From 65bff2683bdffe7ec82eacd8144c70c09d19c88d Mon Sep 17 00:00:00 2001 From: AlexeyAB <alexeyab84@gmail.com> Date: Fri, 16 Feb 2018 20:55:37 +0000 Subject: [PATCH] It takes into account the Difficult for calculating mAP for PascalVOC --- README.md | 8 +++++--- 1 files changed, 5 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 94fbdb9..ebe9a1a 100644 --- a/README.md +++ b/README.md @@ -131,7 +131,7 @@ 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 **don't have GPU**, but have **MSVS 2015 and OpenCV 3.0** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet_no_gpu.sln`, set **x64** and **Release**, and do the: Build -> Build darknet +3. If you **don't have GPU**, but have **MSVS 2015 and OpenCV 3.0** (with paths: `C:\opencv_3.0\opencv\build\include` & `C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet_no_gpu.sln`, set **x64** and **Release**, and do the: Build -> Build darknet_no_gpu 4. If you have **OpenCV 2.4.13** instead of 3.0 then you should change pathes after `\darknet.sln` is opened @@ -195,7 +195,7 @@ 6. Set `batch=64` and `subdivisions=8` in the file `yolo-voc.2.0.cfg`: [link](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.2.0.cfg#L2) -7. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23` +7. Start training by using `train_voc.cmd` or by using the command line: `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23` (**Note:** If you are using CPU, try `darknet_no_gpu.exe` instead of `darknet.exe`.) If required change pathes in the file `build\darknet\x64\data\voc.data` @@ -272,7 +272,7 @@ 8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23` - (file `yolo-obj_xxx.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations until 1000 iterations has been reached, and after for each 1000 iterations) + (file `yolo-obj_xxx.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations) 9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\` @@ -341,6 +341,8 @@ * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides + * for training on small objects, add the parameter `small_object=1` in the last layer [region] in your cfg-file + 2. After training - for detection: * Increase network-resolution by set in your `.cfg`-file (`height=608` and `width=608`) or (`height=832` and `width=832`) or (any value multiple of 32) - this increases the precision and makes it possible to detect small objects: [link](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L4) -- Gitblit v1.10.0