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4 changes: 2 additions & 2 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@ Supported both: OpenCV 2.x and OpenCV 3.x

* delete all files from directory `x64/Release/data/img`
* put your `.jpg`-images to this directory `x64/Release/data/img`
* change numer of classes (objects for detection) in file `x64/Release/data/obj.data`: https://github.com/AlexeyAB/Yolo_mark/blob/master/x64/Release/data/obj.data#L1
* change number of classes (objects for detection) in file `x64/Release/data/obj.data`: https://github.com/AlexeyAB/Yolo_mark/blob/master/x64/Release/data/obj.data#L1
* put names of objects, one for each line in file `x64/Release/data/obj.names`: https://github.com/AlexeyAB/Yolo_mark/blob/master/x64/Release/data/obj.names
* run file: `x64\Release\yolo_mark.cmd`

Expand Down Expand Up @@ -62,7 +62,7 @@ As a result, many frames will be collected in the directory `data/img`. Then you
#### Here are:

* /x64/Release/
* `yolo_mark.cmd` - example hot to use yolo mark: `yolo_mark.exe data/img data/train.txt data/obj.names`
* `yolo_mark.cmd` - example how to use yolo mark: `yolo_mark.exe data/img data/train.txt data/obj.names`
* `train_obj.cmd` - example how to train yolo for your custom objects (put this file near with darknet.exe): `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23`
* `yolo-obj.cfg` - example of yoloV3-neural-network for 2 object
* /x64/Release/data/
Expand Down