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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: 0.6
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: 4.876201 Loss T 0.470467 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 4.9321, Accuracy: 19/1134 F1 (1.6755%)
Test set: Average loss: 4.9291, Accuracy: 502/23808 F1 (2.1085%)
Val set: Average loss: 4.9329, Accuracy: 6/360 F1 (1.6667%)
Patience Reset, Counter is: 0
best acc test 2.108535 acc val 1.666667 acc labeled target 1.675485
S real T sketch Train Ep: 100 lr0.009925650290240803 Loss Classification: 0.905573 Loss T 0.273336 Method MME
S real T sketch Train Ep: 200 lr0.009852577760521605 Loss Classification: 0.458071 Loss T 0.223000 Method MME
S real T sketch Train Ep: 300 lr0.009780748269686728 Loss Classification: 1.074344 Loss T 0.178802 Method MME
S real T sketch Train Ep: 400 lr0.009710128909124701 Loss Classification: 0.750566 Loss T 0.160800 Method MME
S real T sketch Train Ep: 500 lr0.00964068794694323 Loss Classification: 1.226661 Loss T 0.135067 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 2.6798, Accuracy: 505/1134 F1 (44.5326%)
Test set: Average loss: 2.4332, Accuracy: 11910/23808 F1 (50.0252%)
Val set: Average loss: 2.3735, Accuracy: 175/360 F1 (48.6111%)
Patience Reset, Counter is: 0
best acc test 50.025202 acc val 48.611111 acc labeled target 44.532628
S real T sketch Train Ep: 600 lr0.00957239477517603 Loss Classification: 0.516181 Loss T 0.129822 Method MME
S real T sketch Train Ep: 700 lr0.009505219859830012 Loss Classification: 1.165909 Loss T 0.095830 Method MME
S real T sketch Train Ep: 800 lr0.009439134693595126 Loss Classification: 1.268313 Loss T 0.097967 Method MME
S real T sketch Train Ep: 900 lr0.009374111751051751 Loss Classification: 1.019921 Loss T 0.146299 Method MME
S real T sketch Train Ep: 1000 lr0.009310124446222227 Loss Classification: 1.043588 Loss T 0.125434 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 2.4628, Accuracy: 572/1134 F1 (50.4409%)
Test set: Average loss: 2.3740, Accuracy: 12576/23808 F1 (52.8226%)
Val set: Average loss: 2.1366, Accuracy: 204/360 F1 (56.6667%)
Patience Reset, Counter is: 0
best acc test 52.822581 acc val 56.666667 acc labeled target 50.440917
S real T sketch Train Ep: 1100 lr0.00924714709232377 Loss Classification: 0.870392 Loss T 0.106661 Method MME
S real T sketch Train Ep: 1200 lr0.009185154863590003 Loss Classification: 0.380526 Loss T 0.128443 Method MME
S real T sketch Train Ep: 1300 lr0.00912412375903735 Loss Classification: 0.374072 Loss T 0.115219 Method MME
S real T sketch Train Ep: 1400 lr0.009064030568061049 Loss Classification: 0.780829 Loss T 0.125438 Method MME
S real T sketch Train Ep: 1500 lr0.009004852837753237 Loss Classification: 0.522250 Loss T 0.099341 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 2.2990, Accuracy: 585/1134 F1 (51.5873%)
Test set: Average loss: 2.2456, Accuracy: 13387/23808 F1 (56.2290%)
Val set: Average loss: 2.0410, Accuracy: 199/360 F1 (55.2778%)
Patience getting saturated, current counter is: 0
best acc test 52.822581 acc val 55.277778 acc labeled target 51.587302
S real T sketch Train Ep: 1600 lr0.008946568841842816 Loss Classification: 0.642707 Loss T 0.094293 Method MME
S real T sketch Train Ep: 1700 lr0.008889157551163433 Loss Classification: 0.908724 Loss T 0.116498 Method MME
S real T sketch Train Ep: 1800 lr0.008832598605562044 Loss Classification: 0.918432 Loss T 0.085639 Method MME
S real T sketch Train Ep: 1900 lr0.008776872287166303 Loss Classification: 1.035692 Loss T 0.103142 Method MME
Per Class Accuracy Calculated According to the Labelled Target examples is: [0.7777778 1. 0.6666667 0.22222222 1. 0.33333334
0.33333334 0.5555556 0.8888889 0. 0.7777778 1.
