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Dataset multi Source real Target sketch Labeled num perclass 3 Network resnet34
126 classes in this dataset
Labelled Source Examples: 70358
Unlabelled Target Dataset Size: 23826
Labelled Target Dataset Size: 378
Misc. Labelled Target Dataset Size: 378
Confidence Threshold for Consistency Reg is: 0.7
Phi value in Source example weighing is: 0.5
Bank keys - Target: dict_keys(['names', 'labels', 'feat_vec', 'domain_identifier']) Source: dict_keys(['feat_vec', 'names', 'labels', 'domain_identifier'])
Num - Target: 23826 Source: 70358
Unlabeled Target Data Batches: 496
S real T sketch Train Ep: 0 lr0.01 Loss Classification: 4.630312 Loss T 0.470104 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 4.9085, Accuracy: 8/1134 F1 (0.7055%)
Test set: Average loss: 4.9088, Accuracy: 553/23808 F1 (2.3227%)
Val set: Average loss: 4.9475, Accuracy: 5/360 F1 (1.3889%)
Patience Reset, Counter is: 0
best acc test 2.322749 acc val 1.388889 acc labeled target 0.705467
S real T sketch Train Ep: 100 lr0.009925650290240803 Loss Classification: 1.190844 Loss T 0.284911 Method MME
S real T sketch Train Ep: 200 lr0.009852577760521605 Loss Classification: 0.989957 Loss T 0.216871 Method MME
S real T sketch Train Ep: 300 lr0.009780748269686728 Loss Classification: 1.203546 Loss T 0.188634 Method MME
S real T sketch Train Ep: 400 lr0.009710128909124701 Loss Classification: 1.953593 Loss T 0.146474 Method MME
S real T sketch Train Ep: 500 lr0.00964068794694323 Loss Classification: 1.445050 Loss T 0.155289 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 2.5877, Accuracy: 489/1134 F1 (43.1217%)
Test set: Average loss: 2.4538, Accuracy: 11545/23808 F1 (48.4921%)
Val set: Average loss: 2.2590, Accuracy: 179/360 F1 (49.7222%)
Patience Reset, Counter is: 0
best acc test 48.492103 acc val 49.722222 acc labeled target 43.121693
S real T sketch Train Ep: 600 lr0.00957239477517603 Loss Classification: 0.868885 Loss T 0.157428 Method MME
S real T sketch Train Ep: 700 lr0.009505219859830012 Loss Classification: 0.741568 Loss T 0.125001 Method MME
S real T sketch Train Ep: 800 lr0.009439134693595126 Loss Classification: 0.834087 Loss T 0.137407 Method MME
S real T sketch Train Ep: 900 lr0.009374111751051751 Loss Classification: 1.363201 Loss T 0.129321 Method MME
S real T sketch Train Ep: 1000 lr0.009310124446222227 Loss Classification: 0.886719 Loss T 0.111964 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 2.5078, Accuracy: 534/1134 F1 (47.0899%)
Test set: Average loss: 2.2695, Accuracy: 12807/23808 F1 (53.7928%)
Val set: Average loss: 2.0973, Accuracy: 201/360 F1 (55.8333%)
Patience Reset, Counter is: 0
best acc test 53.792843 acc val 55.833333 acc labeled target 47.089947
S real T sketch Train Ep: 1100 lr0.00924714709232377 Loss Classification: 1.162746 Loss T 0.114028 Method MME
S real T sketch Train Ep: 1200 lr0.009185154863590003 Loss Classification: 0.789726 Loss T 0.122561 Method MME
S real T sketch Train Ep: 1300 lr0.00912412375903735 Loss Classification: 0.933266 Loss T 0.102427 Method MME
S real T sketch Train Ep: 1400 lr0.009064030568061049 Loss Classification: 1.401818 Loss T 0.108081 Method MME
S real T sketch Train Ep: 1500 lr0.009004852837753237 Loss Classification: 0.395528 Loss T 0.123458 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 2.2754, Accuracy: 599/1134 F1 (52.8219%)
Test set: Average loss: 2.2127, Accuracy: 13506/23808 F1 (56.7288%)
Val set: Average loss: 2.0468, Accuracy: 196/360 F1 (54.4444%)
Patience getting saturated, current counter is: 0
best acc test 53.792843 acc val 54.444444 acc labeled target 52.821869
S real T sketch Train Ep: 1600 lr0.008946568841842816 Loss Classification: 0.466660 Loss T 0.111746 Method MME
S real T sketch Train Ep: 1700 lr0.008889157551163433 Loss Classification: 1.415049 Loss T 0.127781 Method MME
S real T sketch Train Ep: 1800 lr0.008832598605562044 Loss Classification: 1.053801 Loss T 0.093029 Method MME
S real T sketch Train Ep: 1900 lr0.008776872287166303 Loss Classification: 0.354681 Loss T 0.112242 Method MME
Per Class Accuracy Calculated According to the Labelled Target examples is: [1. 1. 0.5555556 0.33333334 0.7777778 0.
