Faster-RCNN训练结果

I1002 20:18:56.525288 28265 net.cpp:228] conv5_relu5_0_split does not need backward computation.I1002 20:18:56.525290 28265 net.cpp:228] relu5 does not need backward computation.I1002 20:18:56.525291 28265 net.cpp:228] conv5 does not need backward computation.I1002 20:18:56.525292 28265 net.cpp:228] relu4 does not need backward computation.I1002 20:18:56.525295 28265 net.cpp:228] conv4 does not need backward computation.I1002 20:18:56.525296 28265 net.cpp:228] relu3 does not need backward computation.I1002 20:18:56.525297 28265 net.cpp:228] conv3 does not need backward computation.I1002 20:18:56.525300 28265 net.cpp:228] pool2 does not need backward computation.I1002 20:18:56.525302 28265 net.cpp:228] norm2 does not need backward computation.I1002 20:18:56.525305 28265 net.cpp:228] relu2 does not need backward computation.I1002 20:18:56.525307 28265 net.cpp:228] conv2 does not need backward computation.I1002 20:18:56.525310 28265 net.cpp:228] pool1 does not need backward computation.I1002 20:18:56.525311 28265 net.cpp:228] norm1 does not need backward computation.I1002 20:18:56.525313 28265 net.cpp:228] relu1 does not need backward computation.I1002 20:18:56.525315 28265 net.cpp:228] conv1 does not need backward computation.I1002 20:18:56.525316 28265 net.cpp:270] This network produces output bbox_predI1002 20:18:56.525318 28265 net.cpp:270] This network produces output cls_probI1002 20:18:56.525333 28265 net.cpp:283] Network initialization done.I1002 20:18:56.591675 28265 net.cpp:816] Ignoring source layer dataI1002 20:18:56.625249 28265 net.cpp:816] Ignoring source layer loss_clsI1002 20:18:56.625264 28265 net.cpp:816] Ignoring source layer loss_bboxI1002 20:18:56.625594 28265 net.cpp:816] Ignoring source layer silence_rpn_cls_scoreI1002 20:18:56.625598 28265 net.cpp:816] Ignoring source layer silence_rpn_bbox_predim_detect: 1/622 0.128s 0.000sim_detect: 2/622 0.080s 0.000sim_detect: 3/622 0.063s 0.000sim_detect: 4/622 0.055s 0.000sim_detect: 5/622 0.050s 0.000sim_detect: 6/622 0.047s 0.000sim_detect: 7/622 0.044s 0.000sim_detect: 8/622 0.042s 0.000sim_detect: 9/622 0.041s 0.000sim_detect: 10/622 0.040s 0.000sim_detect: 11/622 0.039s 0.000sim_detect: 12/622 0.038s 0.000sim_detect: 13/622 0.037s 0.000sim_detect: 14/622 0.037s 0.000sim_detect: 15/622 0.036s 0.000sim_detect: 16/622 0.036s 0.000sim_detect: 17/622 0.035s 0.000sim_detect: 18/622 0.035s 0.000sim_detect: 19/622 0.035s 0.000sim_detect: 20/622 0.034s 0.000sim_detect: 21/622 0.034s 0.000sim_detect: 22/622 0.034s 0.000sim_detect: 23/622 0.034s 0.000sim_detect: 24/622 0.033s 0.000sim_detect: 25/622 0.033s 0.000sim_detect: 26/622 0.033s 0.000sim_detect: 27/622 0.033s 0.000sim_detect: 28/622 0.033s 0.000sim_detect: 29/622 0.033s 0.000sim_detect: 30/622 0.033s 0.000sim_detect: 31/622 0.032s 0.000sim_detect: 32/622 0.032s 0.000sim_detect: 33/622 0.032s 0.000sim_detect: 34/622 0.032s 0.000sim_detect: 35/622 0.032s 0.000sim_detect: 36/622 0.032s 0.000sim_detect: 37/622 0.032s 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0.000sim_detect: 536/622 0.029s 0.000sim_detect: 537/622 0.029s 0.000sim_detect: 538/622 0.029s 0.000sim_detect: 539/622 0.029s 0.000sim_detect: 540/622 0.029s 0.000sim_detect: 541/622 0.029s 0.000sim_detect: 542/622 0.029s 0.000sim_detect: 543/622 0.029s 0.000sim_detect: 