This repository is developed on the basis of pyqt5, mainly through the log files generated during the running of Darknet, and then draw loss files.
the release version can be used only in Windows
when you run you darknet using scipts below:
./darknet detector train cfg/coco.data cfg/yolov3.cfg
The output will like this:
Resizing
320
Loaded: 0.502709 seconds
Region 30 Avg IOU: 0.453259, Class: 0.406288, Obj: 0.616954, No Obj: 0.530078, .5R: 0.500000, .75R: 0.333333, count: 6
Region 42 Avg IOU: 0.237628, Class: 0.556286, Obj: 0.263673, No Obj: 0.461042, .5R: 0.142857, .75R: 0.000000, count: 7
Region 54 Avg IOU: 0.244585, Class: 0.550149, Obj: 0.428019, No Obj: 0.469195, .5R: 0.074074, .75R: 0.037037, count: 27
Region 30 Avg IOU: 0.306451, Class: 0.559028, Obj: 0.413168, No Obj: 0.530804, .5R: 0.000000, .75R: 0.000000, count: 4
Region 42 Avg IOU: 0.262898, Class: 0.576616, Obj: 0.381279, No Obj: 0.460506, .5R: 0.111111, .75R: 0.000000, count: 9
Region 54 Avg IOU: 0.255984, Class: 0.518751, Obj: 0.423396, No Obj: 0.469569, .5R: 0.074074, .75R: 0.000000, count: 27
Region 30 Avg IOU: 0.324626, Class: 0.472797, Obj: 0.618393, No Obj: 0.529930, .5R: 0.285714, .75R: 0.000000, count: 7
Region 42 Avg IOU: 0.289221, Class: 0.723829, Obj: 0.541236, No Obj: 0.462390, .5R: 0.166667, .75R: 0.000000, count: 6
Region 54 Avg IOU: 0.237150, Class: 0.541031, Obj: 0.491277, No Obj: 0.469132, .5R: 0.045455, .75R: 0.000000, count: 22
Region 30 Avg IOU: 0.358145, Class: 0.339489, Obj: 0.425689, No Obj: 0.529440, .5R: 0.333333, .75R: 0.333333, count: 3
Region 42 Avg IOU: 0.391667, Class: 0.668793, Obj: 0.368548, No Obj: 0.461217, .5R: 0.428571, .75R: 0.000000, count: 7
Region 54 Avg IOU: 0.214031, Class: 0.543846, Obj: 0.334361, No Obj: 0.469448, .5R: 0.000000, .75R: 0.000000, count: 29
1: 562.584595, 562.584595 avg, 0.000000 rate, 0.553693 seconds, 32 images
Loaded: 0.936840 seconds
Region 30 Avg IOU: 0.210395, Class: 0.535263, Obj: 0.585981, No Obj: 0.530203, .5R: 0.000000, .75R: 0.000000, count: 5
Region 42 Avg IOU: 0.246853, Class: 0.507559, Obj: 0.499927, No Obj: 0.459885, .5R: 0.125000, .75R: 0.000000, count: 8
Region 54 Avg IOU: 0.155895, Class: 0.499203, Obj: 0.295966, No Obj: 0.468601, .5R: 0.047619, .75R: 0.000000, count: 42
Region 30 Avg IOU: 0.343129, Class: 0.493684, Obj: 0.606848, No Obj: 0.528431, .5R: 0.200000, .75R: 0.200000, count: 5
Region 42 Avg IOU: 0.282754, Class: 0.626201, Obj: 0.483792, No Obj: 0.459979, .5R: 0.000000, .75R: 0.000000, count: 8
Region 54 Avg IOU: 0.205155, Class: 0.482449, Obj: 0.359561, No Obj: 0.469626, .5R: 0.027778, .75R: 0.000000, count: 36
Region 30 Avg IOU: 0.218516, Class: 0.643676, Obj: 0.513226, No Obj: 0.530422, .5R: 0.000000, .75R: 0.000000, count: 5
Region 42 Avg IOU: 0.223242, Class: 0.580035, Obj: 0.488847, No Obj: 0.460264, .5R: 0.111111, .75R: 0.000000, count: 9
Region 54 Avg IOU: 0.151966, Class: 0.455435, Obj: 0.314113, No Obj: 0.468514, .5R: 0.052632, .75R: 0.000000, count: 38
Region 30 Avg IOU: 0.362651, Class: 0.503255, Obj: 0.630907, No Obj: 0.530558, .5R: 0.400000, .75R: 0.000000, count: 5
Region 42 Avg IOU: 0.263569, Class: 0.696441, Obj: 0.464465, No Obj: 0.459764, .5R: 0.250000, .75R: 0.000000, count: 8
Region 54 Avg IOU: 0.149923, Class: 0.491762, Obj: 0.336601, No Obj: 0.467867, .5R: 0.000000, .75R: 0.000000, count: 30
2: 583.775513, 564.703674 avg, 0.000000 rate, 0.534891 seconds, 64 images
Loaded: 1.104566 seconds
so using nohup to collect the outputs:
nohup ./darknet detector train cfg/coco.data cfg/yolov3.cfg
and then:
cp nohup.out log/yolov3.log
then open the exe file and fill in the parameters:
The parameters you filled in is very important:
line: the lines of you log file
step: How many rows are sampled every time?
start: from which line
end: to which line
ignore: Ignore the value of the previous n times.
选择文件：choose you log file
newName: your extract file name
picName: Name of picture saved
so, in my log file, there is 489284 lines and after extract the file, there is only 29328 lines left.
I hope you enjoy using this software.