开源！适用于Win和Linux平台的YOLO4和YOLO3( 九 ) _Linux

to speedup training (with decreasing detection accuracy) set param stopbackward=1 for layer-136 in cfg-file

each: model of object, side, illimination, scale, each 30 grad of the turn and inclination angles - these are different objects from an internal perspective of the neural network. So the more different objects you want to detect, the more complex network model should be used.

to make the detected bounded boxes more accurate, you can add 3 parameters ignore_thresh = .9 iou_normalizer=0.5 iou_loss=giou to each [yolo] layer and train, it will increase mAP@0.9, but decrease mAP@0.5.

Only if you are an expert in neural detection networks - recalculate anchors for your dataset for width and height from cfg-file: darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416 then set the same 9 anchors in each of 3 [yolo]-layers in your cfg-file. But you should change indexes of anchors masks= for each [yolo]-layer, so for YOLOv4 the 1st-[yolo]-layer has anchors smaller than 30x30, 2nd smaller than 60x60, 3rd remaining, and vice versa for YOLOv3. Also you should change the filters=(classes + 5)*<number of mask> before each [yolo]-layer. If many of the calculated anchors do not fit under the appropriate layers - then just try using all the default anchors.

After training - for detection:

Increase network-resolution by set in your .cfg-file (height=608 and width=608) or (height=832 and width=832) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: linkit is not necessary to train the network again, just use .weights-file already trained for 416x416 resolutionbut to get even greater accuracy you should train with higher resolution 608x608 or 832x832, note: if error Out of memory occurs then in .cfg-file you should increase subdivisions=16, 32 or 64: link

How to mark bounded boxes of objects and create annotation files:Here you can find repository with GUI-software for marking bounded boxes of objects and generating annotation files for Yolo v2 - v4: https://github.com/AlexeyAB/Yolo_mark
With example of: train.txt, obj.names, obj.data, yolo-obj.cfg, air1-6.txt, bird1-4.txt for 2 classes of objects (air, bird) and train_obj.cmd with example how to train this image-set with Yolo v2 - v4
Different tools for marking objects in images:

in C++: https://github.com/AlexeyAB/Yolo_mark
in Python: https://github.com/tzutalin/labelImg
in Python: https://github.com/Cartucho/OpenLabeling
in C++: https://www.ccoderun.ca/darkmark/
in JAVAScript: https://github.com/opencv/cvat

How to use Yolo as DLL and SO libraries

on Linuxusing build.sh orbuild darknet using cmake orset LIBSO=1 in the Makefile and do make
on Windowsusing build.ps1 orbuild darknet using cmake orcompile builddarknetyolo_cpp_dll.sln solution or builddarknetyolo_cpp_dll_no_gpu.sln solution

There are 2 APIs:

C API: https://github.com/AlexeyAB/darknet/blob/master/include/darknet.hPython examples using the C API::https://github.com/AlexeyAB/darknet/blob/master/darknet.pyhttps://github.com/AlexeyAB/darknet/blob/master/darknet_video.py
C++ API: https://github.com/AlexeyAB/darknet/blob/master/include/yolo_v2_class.hppC++ example that uses C++ API: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp