From 6d2e31bf2026301a25a3e8dabeae0680c159328b Mon Sep 17 00:00:00 2001 From: Alexey <AlexeyAB@users.noreply.github.com> Date: Mon, 02 Jul 2018 16:29:10 +0000 Subject: [PATCH] Update Readme.md --- README.md | 4 +++- 1 files changed, 3 insertions(+), 1 deletions(-) diff --git a/README.md b/README.md index d43d797..a52aafa 100644 --- a/README.md +++ b/README.md @@ -322,6 +322,8 @@ **Note:** If you changed width= or height= in your cfg-file, then new width and height must be divisible by 32. + **Note:** After training use such command for detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights` + ### How to train tiny-yolo (to detect your custom objects): Do all the same steps as for the full yolo model as described above. With the exception of: @@ -420,7 +422,7 @@ * 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 - you should preferably have 2000 images for each class or more + * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds - you should preferably have 2000 different images for each class or more, and you should train `2000*classes` iterations or more * 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 -- Gitblit v1.10.0