基于深度学习的android端交通牌标志检测与识别.zip

I0224 11:51:36.653954 25595 solver.cpp:331] Iteration 9400, Testing net (#0) I0224 11:51:38.858273 25595 solver.cpp:398] Test net output #0: accuracy = 0.963293 I0224 11:51:38.858311 25595 solver.cpp:398] Test net output #1: loss = 0.1712 (* 1 = 0.1712 loss) I0224 11:51:38.922637 25595 solver.cpp:219] Iteration 9400 (11.6727 iter/s, 8.567s/100 iters), loss = 0.140704 I0224 11:51:38.922677 25595 solver.cpp:238] Train net output #0: loss = 0.140704 (* 1 = 0.140704 loss) I0224 11:51:38.922686 25595 sgd_solver.cpp:105] Iteration 9400, lr = 0.00573211 I0224 11:51:45.216907 25595 solver.cpp:448] Snapshotting to binary proto file /home/ren/pycharm_project/caffe_trafficSign/mytrain/_iter_9500.caffemodel I0224 11:51:45.218072 25595 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /home/ren/pycharm_project/caffe_trafficSign/mytrain/_iter_9500.solverstate I0224 11:51:45.218612 25595 solver.cpp:331] Iteration 9500, Testing net (#0) I0224 11:51:47.412976 25595 solver.cpp:398] Test net output #0: accuracy = 0.96439 I0224 11:51:47.413017 25595 solver.cpp:398] Test net output #1: loss = 0.176284 (* 1 = 0.176284 loss) I0224 11:51:47.480135 25595 solver.cpp:219] Iteration 9500 (11.6863 iter/s, 8.557s/100 iters), loss = 0.065421 I0224 11:51:47.480170 25595 solver.cpp:238] Train net output #0: loss = 0.0654209 (* 1 = 0.0654209 loss) I0224 11:51:47.480178 25595 sgd_solver.cpp:105] Iteration 9500, lr = 0.00566947 I0224 11:51:53.933868 25595 solver.cpp:448] Snapshotting to binary proto file /home/ren/pycharm_project/caffe_trafficSign/mytrain/_iter_9600.caffemodel I0224 11:51:53.935106 25595 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /home/ren/pycharm_project/caffe_trafficSign/mytrain/_iter_9600.solverstate I0224 11:51:53.935675 25595 solver.cpp:331] Iteration 9600, Testing net (#0) I0224 11:51:56.189111 25595 solver.cpp:398] Test net output #0: accuracy = 0.961098 I0224 11:51:56.189184 25595 solver.cpp:398] Test net output #1: loss = 0.180788 (* 1 = 0.180788 loss) I0224 11:51:56.254528 25595 solver.cpp:219] Iteration 9600 (11.3973 iter/s, 8.774s/100 iters), loss = 0.136458 I0224 11:51:56.254570 25595 solver.cpp:238] Train net output #0: loss = 0.136457 (* 1 = 0.136457 loss) I0224 11:51:56.254580 25595 sgd_solver.cpp:105] Iteration 9600, lr = 0.00560612 I0224 11:52:02.674209 25595 solver.cpp:448] Snapshotting to binary proto file /home/ren/pycharm_project/caffe_trafficSign/mytrain/_iter_9700.caffemodel I0224 11:52:02.675360 25595 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /home/ren/pycharm_project/caffe_trafficSign/mytrain/_iter_9700.solverstate I0224 11:52:02.675863 25595 solver.cpp:331] Iteration 9700, Testing net (#0) I0224 11:52:04.933218 25595 solver.cpp:398] Test net output #0: accuracy = 0.961829 I0224 11:52:04.933254 25595 solver.cpp:398] Test net output #1: loss = 0.171502 (* 1 = 0.171502 loss) I0224 11:52:05.002348 25595 solver.cpp:219] Iteration 9700 (11.4325 iter/s, 8.747s/100 iters), loss = 0.0891743 I0224 11:52:05.002421 25595 solver.cpp:238] Train net output #0: loss = 0.0891742 (* 1 = 0.0891742 loss) I0224 11:52:05.002434 25595 sgd_solver.cpp:105] Iteration 9700, lr = 0.00554205 I0224 11:52:11.386101 25595 solver.cpp:448] Snapshotting to binary proto file /home/ren/pycharm_project/caffe_trafficSign/mytrain/_iter_9800.caffemodel I0224 11:52:11.387279 25595 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /home/ren/pycharm_project/caffe_trafficSign/mytrain/_iter_9800.solverstate I0224 11:52:11.387799 25595 solver.cpp:331] Iteration 9800, Testing net (#0) I0224 11:52:13.657847 25595 solver.cpp:398] Test net output #0: accuracy = 0.962561 I0224 11:52:13.657891 25595 solver.cpp:398] Test net output #1: loss = 0.158055 (* 1 = 0.158055 loss) I0224 11:52:13.724700 25595 solver.cpp:219] Iteration 9800 (11.4653 iter/s, 8.722s/100 iters), loss = 0.123472 I0224 11:52:13.724762 25595 