From a7ddb205067bd0b1e83e4f5ce6f40bfd1d18afae Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Mon, 04 Jun 2018 11:23:13 +0000
Subject: [PATCH] Update Readme.md

---
 README.md |    8 ++++++++
 1 files changed, 8 insertions(+), 0 deletions(-)

diff --git a/README.md b/README.md
index e56efa8..8e93f9b 100644
--- a/README.md
+++ b/README.md
@@ -421,6 +421,14 @@
 
   * 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 - 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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