From d502dea9a451c290f602ba18bc61f4f79c51be0c Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Mon, 04 Jun 2018 10:55:27 +0000
Subject: [PATCH] Update Readme.md

---
 README.md |    9 +++++++--
 1 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index b56299e..40289e9 100644
--- a/README.md
+++ b/README.md
@@ -218,7 +218,7 @@
 
 More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc
 
- **Note:** If during training you see `nan` values in some lines then training goes well, but if `nan` are in all lines then training goes wrong.
+ **Note:** If during training you see `nan` values for `avg` (loss) field - then training goes wrong, but if `nan` is in some other lines - then training goes well.
 
 ## How to train with multi-GPU:
 
@@ -317,7 +317,7 @@
 
  * Also you can get result earlier than all 45000 iterations.
  
- **Note:** If during training you see `nan` values in some lines then training goes well, but if `nan` are in all lines then training goes wrong.
+ **Note:** If during training you see `nan` values for `avg` (loss) field - then training goes wrong, but if `nan` is in some other lines - then training goes well.
  
 ### How to train tiny-yolo (to detect your custom objects):
 
@@ -421,6 +421,11 @@
 
   * 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
   
+  * General rule - you should keep relative size of objects in the Training and Testing datasets 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`
+  
   * to speedup training (with decreasing detection accuracy) do Fine-Tuning instead of Transfer-Learning, set param `stopbackward=1` in one of the penultimate convolutional layers before the 1-st `[yolo]`-layer, for example here: https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L598
 
 2. After training - for detection:

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