Closed T109318049 closed 3 years ago
When I train the Caltech code from https://github.com/dominikandreas/CSP, the records show the loss value is very high. I think that the value is the abnormal situation from cls_center, shown as below. But I have no idea to solve this issue. The records from train_Caltech are shown as below: "total loss" "cls" "regr_h" "offset" 0.146088 0.115322 0.009933 0.020833 0.098287 0.087741 0.002239 0.008307 0.071353 0.062760 0.001668 0.006925 0.081938 0.073539 0.001938 0.006461 0.073801 0.065529 0.001339 0.006932 0.072089 0.064259 0.001209 0.006621 0.067687 0.059619 0.001296 0.006772 0.064946 0.057368 0.001091 0.006488 0.061354 0.054006 0.001062 0.006286 0.061342 0.053803 0.001177 0.006362 0.061981 0.054710 0.001086 0.006185 0.058011 0.050864 0.001055 0.006091 0.055380 0.048392 0.001013 0.005975 0.059276 0.052155 0.001003 0.006118 0.051897 0.045055 0.000858 0.005984 0.052011 0.045238 0.000839 0.005935 0.055235 0.048431 0.000995 0.005809 0.053121 0.046175 0.000810 0.006136 0.054537 0.047586 0.001055 0.005896 0.050895 0.044234 0.000816 0.005845 0.051259 0.044498 0.000852 0.005909 0.050222 0.043484 0.000812 0.005926 0.049202 0.042674 0.000820 0.005707 0.047568 0.041040 0.000794 0.005733 0.045857 0.039506 0.000708 0.005643 0.049599 0.043301 0.000772 0.005527 0.046297 0.040108 0.000747 0.005442 0.045776 0.039386 0.000682 0.005708 0.047359 0.041113 0.000751 0.005495 0.044618 0.038325 0.000724 0.005569 0.044197 0.037804 0.000705 0.005688 0.043063 0.036738 0.000680 0.005645 0.044264 0.038139 0.000707 0.005418 0.041878 0.035751 0.000642 0.005485 0.044452 0.038305 0.000699 0.005448 0.042483 0.036077 0.000710 0.005695 0.044659 0.038537 0.000726 0.005396 0.041108 0.035030 0.000636 0.005442 0.040449 0.034342 0.000665 0.005442 0.040523 0.034651 0.000586 0.005286 0.040542 0.034645 0.000655 0.005242 0.039925 0.033994 0.000614 0.005317 0.040263 0.034208 0.000649 0.005406 0.039177 0.033443 0.000596 0.005138 0.037185 0.031399 0.000609 0.005177 0.041520 0.035570 0.000676 0.005274 0.039260 0.033446 0.000643 0.005171 0.038458 0.032674 0.000604 0.005180 0.038988 0.033193 0.000586 0.005209 0.038271 0.032380 0.000574 0.005317 0.037493 0.031569 0.000649 0.005276 0.038233 0.032616 0.000627 0.004990 0.037209 0.031312 0.000611 0.005286 0.038365 0.032619 0.000623 0.005123 0.036683 0.030863 0.000534 0.005286 0.036462 0.030927 0.000547 0.004988 0.035886 0.030214 0.000565 0.005108 0.037315 0.031443 0.000540 0.005332 0.035665 0.029956 0.000580 0.005128 0.036883 0.031298 0.000569 0.005016 0.036175 0.030355 0.000578 0.005241 0.034284 0.028385 0.000520 0.005379 0.034460 0.028736 0.000503 0.005221 0.034561 0.028825 0.000544 0.005192 0.035108 0.029526 0.000535 0.005047 0.033300 0.027814 0.000516 0.004970 0.035685 0.030010 0.000626 0.005049 0.034709 0.029089 0.000552 0.005067 0.032829 0.027182 0.000552 0.005095 0.034950 0.029222 0.000549 0.005179 0.035355 0.029635 0.000521 0.005199 0.033097 0.027575 0.000506 0.005016 0.034409 0.028776 0.000539 0.005094 0.034374 0.028723 0.000560 0.005091 0.033462 0.028019 0.000502 0.004941 0.031856 0.026466 0.000501 0.004889 0.034407 0.028888 0.000542 0.004977 0.033637 0.028005 0.000525 0.005106 0.032011 0.026455 0.000506 0.005049 0.032594 