978516 | 0.8427634239196777 | 0.825032114982605 |
| 48 | 0.103585384786129 | 0.001 | 0.37182655930519104 | 0.9209527969360352 | 0.9087461233139038 | 0.2110774666070938 | 0.37157535552978516 | 0.8639764785766602 | 0.8252225518226624 |
| 49 | 0.09157560020685196 | 0.001 | 0.37182655930519104 | 0.9292182922363281 | 0.9203035831451416 | 0.22161123156547546 | 0.37157535552978516 | 0.8649827837944031 | 0.8406093120574951 |
| 50 | 0.08616402745246887 | 0.001 | 0.37182655930519104 | 0.9334553480148315 | 0.9252204298973083 | 0.2387685328722 | 0.37157535552978516 | 0.8806527256965637 | 0.811405599117279 |
| 51 | 0.0846954956650734 | 0.001 | 0.37182655930519104 | 0.9345796704292297 | 0.9265674352645874 | 0.22581790387630463 | 0.37157535552978516 | 0.8756505846977234 | 0.8313769698143005 |
对于二进制问题，还有一个名为 tf.keras.metrics.BinaryIoU(name='IoU') 的 IOU。这可能会解决问题。
我在从 tf.keras.metrics.MeanIoU 移动后解决了多 class 分段的相同问题 到 tf.keras.metrics.OneHotMeanIoU 因为我正在使用一个热编码标签。