Update Readme.md

README.md

@@ -422,14 +422,14 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol
 
   * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 images for each class or more
 
-  * 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)
+  * 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) - use as many images of negative samples as there are images with objects
 
   * for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last layer [region] in your cfg-file
   
   * 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 - your training dataset should include such a set of relative sizes of objects that you want to detect - differing by no more than 2 times: 
+  * General rule - your training dataset should include such a set of relative sizes of objects that you want to detect: 
 
     * `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`