From 4a7a076d0939a491af8fe3fd63e5012ffd5afebc Mon Sep 17 00:00:00 2001 From: Alexey <AlexeyAB@users.noreply.github.com> Date: Sun, 18 Feb 2018 23:28:46 +0000 Subject: [PATCH] Update Readme.md --- README.md | 6 ++++++ 1 files changed, 6 insertions(+), 0 deletions(-) diff --git a/README.md b/README.md index e7683da..ec8c19a 100644 --- a/README.md +++ b/README.md @@ -322,10 +322,14 @@ For example, **bigger IOU** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**. +Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights` + * **IoU** (intersect of union) - average instersect of union of objects and detections for a certain threshold = 0.24 * **mAP** (mean average precision) - mean value of `average precisions` for each class, where `average precision` is average value of 11 points on PR-curve for each possible threshold (each probability of detection) for the same class (Precision-Recall in terms of PascalVOC, where Precision=TP/(TP+FP) and Recall=TP/(TP+FN) ), page-11: http://homepages.inf.ed.ac.uk/ckiw/postscript/ijcv_voc09.pdf +In terms of Wiki, indicators Precision and Recall have a slightly different meaning than in the PascalVOC competition, but **IoU always has the same meaning**. +  ### How to calculate mAP on PascalVOC 2007: @@ -359,6 +363,8 @@ * for training on small objects, add the parameter `small_object=1` 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 layer [region] in your cfg-file + 2. After training - for detection: * Increase network-resolution by set in your `.cfg`-file (`height=608` and `width=608`) or (`height=832` and `width=832`) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: [link](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L4) -- Gitblit v1.10.0