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| | | 5. If you want to build with CUDNN to speed up then: |
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| | | * download and install **cuDNN 5.1 for CUDA 8.0**: https://developer.nvidia.com/cudnn |
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| | | * download and install **cuDNN 6.0 for CUDA 8.0**: https://developer.nvidia.com/cudnn |
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| | | |
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| | | * add Windows system variable `cudnn` with path to CUDNN: https://hsto.org/files/a49/3dc/fc4/a493dcfc4bd34a1295fd15e0e2e01f26.jpg |
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| | | 5. Run command: `type 2007_train.txt 2007_val.txt 2012_*.txt > train.txt` |
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| | | 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) |
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| | | 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) |
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| | | |
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| | | 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` |
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| | | 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: |
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| | | * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L3) |
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| | | * change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.cfg#L4) |
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| | | * change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.2.0.cfg#L2) |
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| | | * change line subdivisions to [`subdivisions=8`](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/yolo-voc.2.0.cfg#L3) |
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| | | * change line `classes=20` to your number of objects |
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| | | * 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`) |
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| | | * change line #237 from [`filters=125`](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L224) to `filters=(classes + 5)*5` (generally this depends on the `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`) |
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| | | |
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| | | For example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolo-voc.2.0.cfg` in such lines: |
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| | | |
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| | |
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| | | ## How to improve object detection: |
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| | | |
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| | | 1. Before training: |
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| | | * 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) |
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| | | * 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/master/cfg/yolo-voc.2.0.cfg#L244) |
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| | | |
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| | | * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides |
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| | | |
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| | | 2. After training - for detection: |
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| | | * 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/47409529d0eb935fa7bafbe2b3484431117269f5/cfg/yolo-voc.cfg#L4) |
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| | | * 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) |
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| | | |
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| | | * you do not need to train the network again, just use `.weights`-file already trained for 416x416 resolution |
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| | | * if error `Out of memory` occurs then in `.cfg`-file you should increase `subdivisions=16`, 32 or 64: [link](https://github.com/AlexeyAB/darknet/blob/47409529d0eb935fa7bafbe2b3484431117269f5/cfg/yolo-voc.cfg#L3) |
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| | | * if error `Out of memory` occurs then in `.cfg`-file you should increase `subdivisions=16`, 32 or 64: [link](https://github.com/AlexeyAB/darknet/blob/master/cfg/yolo-voc.2.0.cfg#L3) |
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| | | |
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| | | ## How to mark bounded boxes of objects and create annotation files: |
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| | | |
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| | | * after launching your console application and entering the image file name - you will see info for each object: |
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| | | `<obj_id> <left_x> <top_y> <width> <height> <probability>` |
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| | | * to use simple OpenCV-GUI you should uncomment line `//#define OPENCV` in `yolo_console_dll.cpp`-file: [link](https://github.com/AlexeyAB/darknet/blob/a6cbaeecde40f91ddc3ea09aa26a03ab5bbf8ba8/src/yolo_console_dll.cpp#L5) |
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| | | * you can see source code of simple example for detection on the video file: [link](https://github.com/AlexeyAB/darknet/blob/ab1c5f9e57b4175f29a6ef39e7e68987d3e98704/src/yolo_console_dll.cpp#L75) |
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| | | |
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| | | `yolo_cpp_dll.dll`-API: [link](https://github.com/AlexeyAB/darknet/blob/master/src/yolo_v2_class.hpp#L31) |
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| | | ``` |
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