0.027016 0.000507 0.005072 0.031713 0.026079 0.000506 0.005128 0.032270 0.026873 0.000550 0.004847 0.033212 0.027798 0.000508 0.004907 0.033275 0.027806 0.000561 0.004908 0.031593 0.026026 0.000524 0.005043 0.033019 0.027241 0.000543 0.005235 0.031242 0.025833 0.000487 0.004922 0.033085 0.027534 0.000545 0.005007 0.032539 0.027065 0.000514 0.004960 0.031214 0.025757 0.000496 0.004960 0.031400 0.026045 0.000475 0.004880 0.030927 0.025401 0.000532 0.004995 0.031312 0.025998 0.000474 0.004839 0.031789 0.026305 0.000497 0.004988 0.031681 0.026299 0.000518 0.004863 0.029155 0.023938 0.000507 0.004711 0.028076 0.022962 0.000447 0.004667 0.030467 0.025188 0.000475 0.004803 0.031895 0.026434 0.000499 0.004961 0.030741 0.025282 0.000505 0.004954 0.030523 0.025147 0.000490 0.004887 0.031197 0.025962 0.000495 0.004740 0.028096 0.022867 0.000463 0.004767 0.029270 0.023728 0.000469 0.005072 0.030056 0.024727 0.000470 0.004859 0.029694 0.024355 0.000457 0.004883 0.029383 0.024017 0.000482 0.004884 0.029807 0.024387 0.000506 0.004914 0.030971 0.025645 0.000495 0.004831 0.031964 0.026566 0.000506 0.004892 0.030898 0.025541 0.000504 0.004853 0.029556 0.024205 0.000474 0.004878 0.028333 0.023096 0.000453 0.004784 0.028542 0.023254 0.000438 0.004850 0.028839 0.023586 0.000491 0.004762 0.029530 0.024289 0.000471 0.004770 0.030640 0.025408 0.000454 0.004779 0.030001 0.024737 0.000458 0.004806 0.029234 0.023925 0.000518 0.004791 0.028570 0.023454 0.000488 0.004627
Here is the version from the modules: Python:3.6 Keras:2.0.8 Tensorflow: 1.14.0 py-OpenCV: 3.4.2
If anyone knows what the reason is, please answer me, thanks!
When I train the Caltech code from https://github.com/dominikandreas/CSP, the records show the loss value is very high. I think that the value is the abnormal situation from cls_center, shown as below. But I have no idea to solve this issue. The records from train_Caltech are shown as below: "total loss" "cls" "regr_h" "offset" 0.146088 0.115322 0.009933 0.020833 0.098287 0.087741 0.002239 0.008307 0.071353 0.062760 0.001668 0.006925 0.081938 0.073539 0.001938 0.006461 0.073801 0.065529 0.001339 0.006932 0.072089 0.064259 0.001209 0.006621 0.067687 0.059619 0.001296 0.006772 0.064946 0.057368 0.001091 0.006488 0.061354 0.054006 0.001062 0.006286 0.061342 0.053803 0.001177 0.006362 0.061981 0.054710 0.001086 0.006185 0.058011 0.050864 0.001055 0.006091 0.055380 0.048392 0.001013 0.005975 0.059276 0.052155 0.001003 0.006118 0.051897 0.045055 0.000858 0.005984 0.052011 0.045238 0.000839 0.005935 0.055235 0.048431 0.000995 0.005809 0.053121 0.046175 0.000810 0.006136 0.054537 0.047586 0.001055 0.005896 0.050895 0.044234 0.000816 0.005845 0.051259 0.044498 0.000852 0.005909 0.050222 0.043484 0.000812 0.005926 0.049202 0.042674 0.000820 0.005707 0.047568 0.041040 0.000794 0.005733 0.045857 0.039506 0.000708 0.005643 0.049599 0.043301 0.000772 0.005527 0.046297 0.040108 0.000747 0.005442 0.045776 0.039386 0.000682 0.005708 0.047359 0.041113 0.000751 0.005495 0.044618 0.038325 0.000724 0.005569 0.044197 0.037804 0.000705 0.005688 0.043063 0.036738 0.000680 0.005645 0.044264 0.038139 0.000707 0.005418 0.041878 0.035751 0.000642 0.005485 0.044452 0.038305 0.000699 0.005448 