From 5bc62b14e06a3fcfda4e3a19fba77589920eddee Mon Sep 17 00:00:00 2001 From: AlexeyAB <alexeyab84@gmail.com> Date: Mon, 03 Jul 2017 11:12:52 +0000 Subject: [PATCH] Memory leak fixed --- README.md | 36 +++++++++++++++++++++--------------- 1 files changed, 21 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index f833da9..43b21b3 100644 --- a/README.md +++ b/README.md @@ -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 @@ -169,9 +169,9 @@ 5. Run command: `type 2007_train.txt 2007_val.txt 2012_*.txt > train.txt` -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) +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.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` +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` If required change pathes in the file `build\darknet\x64\data\voc.data` @@ -179,22 +179,22 @@ ## How to train with multi-GPU: -1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data yolo-voc.cfg darknet19_448.conv.23` +1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg darknet19_448.conv.23` -2. Then stop and by using partially-trained model `/backup/yolo-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data yolo-voc.cfg yolo-voc_1000.weights -gpus 0,1,2,3` +2. Then stop and by using partially-trained model `/backup/yolo-voc_1000.weights` run training with multigpu (up to 4 GPUs): `darknet.exe detector train data/voc.data yolo-voc.2.0.cfg yolo-voc_1000.weights -gpus 0,1,2,3` 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) * 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: + For example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolo-voc.2.0.cfg` in such lines: ``` [convolutional] @@ -210,9 +210,9 @@ ``` classes= 2 - train = train.txt - valid = test.txt - names = obj.names + train = data/train.txt + valid = data/test.txt + names = data/obj.names backup = backup/ ``` @@ -246,6 +246,8 @@ 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) + 9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\` * 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` @@ -267,7 +269,7 @@ * **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: @@ -275,7 +277,9 @@  - 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`. +To get weights from Early Stopping Point: + + 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: @@ -288,9 +292,9 @@ > 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**. +For example, **bigger IOU** gives weights `yolo-obj_8000.weights` - then **use this weights for detection**.  @@ -306,6 +310,8 @@ 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