From ca3bf2fa92192cc6b53532588f01fa5f67b04b7c Mon Sep 17 00:00:00 2001 From: Alexey Date: Mon, 10 Dec 2018 01:30:48 +0300 Subject: [PATCH] Update Readme.md --- README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 6c1fdc14..d621f9d8 100644 --- a/README.md +++ b/README.md @@ -411,9 +411,13 @@ Choose weights-file **with the highest mAP (mean average precision)** or IoU (in For example, **bigger mAP** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**. -Or just train with `-map` flag: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -map` So you will see loss-chart with mAP-chart (mAP will be calculated for each 4 Epochs using `valid=valid.txt` file that is specified in `obj.data` file) +Or just train with `-map` flag: -![loss_chart_map_chart](https://hsto.org/webt/ip/fx/tn/ipfxtn_fpxwh_0zj8kvm2kdgpd4.jpeg) +`darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -map` + +So you will see loss-chart with mAP-chart (mAP will be calculated for each 4 Epochs using `valid=valid.txt` file that is specified in `obj.data` file) + +![loss_chart_map_chart](https://hsto.org/webt/yd/vl/ag/ydvlagutof2zcnjodstgroen8ac.jpeg) Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights`