Instructions on how to create and train High Power Rocketry tracker model with Python and Yolo. The weights generated by this method can be used to run your custom tracker.

• Uploaded OID4-rocket with Yolov3 notations
• Added script to generate train.txt file,put the output file "train.txt" in "darknet-master\build\darknet\x64\data "folder.
• Create a "obj" folder inside the folder having "train.txt"
• Add all the images under train\images folder into "darknet-master\build\darknet\x64\data\obj" folder
• Add all the yolov3 labels txt files in train\label-yolov3 folder into "darknet-master\build\darknet\x64\data\obj" folder
• Create "obj.data" in "\data" folder with

classes = 1
train = data/train.txt
valid = data/test.txt
names = data/obj.names
backup = backup/

• Create "obj.names" with "0" inside
• Copy "yolov3-tiny_obj.cfg" from "x64\cfg" file to "x64". We will experiment with it later on.Rename it to "yolov3-tiny-obj.cfg". Modyfying it.

[net]
# Testing
#batch=64
#subdivisions=8

filters = number of classes5 + 3, in my case, 1 = rocket, 15+3

[convolutional]
size=1
stride=1
pad=1
filters=8
activation=linear

• [x]Also it is advised to calculate anchors of training dataset. Therefore, we will do that by opening terminal or cmd and running darknet_no_gpu.exe detector calc_anchors data/obj.data -num_of_clusters 6 -width 416 -height 416 hit enter and it should compute the anchors. Paste them inside the first yolo layer , like this

[yolo]
mask = 3,4,5
anchors = 9, 55,  23,127,  70,136,  48,268, 110,316, 279,304

Note: The -cluster is number of pairs, tiny yolo has 6. It can change

• Train custom yolov3 object detector
• run darknet_no_gpu.exe partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15
• darknet_no_gpu.exe detector train data/obj.data yolov3-tiny-obj.cfg yolov3-tiny.conv.15 -dont_show

• Tried various methods. Easiest is to simply download opencv frol sourceforge. Extract into single folder with name "opencv" inside "opencv_3.0" folder. Build x64,release. Try darknet_no_gpu.exe detect cfg/yolov3.cfg yolov3.weights data/eagledarknet_no_gpu.exe detect cfg/yolov3.cfg yolov3.weights data/eagle

• Then open yolo-no-gpu with visual studio 15. Set: Release and x64 IS. Click second build tolo-no-gpu . Pray that it builds.

• finally something is working. Don't give up.