From b1dddf02ccf8dcfaadee4e8a5ed8726725ec1b93 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Sun, 12 Aug 2018 23:43:45 +0000
Subject: [PATCH] Fixed AVX compiled bug
---
README.md | 6 ++++--
1 files changed, 4 insertions(+), 2 deletions(-)
diff --git a/README.md b/README.md
index f980b9d..c3b7a84 100644
--- a/README.md
+++ b/README.md
@@ -282,7 +282,7 @@
It will create `.txt`-file for each `.jpg`-image-file - in the same directory and with the same name, but with `.txt`-extension, and put to file: object number and object coordinates on this image, for each object in new line: `<object-class> <x> <y> <width> <height>`
Where:
- * `<object-class>` - integer number of object from `0` to `(classes-1)`
+ * `<object-class>` - integer object number from `0` to `(classes-1)`
* `<x> <y> <width> <height>` - float values relative to width and height of image, it can be equal from (0.0 to 1.0]
* for example: `<x> = <absolute_x> / <image_width>` or `<height> = <absolute_height> / <image_height>`
* atention: `<x> <y>` - are center of rectangle (are not top-left corner)
@@ -428,11 +428,13 @@
* 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 with a large number of objects in each image, add the parameter `max=200` or higher value in the last `[yolo]`-layer or `[region]`-layer in your cfg-file (the global maximum number of objects that can be detected by YoloV3 is `0,0615234375*(width*height)` where are width and height are parameters from `[net]` section in 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
+ * If you train the model to distinguish Left and Right objects as separate classes (left/right hand, left/right-turn on road signs, ...) then for disabling flip data augmentation - add `flip=0` here: https://github.com/AlexeyAB/darknet/blob/3d2d0a7c98dbc8923d9ff705b81ff4f7940ea6ff/cfg/yolov3.cfg#L17
+
* 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`
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