0.042483 0.036077 0.000710 0.005695 0.044659 0.038537 0.000726 0.005396 0.041108 0.035030 0.000636 0.005442 0.040449 0.034342 0.000665 0.005442 0.040523 0.034651 0.000586 0.005286 0.040542 0.034645 0.000655 0.005242 0.039925 0.033994 0.000614 0.005317 0.040263 0.034208 0.000649 0.005406 0.039177 0.033443 0.000596 0.005138 0.037185 0.031399 0.000609 0.005177 0.041520 0.035570 0.000676 0.005274 0.039260 0.033446 0.000643 0.005171 0.038458 0.032674 0.000604 0.005180 0.038988 0.033193 0.000586 0.005209 0.038271 0.032380 0.000574 0.005317 0.037493 0.031569 0.000649 0.005276 0.038233 0.032616 0.000627 0.004990 0.037209 0.031312 0.000611 0.005286 0.038365 0.032619 0.000623 0.005123 0.036683 0.030863 0.000534 0.005286 0.036462 0.030927 0.000547 0.004988 0.035886 0.030214 0.000565 0.005108 0.037315 0.031443 0.000540 0.005332 0.035665 0.029956 0.000580 0.005128 0.036883 0.031298 0.000569 0.005016 0.036175 0.030355 0.000578 0.005241 0.034284 0.028385 0.000520 0.005379 0.034460 0.028736 0.000503 0.005221 0.034561 0.028825 0.000544 0.005192 0.035108 0.029526 0.000535 0.005047 0.033300 0.027814 0.000516 0.004970 0.035685 0.030010 0.000626 0.005049 0.034709 0.029089 0.000552 0.005067 0.032829 0.027182 0.000552 0.005095 0.034950 0.029222 0.000549 0.005179 0.035355 0.029635 0.000521 0.005199 0.033097 0.027575 0.000506 0.005016 0.034409 0.028776 0.000539 0.005094 0.034374 0.028723 0.000560 0.005091 0.033462 0.028019 0.000502 0.004941 0.031856 0.026466 0.000501 0.004889 0.034407 0.028888 0.000542 0.004977 0.033637 0.028005 0.000525 0.005106 0.032011 0.026455 0.000506 0.005049 0.032594 0.027016 0.000507 0.005072 0.031713 0.026079 0.000506 0.005128 0.032270 0.026873 0.000550 0.004847 0.033212 0.027798 0.000508 0.004907 0.033275 0.027806 0.000561 0.004908 0.031593 0.026026 0.000524 0.005043 0.033019 0.027241 0.000543 0.005235 0.031242 0.025833 0.000487 0.004922 0.033085 0.027534 0.000545 0.005007 0.032539 0.027065 0.000514 0.004960 0.031214 0.025757 0.000496 0.004960 0.031400 0.026045 0.000475 0.004880 0.030927 0.025401 0.000532 0.004995 0.031312 0.025998 0.000474 0.004839 0.031789 0.026305 0.000497 0.004988 0.031681 0.026299 0.000518 0.004863 0.029155 0.023938 0.000507 0.004711 0.028076 0.022962 0.000447 0.004667 0.030467 0.025188 0.000475 0.004803 0.031895 0.026434 0.000499 0.004961 0.030741 0.025282 0.000505 0.004954 0.030523 0.025147 0.000490 0.004887 0.031197 0.025962 0.000495 0.004740 0.028096 0.022867 0.000463 0.004767 0.029270 0.023728 0.000469 0.005072 0.030056 0.024727 0.000470 0.004859 0.029694 0.024355 0.000457 0.004883 0.029383 0.024017 0.000482 0.004884 0.029807 0.024387 0.000506 0.004914 0.030971 0.025645 0.000495 0.004831 0.031964 0.026566 0.000506 0.004892 0.030898 0.025541 0.000504 0.004853 0.029556 0.024205 0.000474 0.004878 0.028333 0.023096 0.000453 0.004784 0.028542 0.023254 0.000438 0.004850 0.028839 0.023586 0.000491 0.004762 0.029530 0.024289 0.000471 0.004770 0.030640 0.025408 0.000454 0.004779 0.030001 0.024737 0.000458 0.004806 0.029234 0.023925 0.000518 0.004791 0.028570 0.023454 0.000488 0.004627
Here is the version from the modules: Python:3.6 Keras:2.0.8 Tensorflow: 1.14.0 py-OpenCV: 3.4.2
If anyone knows what the reason is, please answer me, thanks!