From 9ed3e2df4a2dc765245fa2e75ac4a9f4e6187b83 Mon Sep 17 00:00:00 2001
From: Tino Hager <tino.hager@nager.at>
Date: Sun, 24 Jun 2018 19:22:50 +0000
Subject: [PATCH] rename c++ method

---
 README.md |    8 ++++++--
 1 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index db0f945..690cd74 100644
--- a/README.md
+++ b/README.md
@@ -320,6 +320,10 @@
  
  **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.
  
+ **Note:** If you changed width= or height= in your cfg-file, then new width and height must be divisible by 32.
+ 
+ **Note:** After training use such command for detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights`
+ 
 ### How to train tiny-yolo (to detect your custom objects):
 
 Do all the same steps as for the full yolo model as described above. With the exception of:
@@ -420,14 +424,14 @@
 
   * 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`

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