solver.cpp:238] Train net output #0: loss = 0.123472 (* 1 = 0.123472 loss) I0224 11:52:13.724782 25595 sgd_solver.cpp:105] Iteration 9800, lr = 0.00547723 I0224 11:52:20.166818 25595 solver.cpp:448] Snapshotting to binary proto file /home/ren/pycharm_project/caffe_trafficSign/mytrain/_iter_9900.caffemodel I0224 11:52:20.168208 25595 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /home/ren/pycharm_project/caffe_trafficSign/mytrain/_iter_9900.solverstate I0224 11:52:20.168875 25595 solver.cpp:331] Iteration 9900, Testing net (#0) I0224 11:52:22.450837 25595 solver.cpp:398] Test net output #0: accuracy = 0.961585 I0224 11:52:22.450875 25595 solver.cpp:398] Test net output #1: loss = 0.175985 (* 1 = 0.175985 loss) I0224 11:52:22.517632 25595 solver.cpp:219] Iteration 9900 (11.374 iter/s, 8.792s/100 iters), loss = 0.123995 I0224 11:52:22.517673 25595 solver.cpp:238] Train net output #0: loss = 0.123995 (* 1 = 0.123995 loss) I0224 11:52:22.517681 25595 sgd_solver.cpp:105] Iteration 9900, lr = 0.00541163 I0224 11:52:28.990134 25595 solver.cpp:448] Snapshotting to binary proto file /home/ren/pycharm_project/caffe_trafficSign/mytrain/_iter_10000.caffemodel I0224 11:52:28.991775 25595 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /home/ren/pycharm_project/caffe_trafficSign/mytrain/_iter_10000.solverstate I0224 11:52:28.992301 25595 solver.cpp:331] Iteration 10000, Testing net (#0) I0224 11:52:31.183526 25595 solver.cpp:398] Test net output #0: accuracy = 0.964146 I0224 11:52:31.183593 25595 solver.cpp:398] Test net output #1: loss = 0.173524 (* 1 = 0.173524 loss) I0224 11:52:31.246228 25595 solver.cpp:219] Iteration 10000 (11.4574 iter/s, 8.728s/100 iters), loss = 0.100884 I0224 11:52:31.246285 25595 solver.cpp:238] Train net output #0: loss = 0.100884 (* 1 = 0.100884 loss) I0224 11:52:31.246297 25595 sgd_solver.cpp:105] Iteration 10000, lr = 0.00534522 I0224 11:52:37.558666 25595 solver.cpp:448] Snapshotting to binary proto file /home/ren/pycharm_project/caffe_trafficSign/mytrain/_iter_10100.caffemodel I0224 11:52:37.560303 25595 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /home/ren/pycharm_project/caffe_trafficSign/mytrain/_iter_10100.solverstate I0224 11:52:37.561098 25595 solver.cpp:331] Iteration 10100, Testing net (#0) I0224 11:52:39.830718 25595 solver.cpp:398] Test net output #0: accuracy = 0.966342 I0224 11:52:39.830768 25595 solver.cpp:398] Test net output #1: loss = 0.153909 (* 1 = 0.153909 loss) I0224 11:52:39.902479 25595 solver.cpp:219] Iteration 10100 (11.5527 iter/s, 8.656s/100 iters), loss = 0.0749842 I0224 11:52:39.902521 25595 solver.cpp:238] Train net output #0: loss = 0.0749841 (* 1 = 0.0749841 loss) I0224 11:52:39.902530 25595 sgd_solver.cpp:105] Iteration 10100, lr = 0.00527799 I0224 11:52:46.389170 25595 solver.cpp:448] Snapshotting to binary proto file /home/ren/pycharm_project/caffe_trafficSign/mytrain/_iter_10200.caffemodel I0224 11:52:46.390403 25595 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /home/ren/pycharm_project/caffe_trafficSign/mytrain/_iter_10200.solverstate I0224 11:52:46.390944 25595 solver.cpp:331] Iteration 10200, Testing net (#0) I0224 11:52:48.966071 25595 solver.cpp:398] Test net output #0: accuracy = 0.96439 I0224 11:52:48.966119 25595 solver.cpp:398] Test net output #1: loss = 0.160011 (* 1 = 0.160011 loss) I0224 11:52:49.034940 25595 solver.cpp:219] Iteration 10200 (10.9505 iter/s, 9.132s/100 iters), loss = 0.0955413 I0224 11:52:49.034979 25595 solver.cpp:238] Train net output #0: loss = 0.0955413 (* 1 = 0.0955413 loss) I0224 11:52:49.034989 25595 sgd_solver.cpp:105] Iteration 10200, lr = 0.00520988 I0224 11:52:55.326913 25595 solver.cpp:448] Snapshotting to binary proto file /home/ren/pycharm_project/caffe_trafficSign/mytrain/_iter_10300.caffemodel I0224 11:52:55.328250 25595 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /home/ren/pycharm_project/caffe_traff