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Update Readme.md

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@ -415,6 +415,8 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol
`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
* check that each object are mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark
* desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds
* desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files)
@ -424,7 +426,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol
* for training for small objects - set `layers = -1, 11` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720
and set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L717
* General rule - you should keep relative size of objects in the Training and Testing datasets the same:
* General rule - you should keep relative size of objects in the Training and Testing datasets roughly the same:
* `train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width`
* `train_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height`