From a360f694093f7748e3232f4ed74d40446d735fc3 Mon Sep 17 00:00:00 2001 From: Alexey <AlexeyAB@users.noreply.github.com> Date: Wed, 01 Mar 2017 12:29:50 +0000 Subject: [PATCH] Readme.md - When should I stop training --- README.md | 50 +++++++++++++++++++++++++++++++++++++++++++++++--- 1 files changed, 47 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index d4f850e..5c29830 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,8 @@ 2. [How to compile](#how-to-compile) 3. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data) 4. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects) -5. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) +5. [When should I stop training](#when-should-i-stop-training) +6. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) |  |  https://arxiv.org/abs/1612.08242 | |---|---| @@ -85,6 +86,8 @@ 1. If you have MSVS 2015, CUDA 8.0 and OpenCV 2.4.9 (with paths: `C:\opencv_2.4.9\opencv\build\include` & `C:\opencv_2.4.9\opencv\build\x64\vc12\lib` or `vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release**, and do the: Build -> Build darknet + 1.1. Find files `opencv_core249.dll`, `opencv_highgui249.dll` and `opencv_ffmpeg249_64.dll` in `C:\opencv_2.4.9\opencv\build\x64\vc12\bin` or `vc14\bin` and put it near with `darknet.exe` + 2. If you have other version of CUDA (not 8.0) then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 8.0" and change it to your CUDA-version, then do step 1 3. If you have other version of OpenCV 2.4.x (not 2.4.9) then you should change pathes after `\darknet.sln` is opened @@ -236,11 +239,52 @@ * After each 1000 iterations you can stop and later start training from this point. For example, after 2000 iterations you can stop training, and later just copy `yolo-obj_2000.weights` from `build\darknet\x64\backup\` to `build\darknet\x64\` and start training using: `darknet.exe detector train data/obj.data yolo-obj.cfg yolo-obj_2000.weights` - * Also you can get result earlier than all 45000 iterations, for example, usually sufficient 2000 iterations for each class(object). I.e. for 6 classes to avoid overfitting - you can stop training after 12000 iterations and use `yolo-obj_12000.weights` to detection. + * Also you can get result earlier than all 45000 iterations. +## When should I stop training: + +Usually sufficient 2000 iterations for each class(object). But for a more precise definition when you should stop training, use the following manual: + +1. During training, you will see varying indicators of error, and you should stop when no longer decreases **0.060730 avg**: + + > Region Avg IOU: 0.798363, Class: 0.893232, Obj: 0.700808, No Obj: 0.004567, Avg Recall: 1.000000, count: 8 + > Region Avg IOU: 0.800677, Class: 0.892181, Obj: 0.701590, No Obj: 0.004574, Avg Recall: 1.000000, count: 8 + > + > **9002**: 0.211667, **0.060730 avg**, 0.001000 rate, 3.868000 seconds, 576128 images + > Loaded: 0.000000 seconds + + * **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. + +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: + +For example, you stopped training after 9000 iterations, but the best result can give one of previous weights (7000, 8000, 9000). It can happen due to overfitting. **Overfitting** - is case when you can detect objects on images from training-dataset, but can't detect ojbects on any others images. You should get weights from **Early Stopping Point**: + + + + If training is stopped after 9000 iterations, to validate some of previous weights use this commands: + +* `darknet.exe detector recall data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights` +* `darknet.exe detector recall data/obj.data yolo-obj.cfg backup\yolo-obj_8000.weights` +* `darknet.exe detector recall data/obj.data yolo-obj.cfg backup\yolo-obj_9000.weights` + +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) + +For example, **bigger IUO** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**. + + + + ### Custom object detection: -Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_3000.weights` +Example of custom object detection: `darknet.exe detector test data/obj.data yolo-obj.cfg yolo-obj_8000.weights` |  |  | |---|---| -- Gitblit v1.10.0