From d8bafc728478e5cba9cf41eca01d66a38800eddd Mon Sep 17 00:00:00 2001 From: Alexey Date: Fri, 28 Apr 2017 14:04:56 +0300 Subject: [PATCH] Update Readme.md --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 99495d1d..85b25d5a 100644 --- a/README.md +++ b/README.md @@ -187,7 +187,7 @@ https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ ## How to train (to detect your custom objects): -1. Create file `yolo-obj.cfg` with the same content as in `yolo-voc.cfg` (or copy `yolo-voc.cfg` to `yolo-obj.cfg)` and: +1. Create file `yolo-obj.cfg` with the same content as in `yolo-voc.2.0.cfg` (or copy `yolo-voc.2.0.cfg` to `yolo-obj.cfg)` and: * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L3) * change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L4) @@ -267,7 +267,7 @@ Usually sufficient 2000 iterations for each class(object). But for a more precis * **9002** - iteration number (number of batch) * **0.060730 avg** - average loss (error) - **the lower, the better** - When you see that average loss **0.060730 avg** enough low at many iterations and no longer decreases then you should stop training. + When you see that average loss **0.xxxxxx avg** no longer decreases at many iterations then you should stop training. 2. Once training is stopped, you should take some of last `.weights`-files from `darknet\build\darknet\x64\backup` and choose the best of them: @@ -288,7 +288,7 @@ And comapre last output lines for each weights (7000, 8000, 9000): > 7586 7612 7689 RPs/Img: 68.23 **IOU: 77.86%** Recall:99.00% * **IOU** - the bigger, the better (says about accuracy) - **better to use** -* **Recall** - the bigger, the better (says about accuracy) +* **Recall** - the bigger, the better (says about accuracy) - actually Yolo calculates true positives, so it shouldn't be used For example, **bigger IUO** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**.