0.22222222 0.8888889 0.6666667 0. 0.8888889 0.8888889
0. 0.33333334 0.44444445 0.22222222 0.33333334 1.
0.22222222 0. 0.5555556 1. 0.7777778 0.22222222
0.5555556 0.11111111 0. 0.5555556 0.5555556 0.11111111
0.7777778 0.44444445 0.7777778 0.44444445 0.8888889 0.22222222
0.33333334 0. 0.5555556 0.7777778 0.5555556 1.
0.11111111 0.5555556 0. 0.44444445 1. 0.7777778
0.44444445 1. 0.33333334 0.33333334 1. 0.8888889
1. 0.33333334 0.44444445 0.5555556 0.8888889 0.8888889
0.8888889 0. 0.8888889 0.33333334 0.7777778 0.5555556
0.5555556 0.6666667 0.5555556 0.6666667 0.33333334 0.33333334
0.5555556 0.5555556 1. 0. 0. 0.7777778
1. 0.6666667 0. 0.6666667 0.22222222 1.
0.33333334 0.33333334 1. 0.6666667 0. 0.22222222
0.5555556 1. 0.5555556 0.8888889 0.6666667 0.5555556
0.22222222 0.8888889 0.22222222 0.33333334 0.8888889 0.6666667
0.6666667 0.6666667 0.6666667 0. 0.33333334 0.6666667
0. 0.44444445 0.5555556 0.8888889 0.33333334 1.
0.22222222 1. 0. 0.44444445 0.22222222 0.33333334
0.6666667 0.7777778 0.7777778 0. 0. 0.8888889 ]
Top k classes which perform poorly are: [116, 19, 26, 105, 61, 75, 76, 12, 124, 80, 108, 5, 88, 37, 44, 123, 9, 29, 25, 42, 35, 114, 23, 98, 118, 18, 96, 15, 89, 82, 6, 85, 99, 51, 63, 55, 84, 106, 71, 50, 70, 112, 16, 3, 13, 36, 119, 45, 14, 48, 117, 33, 109, 31, 56, 68, 40, 20, 73, 66, 72, 65, 90, 95, 27, 28, 92, 57, 110, 24, 2, 38, 43, 8, 103, 81, 120, 79, 101, 94, 69, 107, 67, 87, 104, 102, 4, 39, 121, 77, 64, 22, 30, 32, 47, 122, 100, 111, 62, 125, 93, 7, 10, 11, 60, 59, 58, 53, 34, 97, 1, 17, 21, 41, 115, 49, 113, 52, 54, 74, 78, 83, 86, 91, 46, 0]
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Assigned Classwise weights to source
S real T sketch Train Ep: 2000 lr0.008721959494934213 Loss Classification: 0.362691 Loss T 0.079275 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 2.2482, Accuracy: 600/1134 F1 (52.9101%)
Test set: Average loss: 2.0492, Accuracy: 14195/23808 F1 (59.6228%)
Val set: Average loss: 2.0236, Accuracy: 207/360 F1 (57.5000%)
Patience Reset, Counter is: 1
best acc test 59.622816 acc val 57.500000 acc labeled target 52.910053
S real T sketch Train Ep: 2100 lr0.008667841720414475 Loss Classification: 0.822903 Loss T 0.102576 Method MME
S real T sketch Train Ep: 2200 lr0.008614501024650454 Loss Classification: 0.419282 Loss T 0.084888 Method MME
S real T sketch Train Ep: 2300 lr0.008561920016164943 Loss Classification: 0.793505 Loss T 0.103653 Method MME
S real T sketch Train Ep: 2400 lr0.008510081829966844 Loss Classification: 0.756622 Loss T 0.089165 Method MME
