From c8ad33ceba05cbb60c5197e4db4d3bdef28edd6d Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Mon, 04 Jun 2018 12:57:13 +0000
Subject: [PATCH] Update Readme.md

---
 README.md |    4 +++-
 1 files changed, 3 insertions(+), 1 deletions(-)

diff --git a/README.md b/README.md
index 8e93f9b..7cf0b77 100644
--- a/README.md
+++ b/README.md
@@ -415,6 +415,8 @@
   `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 @@
   * 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`

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