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thersh_0.8.out
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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: 0.8
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: 4.989935 Loss T 0.469755 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 5.0011, Accuracy: 12/1134 F1 (1.0582%)
Test set: Average loss: 4.9799, Accuracy: 265/23808 F1 (1.1131%)
Val set: Average loss: 4.9707, Accuracy: 5/360 F1 (1.3889%)
Patience Reset, Counter is: 0
best acc test 1.113071 acc val 1.388889 acc labeled target 1.058201
S real T sketch Train Ep: 100 lr0.009925650290240803 Loss Classification: 0.711050 Loss T 0.268999 Method MME
S real T sketch Train Ep: 200 lr0.009852577760521605 Loss Classification: 0.752390 Loss T 0.244442 Method MME
S real T sketch Train Ep: 300 lr0.009780748269686728 Loss Classification: 0.920878 Loss T 0.179999 Method MME
S real T sketch Train Ep: 400 lr0.009710128909124701 Loss Classification: 1.037874 Loss T 0.180441 Method MME
S real T sketch Train Ep: 500 lr0.00964068794694323 Loss Classification: 1.235989 Loss T 0.132995 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 2.5179, Accuracy: 524/1134 F1 (46.2081%)
Test set: Average loss: 2.3976, Accuracy: 11806/23808 F1 (49.5884%)
Val set: Average loss: 2.3172, Accuracy: 185/360 F1 (51.3889%)
Patience Reset, Counter is: 0
best acc test 49.588374 acc val 51.388889 acc labeled target 46.208113
S real T sketch Train Ep: 600 lr0.00957239477517603 Loss Classification: 0.540173 Loss T 0.161657 Method MME
S real T sketch Train Ep: 700 lr0.009505219859830012 Loss Classification: 0.683260 Loss T 0.147163 Method MME
S real T sketch Train Ep: 800 lr0.009439134693595126 Loss Classification: 0.673175 Loss T 0.103430 Method MME
S real T sketch Train Ep: 900 lr0.009374111751051751 Loss Classification: 0.563854 Loss T 0.118284 Method MME
S real T sketch Train Ep: 1000 lr0.009310124446222227 Loss Classification: 0.943432 Loss T 0.141808 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 2.4144, Accuracy: 580/1134 F1 (51.1464%)
Test set: Average loss: 2.2344, Accuracy: 12993/23808 F1 (54.5741%)
Val set: Average loss: 2.0522, Accuracy: 200/360 F1 (55.5556%)
Patience Reset, Counter is: 0
best acc test 54.574093 acc val 55.555556 acc labeled target 51.146384
S real T sketch Train Ep: 1100 lr0.00924714709232377 Loss Classification: 0.763934 Loss T 0.144378 Method MME
S real T sketch Train Ep: 1200 lr0.009185154863590003 Loss Classification: 0.720508 Loss T 0.134138 Method MME
S real T sketch Train Ep: 1300 lr0.00912412375903735 Loss Classification: 0.788016 Loss T 0.138802 Method MME
S real T sketch Train Ep: 1400 lr0.009064030568061049 Loss Classification: 0.891695 Loss T 0.127249 Method MME
S real T sketch Train Ep: 1500 lr0.009004852837753237 Loss Classification: 0.405985 Loss T 0.122927 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 2.3300, Accuracy: 605/1134 F1 (53.3510%)
Test set: Average loss: 2.1480, Accuracy: 13713/23808 F1 (57.5983%)
Val set: Average loss: 2.0511, Accuracy: 199/360 F1 (55.2778%)
Patience getting saturated, current counter is: 0
best acc test 54.574093 acc val 55.277778 acc labeled target 53.350970
S real T sketch Train Ep: 1600 lr0.008946568841842816 Loss Classification: 0.942468 Loss T 0.128922 Method MME
S real T sketch Train Ep: 1700 lr0.008889157551163433 Loss Classification: 0.980068 Loss T 0.109194 Method MME
S real T sketch Train Ep: 1800 lr0.008832598605562044 Loss Classification: 0.677776 Loss T 0.112355 Method MME
S real T sketch Train Ep: 1900 lr0.008776872287166303 Loss Classification: 0.328967 Loss T 0.100844 Method MME
Per Class Accuracy Calculated According to the Labelled Target examples is: [0.8888889 1. 0.6666667 0.6666667 0.6666667 0.22222222
0.33333334 0.6666667 0.7777778 0.33333334 0.8888889 0.6666667
0. 0. 0.8888889 0.5555556 1. 0.8888889
0.5555556 0.22222222 0.6666667 0.11111111 0.22222222 0.5555556
0.22222222 0.22222222 0. 0.8888889 0.6666667 0.11111111
0.33333334 0.33333334 0.22222222 0.11111111 0.8888889 0.33333334
0.22222222 0. 0.5555556 1. 0.33333334 0.6666667
0.44444445 0.22222222 0.5555556 0.6666667 1. 0.5555556
0.44444445 0.8888889 0.33333334 0.33333334 1. 1.
