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@ -569,7 +569,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol
* 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. * 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.
* recalculate anchors for your dataset for `width` and `height` from cfg-file: * 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` `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 that 1st-[yolo]-layer has anchors larger than 60x60, 2nd larger than 30x30, 3rd remaining. 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. 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 that 1st-[yolo]-layer has anchors larger than 60x60, 2nd larger than 30x30, 3rd remaining. 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.