0. 0. 0.22222222 0. 0.33333334 0.6666667
0.6666667 0.5555556 0.6666667 0.6666667 0.44444445 0.44444445
0.33333334 0. 0. 0.5555556 0.5555556 0.33333334
0.44444445 0.33333334 0.22222222 0.44444445 0.8888889 0.
0.33333334 0. 0.8888889 0.8888889 0.6666667 0.8888889
0. 0.5555556 0.22222222 0.33333334 0.8888889 0.5555556
0.6666667 1. 0.33333334 0.22222222 1. 0.33333334
0.7777778 0.44444445 0.7777778 0.7777778 0.8888889 1.
0.8888889 0.22222222 0.6666667 0.33333334 0.5555556 0.5555556
0.8888889 0.6666667 0. 0.5555556 0.8888889 0.5555556
0.44444445 0.22222222 0.5555556 0. 0.22222222 0.8888889
0.8888889 0.5555556 0. 1. 0.5555556 0.11111111
0.44444445 0.7777778 1. 0.6666667 0. 0.5555556
0.33333334 0.7777778 0.5555556 0.6666667 1. 0.44444445
0.7777778 1. 0.33333334 0. 0.6666667 0.33333334
0.6666667 0.7777778 0.5555556 0. 0.33333334 0.5555556
0. 0.33333334 0.5555556 1. 0.33333334 1.
0. 0.8888889 0.22222222 0.6666667 0. 0.33333334
1. 0.7777778 0.6666667 0. 0. 1. ]
Top k classes which perform poorly are: [37, 26, 99, 80, 108, 15, 42, 13, 12, 25, 118, 9, 68, 105, 114, 88, 75, 35, 123, 124, 83, 32, 76, 73, 51, 116, 14, 3, 61, 44, 90, 109, 50, 112, 53, 106, 101, 45, 31, 63, 5, 6, 29, 24, 119, 16, 98, 36, 95, 55, 84, 33, 72, 22, 23, 30, 69, 71, 74, 65, 64, 89, 79, 82, 92, 104, 107, 47, 110, 43, 7, 28, 27, 19, 21, 2, 122, 87, 93, 17, 18, 62, 20, 102, 117, 40, 100, 48, 67, 121, 96, 103, 0, 85, 91, 10, 54, 56, 57, 58, 39, 34, 41, 46, 38, 60, 66, 70, 77, 78, 8, 115, 120, 11, 4, 1, 86, 49, 111, 52, 59, 97, 81, 94, 113, 125]
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Assigned Classwise weights to source
S real T sketch Train Ep: 2000 lr0.008721959494934213 Loss Classification: 0.755895 Loss T 0.088423 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 2.3319, Accuracy: 592/1134 F1 (52.2046%)
Test set: Average loss: 2.1674, Accuracy: 13838/23808 F1 (58.1233%)
Val set: Average loss: 2.1222, Accuracy: 208/360 F1 (57.7778%)
Patience Reset, Counter is: 1
best acc test 58.123320 acc val 57.777778 acc labeled target 52.204586
S real T sketch Train Ep: 2100 lr0.008667841720414475 Loss Classification: 0.436899 Loss T 0.083800 Method MME
S real T sketch Train Ep: 2200 lr0.008614501024650454 Loss Classification: 0.515443 Loss T 0.093333 Method MME
S real T sketch Train Ep: 2300 lr0.008561920016164943 Loss Classification: 0.476100 Loss T 0.082788 Method MME
S real T sketch Train Ep: 2400 lr0.008510081829966844 Loss Classification: 1.322358 Loss T 0.087003 Method MME