0.5555556 0.6666667 0.8888889 0.8888889 0.8888889 1.
1. 0.11111111 1. 0.33333334 1. 0.5555556
0. 0.6666667 0.22222222 0.8888889 0. 0.7777778
0.22222222 0.33333334 1. 0. 0.6666667 0.8888889
0.8888889 0.5555556 0. 0.44444445 0.11111111 0.22222222
0.33333334 0.44444445 1. 0.6666667 0. 0.11111111
0.33333334 0.7777778 0.6666667 0.5555556 0.7777778 0.5555556
0.8888889 0.8888889 0.22222222 0.5555556 0.7777778 0.44444445
0.6666667 0.44444445 0.6666667 0. 0.33333334 0.5555556
0. 0.44444445 0.44444445 1. 0.44444445 0.8888889
0.8888889 1. 0.22222222 0.6666667 0. 0.33333334
1. 0.8888889 0.6666667 0. 0. 0.8888889 ]
Top k classes which perform poorly are: [70, 26, 108, 80, 118, 105, 13, 75, 124, 12, 66, 37, 88, 123, 82, 61, 29, 89, 21, 33, 32, 116, 72, 36, 25, 19, 22, 43, 83, 98, 5, 68, 24, 51, 106, 50, 90, 63, 73, 40, 35, 119, 6, 31, 84, 9, 30, 101, 109, 110, 48, 103, 85, 42, 81, 112, 65, 15, 99, 107, 93, 54, 79, 95, 47, 44, 38, 18, 23, 76, 87, 45, 102, 2, 3, 4, 7, 122, 11, 117, 20, 41, 92, 55, 104, 67, 28, 71, 94, 100, 8, 91, 0, 114, 121, 113, 97, 125, 58, 49, 96, 17, 56, 57, 27, 34, 14, 10, 69, 77, 78, 16, 1, 120, 86, 111, 39, 46, 52, 53, 59, 60, 64, 74, 115, 62]
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Assigned Classwise weights to source
S real T sketch Train Ep: 2000 lr0.008721959494934213 Loss Classification: 0.708004 Loss T 0.109882 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 2.2069, Accuracy: 617/1134 F1 (54.4092%)
Test set: Average loss: 2.0963, Accuracy: 13842/23808 F1 (58.1401%)
Val set: Average loss: 1.9719, Accuracy: 203/360 F1 (56.3889%)
Patience Reset, Counter is: 1
best acc test 58.140121 acc val 56.388889 acc labeled target 54.409171
S real T sketch Train Ep: 2100 lr0.008667841720414475 Loss Classification: 0.564980 Loss T 0.112202 Method MME
S real T sketch Train Ep: 2200 lr0.008614501024650454 Loss Classification: 1.044688 Loss T 0.104135 Method MME
S real T sketch Train Ep: 2300 lr0.008561920016164943 Loss Classification: 1.002910 Loss T 0.108713 Method MME
S real T sketch Train Ep: 2400 lr0.008510081829966844 Loss Classification: 0.590844 Loss T 0.095198 Method MME
S real T sketch Train Ep: 2500 lr0.008458970107524513 Loss Classification: 0.614740 Loss T 0.110156 Method MME
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tensor(1134.)
