From f0abcfa02b2094396f955c743f7f11fcdb2e3d13 Mon Sep 17 00:00:00 2001
From: IlyaOvodov <b@ovdv.ru>
Date: Mon, 04 Jun 2018 15:57:15 +0000
Subject: [PATCH] Merge branch 'master' of https://github.com/AlexeyAB/darknet into Fix_get_color_depth
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README.md | 10 ++++++++++
1 files changed, 10 insertions(+), 0 deletions(-)
diff --git a/README.md b/README.md
index e56efa8..7cf0b77 100644
--- a/README.md
+++ b/README.md
@@ -415,12 +415,22 @@
`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)
* 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 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`
+
* 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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