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| | | 6. [When should I stop training](#when-should-i-stop-training) |
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| | | 7. [How to improve object detection](#how-to-improve-object-detection) |
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| | | 8. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files) |
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| | | 9. [How to use Yolo as DLL](#how-to-use-yolo-as-dll) |
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| | | 9. [Using Yolo9000](#using-yolo9000) |
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| | | 10. [How to use Yolo as DLL](#how-to-use-yolo-as-dll) |
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| | | |  |  https://arxiv.org/abs/1612.08242 | |
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| | | `OPENCV;_TIMESPEC_DEFINED;_CRT_SECURE_NO_WARNINGS;_CRT_RAND_S;WIN32;NDEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions)` |
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| | | - open file: `\src\detector.c` and check lines `#pragma` and `#inclue` for OpenCV. |
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| | | - compile to .exe (X64 & Release) and put .dll-s near with .exe: |
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| | | * `pthreadVC2.dll, pthreadGC2.dll` from \3rdparty\dll\x64 |
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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.2.0.cfg#L224) to: filters=(classes + 5)*5, so if `classes=2` then should be `filter=35` |
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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)x5, so if `classes=2` then should be `filters=35`. Or if you use `classes=1` then write `filters=30`, **do not write in the cfg-file: filters=(classes + 5)x5**. |
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| | | (Generally `filters` depends on the `classes`, `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`) |
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| | | (Generally `filters` depends on the `classes`, `num` and `coords`, i.e. equal to `(classes + coords + 1)*num`, where `num` is number of anchors) |
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| | | So 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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| | | With example of: `train.txt`, `obj.names`, `obj.data`, `yolo-obj.cfg`, `air`1-6`.txt`, `bird`1-4`.txt` for 2 classes of objects (air, bird) and `train_obj.cmd` with example how to train this image-set with Yolo v2 |
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| | | ## Using Yolo9000 |
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| | | Simultaneous detection and classification of 9000 objects: |
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| | | * `yolo9000.weights` - (186 MB Yolo9000 Model) requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights |
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| | | * `yolo9000.cfg` - cfg-file of the Yolo9000, also there are paths to the `9k.tree` and `coco9k.map` https://github.com/AlexeyAB/darknet/blob/617cf313ccb1fe005db3f7d88dec04a04bd97cc2/cfg/yolo9000.cfg#L217-L218 |
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| | | * `9k.tree` - **WordTree** of 9418 categories - `<label> <parent_it>`, if `parent_id == -1` then this label hasn't parent: https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/9k.tree |
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| | | * `coco9k.map` - map 80 categories from MSCOCO to WordTree `9k.tree`: https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/coco9k.map |
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| | | * `combine9k.data` - data file, there are paths to: `9k.labels`, `9k.names`, `inet9k.map`, (change path to your `combine9k.train.list`): https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/combine9k.data |
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| | | * `9k.labels` - 9418 labels of objects: https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/9k.labels |
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| | | * `9k.names` - |
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| | | 9418 names of objects: https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/9k.names |
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| | | * `inet9k.map` - map 200 categories from ImageNet to WordTree `9k.tree`: https://raw.githubusercontent.com/AlexeyAB/darknet/master/build/darknet/x64/data/inet9k.map |
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| | | ## How to use Yolo as DLL |
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| | | 1. To compile Yolo as C++ DLL-file `yolo_cpp_dll.dll` - open in MSVS2015 file `build\darknet\yolo_cpp_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_cpp_dll |
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