From d8bafc728478e5cba9cf41eca01d66a38800eddd Mon Sep 17 00:00:00 2001 From: Alexey <AlexeyAB@users.noreply.github.com> Date: Fri, 28 Apr 2017 11:04:56 +0000 Subject: [PATCH] Update Readme.md --- README.md | 22 ++++++++++++++-------- 1 files changed, 14 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index e2224ef..85b25d5 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ |  |  https://arxiv.org/abs/1612.08242 | |---|---| -|  |  https://arxiv.org/abs/1612.08242 | +|  |  https://arxiv.org/abs/1612.08242 | |---|---| @@ -26,7 +26,7 @@ More details: http://pjreddie.com/darknet/yolo/ ##### Requires: -* **MS Visual Studio 2015 (v140)**: https://www.microsoft.com/download/details.aspx?id=48146 +* **MS Visual Studio 2015 (v140)**: https://go.microsoft.com/fwlink/?LinkId=532606&clcid=0x409 (or offline [ISO image](https://go.microsoft.com/fwlink/?LinkId=615448&clcid=0x409)) * **CUDA 8.0 for Windows x64**: https://developer.nvidia.com/cuda-downloads * **OpenCV 2.4.9**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.9/opencv-2.4.9.exe/download - To compile without OpenCV - remove define OPENCV from: Visual Studio->Project->Properties->C/C++->Preprocessor @@ -169,7 +169,9 @@ 5. Run command: `type 2007_train.txt 2007_val.txt 2012_*.txt > train.txt` -6. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.cfg darknet19_448.conv.23` +6. Set `batch=64` and `subdivisions=8` in the file `yolo-voc.cfg`: [link](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L3) + +7. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.cfg darknet19_448.conv.23` If required change pathes in the file `build\darknet\x64\data\voc.data` @@ -185,10 +187,12 @@ ## 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) * change line `classes=20` to your number of objects - * change line #224 from [`filters=125`](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.cfg#L224) to `filters=(classes + 5)*5` (generally this depends on the `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`) + * change line #237 from [`filters=125`](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.cfg#L237) to `filters=(classes + 5)*5` (generally this depends on the `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`) For example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolo-voc.cfg` in such lines: @@ -263,7 +267,7 @@ * **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: @@ -271,7 +275,7 @@  - 2.1. At first, you should put filenames of validation images to file `data\voc.2007.test` (format as in `train.txt`) or if you haven't validation images - simply copy `data\train.txt` to `data\voc.2007.test`. + 2.1. At first, in your file `obj.data` you must specify the path to the validation dataset `valid = valid.txt` (format of `valid.txt` as in `train.txt`), and if you haven't validation images, just copy `data\train.txt` to `data\valid.txt`. 2.2 If training is stopped after 9000 iterations, to validate some of previous weights use this commands: @@ -284,7 +288,7 @@ > 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**. @@ -302,6 +306,8 @@ 1. Before training: * set flag `random=1` in your `.cfg`-file - it will increase precision by training Yolo for different resolutions: [link](https://github.com/AlexeyAB/darknet/blob/47409529d0eb935fa7bafbe2b3484431117269f5/cfg/yolo-voc.cfg#L244) + + * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides 2. After training - for detection: -- Gitblit v1.10.0