S real T sketch Train Ep: 2500 lr0.008458970107524513 Loss Classification: 0.764887 Loss T 0.087047 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 2.3328, Accuracy: 618/1134 F1 (54.4974%)
Test set: Average loss: 2.2374, Accuracy: 13956/23808 F1 (58.6189%)
Val set: Average loss: 2.1539, Accuracy: 210/360 F1 (58.3333%)
Patience Reset, Counter is: 0
best acc test 58.618952 acc val 58.333333 acc labeled target 54.497354
S real T sketch Train Ep: 2600 lr0.008408568977653933 Loss Classification: 0.794446 Loss T 0.080152 Method MME
S real T sketch Train Ep: 2700 lr0.00835886303827305 Loss Classification: 0.624208 Loss T 0.092888 Method MME
S real T sketch Train Ep: 2800 lr0.008309837338976545 Loss Classification: 0.589792 Loss T 0.101564 Method MME
S real T sketch Train Ep: 2900 lr0.008261477364388068 Loss Classification: 0.367060 Loss T 0.094524 Method MME
Per Class Accuracy Calculated According to the Labelled Target examples is: [0.8888889 1. 0.6666667 0.6666667 1. 0.33333334
0.11111111 1. 0.6666667 0.11111111 0.6666667 1.
0. 0. 0.33333334 0. 0. 1.
0.7777778 0.44444445 0.6666667 1. 0.33333334 0.5555556
0.33333334 0.33333334 0. 0.6666667 0.5555556 0.11111111
0.33333334 0. 0.5555556 0.7777778 0.8888889 0.22222222
0.11111111 0. 0.6666667 0.8888889 0.33333334 0.8888889
0.6666667 0.7777778 0.22222222 0.6666667 1. 0.8888889
0.33333334 1. 0.33333334 0.22222222 0.8888889 0.8888889
1. 0.33333334 0.5555556 0.6666667 1. 0.7777778
0.5555556 0.33333334 1. 0.33333334 0.44444445 0.5555556
0. 0.6666667 0.33333334 1. 0.5555556 0.6666667
0.11111111 0.22222222 1. 0.33333334 0. 0.6666667
1. 0.6666667 0. 0.8888889 0.5555556 0.7777778
0.33333334 0.8888889 1. 0.6666667 0. 0.44444445
0.44444445 0.7777778 0.6666667 0.5555556 0.7777778 0.5555556
0.6666667 0.8888889 0.11111111 0.33333334 1. 0.22222222
0.6666667 0.7777778 0.6666667 0. 0.33333334 0.44444445
0. 0.33333334 0.33333334 0.6666667 0.8888889 1.
0.7777778 0.7777778 0.44444445 0.6666667 0. 0.
1. 1. 0.7777778 0. 0. 1. ]
Top k classes which perform poorly are: [108, 119, 16, 15, 13, 12, 31, 26, 37, 105, 66, 76, 80, 88, 123, 124, 118, 36, 72, 29, 98, 6, 9, 35, 44, 73, 101, 51, 48, 55, 61, 63, 99, 40, 109, 68, 110, 30, 14, 75, 106, 25, 24, 84, 22, 5, 50, 90, 64, 89, 107, 19, 116, 95, 82, 65, 32, 28, 60, 70, 93, 56, 23, 111, 104, 79, 3, 102, 71, 77, 27, 67, 38, 42, 87, 8, 45, 92, 57, 117, 96, 20, 10, 2, 91, 103, 94, 115, 83, 122, 18, 33, 114, 43, 59, 53, 52, 47, 97, 85, 41, 39, 34, 112, 81, 0, 113, 120, 121, 62, 86, 78, 74, 69, 58, 54, 49, 46, 21, 17, 11, 7, 4, 1, 100, 125]
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