544/622 0.029s 0.000sim_detect: 545/622 0.029s 0.000sim_detect: 546/622 0.029s 0.000sim_detect: 547/622 0.029s 0.000sim_detect: 548/622 0.029s 0.000sim_detect: 549/622 0.029s 0.000sim_detect: 550/622 0.029s 0.000sim_detect: 551/622 0.029s 0.000sim_detect: 552/622 0.029s 0.000sim_detect: 553/622 0.029s 0.000sim_detect: 554/622 0.029s 0.000sim_detect: 555/622 0.029s 0.000sim_detect: 556/622 0.029s 0.000sim_detect: 557/622 0.029s 0.000sim_detect: 558/622 0.029s 0.000sim_detect: 559/622 0.029s 0.000sim_detect: 560/622 0.029s 0.000sim_detect: 561/622 0.029s 0.000sim_detect: 562/622 0.029s 0.000sim_detect: 563/622 0.029s 0.000sim_detect: 564/622 0.029s 0.000sim_detect: 565/622 0.029s 0.000sim_detect: 566/622 0.029s 0.000sim_detect: 567/622 0.029s 0.000sim_detect: 568/622 0.029s 0.000sim_detect: 569/622 0.029s 0.000sim_detect: 570/622 0.029s 0.000sim_detect: 571/622 0.029s 0.000sim_detect: 572/622 0.029s 0.000sim_detect: 573/622 0.029s 0.000sim_detect: 574/622 0.029s 0.000sim_detect: 575/622 0.029s 0.000sim_detect: 576/622 0.029s 0.000sim_detect: 577/622 0.029s 0.000sim_detect: 578/622 0.029s 0.000sim_detect: 579/622 0.029s 0.000sim_detect: 580/622 0.029s 0.000sim_detect: 581/622 0.029s 0.000sim_detect: 582/622 0.029s 0.000sim_detect: 583/622 0.029s 0.000sim_detect: 584/622 0.029s 0.000sim_detect: 585/622 0.029s 0.000sim_detect: 586/622 0.029s 0.000sim_detect: 587/622 0.029s 0.000sim_detect: 588/622 0.029s 0.000sim_detect: 589/622 0.029s 0.000sim_detect: 590/622 0.029s 0.000sim_detect: 591/622 0.029s 0.000sim_detect: 592/622 0.029s 0.000sim_detect: 593/622 0.029s 0.000sim_detect: 594/622 0.029s 0.000sim_detect: 595/622 0.029s 0.000sim_detect: 596/622 0.029s 0.000sim_detect: 597/622 0.029s 0.000sim_detect: 598/622 0.029s 0.000sim_detect: 599/622 0.029s 0.000sim_detect: 600/622 0.029s 0.000sim_detect: 601/622 0.029s 0.000sim_detect: 602/622 0.029s 0.000sim_detect: 603/622 0.029s 0.000sim_detect: 604/622 0.029s 0.000sim_detect: 605/622 0.029s 0.000sim_detect: 606/622 0.029s 0.000sim_detect: 607/622 0.029s 0.000sim_detect: 608/622 0.029s 0.000sim_detect: 609/622 0.029s 0.000sim_detect: 610/622 0.029s 0.000sim_detect: 611/622 0.029s 0.000sim_detect: 612/622 0.029s 0.000sim_detect: 613/622 0.029s 0.000sim_detect: 614/622 0.029s 0.000sim_detect: 615/622 0.029s 0.000sim_detect: 616/622 0.029s 0.000sim_detect: 617/622 0.029s 0.000sim_detect: 618/622 0.029s 0.000sim_detect: 619/622 0.029s 0.000sim_detect: 620/622 0.029s 0.000sim_detect: 621/622 0.029s 0.000sim_detect: 622/622 0.029s 0.000sEvaluating detectionsWriting obj VOC results fileVOC07 metric? YesReading annotation for 1/622Reading annotation for 101/622Reading annotation for 201/622Reading annotation for 301/622Reading annotation for 401/622Reading annotation for 501/622Reading annotation for 601/622Saving cached annotations to /home/py-faster-rcnn/data/VOCdevkit2007/annotations_cache/annots.pklAP for obj = 0.3467Mean AP = 0.3467~~~~~~~~Results:0.3470.347~~~~~~~~--------------------------------------------------------------Results computed with the **unofficial** Python eval code.Results should be very close to the official MATLAB eval code.Recompute with `./tools/reval.py --matlab ...` for your paper.-- Thanks, The Management--------------------------------------------------------------real0m34.935suser0m33.696ssys0m3.120s