S real T sketch Train Ep: 2500 lr0.008458970107524513 Loss Classification: 0.378013 Loss T 0.106373 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 2.1246, Accuracy: 631/1134 F1 (55.6437%)
Test set: Average loss: 1.9962, Accuracy: 14662/23808 F1 (61.5843%)
Val set: Average loss: 1.8584, Accuracy: 214/360 F1 (59.4444%)
Patience Reset, Counter is: 0
best acc test 61.584341 acc val 59.444444 acc labeled target 55.643739
S real T sketch Train Ep: 2600 lr0.008408568977653933 Loss Classification: 0.557534 Loss T 0.079980 Method MME
S real T sketch Train Ep: 2700 lr0.00835886303827305 Loss Classification: 1.014099 Loss T 0.116736 Method MME
S real T sketch Train Ep: 2800 lr0.008309837338976545 Loss Classification: 0.757944 Loss T 0.093477 Method MME
S real T sketch Train Ep: 2900 lr0.008261477364388068 Loss Classification: 0.424733 Loss T 0.098142 Method MME
Per Class Accuracy Calculated According to the Labelled Target examples is: [1. 1. 0.33333334 0.5555556 0.7777778 0.
0.33333334 1. 0.8888889 0. 0.7777778 1.
0. 0. 0.44444445 0.7777778 1. 0.6666667
0.44444445 0.5555556 0.6666667 0.7777778 1. 0.33333334
0.44444445 0.33333334 0. 0.6666667 0.6666667 0.
0.44444445 0.33333334 0. 0.5555556 0.22222222 0.6666667
0.22222222 0. 1. 0.8888889 0.5555556 0.7777778
0.11111111 0.7777778 0.6666667 0.6666667 0.8888889 0.7777778
0.5555556 1. 0.6666667 0.22222222 1. 0.7777778
1. 0.33333334 0.8888889 0.6666667 0.8888889 1.
0.8888889 0.11111111 0.7777778 0.44444445 1. 0.6666667
0. 0.7777778 0.6666667 0.8888889 0.11111111 0.33333334
0.5555556 0.33333334 1. 0.33333334 0.22222222 0.5555556
0.7777778 0.44444445 0. 1. 0. 0.7777778
0.44444445 0.33333334 1. 0.6666667 0.22222222 0.5555556
0.5555556 1. 0.6666667 0.8888889 1. 0.44444445
0.6666667 1. 0.33333334 0.44444445 0.8888889 0.5555556
0.6666667 0.7777778 0.6666667 0. 0. 0.11111111
0. 0.33333334 0.33333334 0.7777778 0.6666667 1.
0.6666667 0.5555556 0.5555556 0.6666667 0. 0.22222222
0.33333334 0.7777778 0.6666667 0. 0. 1. ]
Top k classes which perform poorly are: [106, 80, 66, 124, 105, 108, 118, 37, 32, 29, 26, 13, 82, 9, 123, 5, 12, 61, 107, 42, 70, 88, 76, 119, 51, 36, 34, 110, 71, 55, 109, 2, 73, 6, 75, 120, 23, 25, 85, 98, 31, 84, 63, 95, 30, 24, 14, 79, 99, 18, 40, 3, 89, 90, 116, 77, 19, 33, 48, 72, 115, 101, 68, 112, 87, 92, 114, 17, 20, 102, 27, 28, 65, 35, 44, 45, 117, 50, 104, 57, 122, 96, 103, 62, 111, 4, 10, 121, 15, 21, 41, 83, 43, 67, 78, 47, 53, 93, 46, 56, 58, 39, 60, 100, 69, 8, 113, 0, 94, 1, 7, 11, 16, 22, 38, 49, 52, 54, 59, 64, 74, 81, 86, 91, 97, 125]
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