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ssv2_large_aq_1clip.log
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ssv2_large_aq_1clip.log
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=> dataset: somethingv2
=> root_path: /tmp/
=> num_classes: 174
Initializing RubiksNet-Large variant "rubiks3d-aq". num_frames=8
RubiksNet attention shift (before block.conv2)
=> setup forward prop for temporal attention shift
=> tier: large
=> variant: attention
=> eval mode: 1-clip
video 0 done, total 0/24777, average 0.428 sec/video, moving Prec@1 60.000 Prec@5 85.000
video 1600 done, total 1600/24777, average 0.027 sec/video, moving Prec@1 58.571 Prec@5 82.619
video 3200 done, total 3200/24777, average 0.017 sec/video, moving Prec@1 59.116 Prec@5 83.567
video 4800 done, total 4800/24777, average 0.014 sec/video, moving Prec@1 58.361 Prec@5 83.484
video 6400 done, total 6400/24777, average 0.012 sec/video, moving Prec@1 58.225 Prec@5 83.534
video 8000 done, total 8000/24777, average 0.011 sec/video, moving Prec@1 58.082 Prec@5 83.688
video 9600 done, total 9600/24777, average 0.011 sec/video, moving Prec@1 58.264 Prec@5 83.833
video 11200 done, total 11200/24777, average 0.010 sec/video, moving Prec@1 58.360 Prec@5 84.105
video 12800 done, total 12800/24777, average 0.010 sec/video, moving Prec@1 58.408 Prec@5 84.270
video 14400 done, total 14400/24777, average 0.010 sec/video, moving Prec@1 58.384 Prec@5 84.482
video 16000 done, total 16000/24777, average 0.009 sec/video, moving Prec@1 58.420 Prec@5 84.397
video 17600 done, total 17600/24777, average 0.009 sec/video, moving Prec@1 58.331 Prec@5 84.423
video 19200 done, total 19200/24777, average 0.009 sec/video, moving Prec@1 58.351 Prec@5 84.393
video 20800 done, total 20800/24777, average 0.009 sec/video, moving Prec@1 58.458 Prec@5 84.468
video 22400 done, total 22400/24777, average 0.009 sec/video, moving Prec@1 58.465 Prec@5 84.591
video 24000 done, total 24000/24777, average 0.009 sec/video, moving Prec@1 58.493 Prec@5 84.564
====================== Evaluation Complete ======================
Class confusion matrix:
[0.88793103 0.42763158 0.37908497 0.61165049 0.51282051 0.58333333
0.75539568 0.60606061 0.52755906 0.66887417 0.69101124 0.64233577
0.32478632 0.25 0.72631579 0.44255319 0.31979695 0.38157895
0.46443515 0.60189573 0.54330709 0.59649123 0.44767442 0.5106383
0.56818182 0.52631579 0.525 0.45544554 0.69090909 0.74329502
0.83783784 0.76576577 0.79545455 0.16504854 0.14457831 0.0625
0.8156682 0.79087452 0.16666667 0.70588235 0.71038251 0.58928571
0.65258216 0.59807074 0.64864865 0.54874652 0.55421687 0.46733668
0.40372671 0.82150538 0.70886076 0.06666667 0.25 0.62857143
0.41666667 0.37333333 0.40410959 0.58490566 0.11764706 0.73741007
0.84 0.21568627 0.75949367 0.21495327 0.18918919 0.64150943
0.31132075 0.47761194 0.68421053 0.39 0.51785714 0.43478261
0.55882353 0.56410256 0.66981132 0.26315789 0.27777778 0.42307692
0.25925926 0.72413793 0.390625 0.16 0.34042553 0.29787234
0.65333333 0.46031746 0.78823529 0.832 0.25 0.13253012
0.39130435 0.85 0.56140351 0.756 0.75423729 0.23611111
0.11764706 0.19008264 0.58673469 0.67399267 0.4754717 0.68131868
0.28448276 0.66071429 0.71653543 0.76296296 0.56164384 0.63546798
0.37837838 0.49666667 0.6056338 0.05555556 0.48920863 0.38888889
0.70486111 0.5 0.44444444 0.79365079 0.3559322 0.69306931
0.76377953 0.78409091 0.27093596 0.63076923 0.40384615 0.60550459
0.48543689 0.49565217 0.16475096 0.65808824 0.33571429 0.17808219
0.3908046 0.19298246 0.81793478 0.51533742 0.23529412 0.53333333
0.2293578 0.74641148 0.68992248 0.40625 0.484375 0.53571429
0.57961783 0.48328267 0.77222222 0.078125 0.66945607 0.91509434
0.74456522 0.38043478 0.35714286 0.82568807 0.58267717 0.18292683
0.43076923 0.53804348 0.55504587 0.30909091 0.30275229 0.36842105
0.43925234 0.475 0.84398977 0.75806452 0.92142857 0.91358025
0.81666667 0.77333333 0.51470588 0.78525641 0.70232558 0.78832117]
Accuracy: top 1: 58.57% top 5: 84.65%