Labeled Target set: Average loss: 2.3210, Accuracy: 613/1134 F1 (54.0564%)
Test set: Average loss: 2.0956, Accuracy: 14253/23808 F1 (59.8664%)
Val set: Average loss: 2.0380, Accuracy: 209/360 F1 (58.0556%)
Patience Reset, Counter is: 0
best acc test 59.866431 acc val 58.055556 acc labeled target 54.056437
S real T sketch Train Ep: 2600 lr0.008408568977653933 Loss Classification: 0.581458 Loss T 0.108939 Method MME
S real T sketch Train Ep: 2700 lr0.00835886303827305 Loss Classification: 0.544113 Loss T 0.080986 Method MME
S real T sketch Train Ep: 2800 lr0.008309837338976545 Loss Classification: 0.875685 Loss T 0.058150 Method MME
S real T sketch Train Ep: 2900 lr0.008261477364388068 Loss Classification: 0.179079 Loss T 0.073367 Method MME
Per Class Accuracy Calculated According to the Labelled Target examples is: [0.8888889 1. 0.33333334 0.5555556 0.5555556 0.22222222
0.44444445 0.6666667 0.7777778 0. 1. 1.
0. 0. 0.6666667 0. 0.5555556 1.
0.5555556 0.44444445 0.6666667 0.7777778 0.33333334 0.33333334
0.44444445 0. 0. 1. 0.22222222 0.33333334
0.6666667 0.33333334 0.11111111 0.6666667 1. 0.
0.33333334 0. 0.8888889 1. 0.6666667 0.8888889
0.44444445 0.22222222 0.5555556 0.6666667 1. 1.
0.33333334 1. 0.33333334 0.22222222 1. 0.5555556
1. 0.33333334 0.7777778 0.6666667 1. 1.
1. 0.33333334 1. 0.33333334 0.44444445 0.8888889
0.11111111 0.6666667 0. 0.8888889 0. 0.5555556
0.6666667 0.33333334 0.8888889 0.33333334 0. 0.6666667
0.44444445 0.5555556 0. 0.6666667 0.22222222 0.33333334
0.5555556 0.5555556 0.8888889 0.6666667 0. 0.5555556
0.22222222 1. 0.6666667 1. 0.8888889 0.6666667
0.6666667 0.8888889 0. 0.11111111 1. 0.6666667
0.6666667 0.6666667 0.6666667 0. 0.33333334 0.5555556
0. 0.11111111 0.44444445 0.8888889 0.6666667 1.
0.7777778 1. 0. 0.6666667 0. 0.44444445
1. 0.6666667 0.8888889 0. 0. 0.8888889 ]
Top k classes which perform poorly are: [35, 68, 124, 105, 76, 108, 80, 116, 37, 118, 26, 25, 15, 88, 70, 12, 123, 13, 9, 98, 66, 109, 99, 32, 28, 90, 5, 51, 43, 82, 50, 36, 55, 83, 106, 31, 29, 61, 2, 23, 22, 63, 73, 75, 48, 64, 42, 110, 119, 6, 19, 24, 78, 16, 18, 89, 3, 85, 4, 71, 107, 53, 44, 79, 84, 101, 87, 92, 81, 72, 77, 95, 67, 7, 121, 14, 20, 30, 33, 102, 40, 117, 45, 96, 57, 104, 103, 112, 114, 21, 56, 8, 111, 122, 97, 0, 125, 94, 86, 74, 69, 65, 38, 41, 93, 115, 34, 120, 17, 11, 10, 1, 27, 39, 46, 47, 49, 52, 54, 58, 59, 60, 100, 91, 113, 62]
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