diff --git a/README.md b/README.md
index 44680abb..e6b576a5 100644
--- a/README.md
+++ b/README.md
@@ -17,9 +17,10 @@
 
 
 
-|  ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_fps](https://hsto.org/webt/pw/zd/0j/pwzd0jb9g7znt_dbsyw9qzbnvti.jpeg) https://pjreddie.com/media/files/papers/YOLOv3.pdf |
+|  ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) |   ![map_fps](https://hsto.org/webt/pw/zd/0j/pwzd0jb9g7znt_dbsyw9qzbnvti.jpeg) mAP (AP50) https://pjreddie.com/media/files/papers/YOLOv3.pdf |
 |---|---|
 
+* Yolo v3 source chart for the RetinaNet on MS COCO got from Table 1 (e): https://arxiv.org/pdf/1708.02002.pdf
 * Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg
 * Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg
 
@@ -368,6 +369,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol
 
 * **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
 
+**mAP** is default metric of precision in the PascalVOC competition, **this is the same as AP50** metric in the MS COCO competition.
 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**.
 
 ![precision_recall_iou](https://hsto.org/files/ca8/866/d76/ca8866d76fb840228940dbf442a7f06a.jpg)