Darknet-cpp project is a bug-fixed and C++ compilable version of darknet (including Yolov3 and v2), an open source neural network framework written in C and CUDA. Darknet-cpp builds on Linux, Windows and also tested on Mac by users.
Prebuilt binaries for evaluation now available
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Prebuilt binaries are provided for evaluation purposes.
- Tegra TK1 (CPU, CUDA, CUDA + CUDNN) - Yolov2 only
- Windows x64 (CUDA + CUDNN) - Yolov3
- Linux x64 (CUDA + CUDNN) - Yolov3
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Download the binaries from yolo-bins
Features of darknet-cpp
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Uses same source code-base as original darknet (ie same .c files are used). Modification is done only for runtime bug-fixes, compile time fixes for c++, and the build system itself. For list of bugs fixed, refer to this thread - https://groups.google.com/forum/#!topic/darknet/4Hb159aZBbA, and https://github.com/prabindh/darknet/issues
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The Linux build system supports 3 targets -
- original darknet (with gcc compiler),
- darknet-cpp (with g++ compiler and Visual Studio compiler), and
- Shared library (libdarknet-cpp-shared.so)
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Can use bounding boxes directly from Euclid object labeller (https://github.com/prabindh/euclid)
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C++ API - arapaho, that works in conjunction with libdarknet-cpp-shared.so, and a test wrapper that can read images or video files, and show detected regions in a complete C++ application.
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darknet-cpp supports OpenCV3. Tested on Ubuntu 16.04 and windows, with CUDA 8.x
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Note: darknet-cpp requires a C++11 compiler for darknet-cpp, and arapaho builds.
Usage
Using the Makefile in the root directory of the darknet source repository,
make darknet- only darknet (original code), with OPENCV=0make darknet-cpp- only the CPP version, with OPENCV=1make darknet-cpp-shared- build the shared-lib version (without darknet.c calling wrapper), OPENCV=1make arapaho- build arapaho and its test wrapper (from within arapaho folder)
After performing `make darknet-cpp`, the executable `darknet-cpp` is generated. Run the below command to recognise the provided data objects.
./darknet-cpp detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
layer filters size input output
0 conv 32 3 x 3 / 1 256 x 256 x 3 -> 256 x 256 x 32 0.113 BFLOPs
1 conv 64 3 x 3 / 2 256 x 256 x 32 -> 128 x 128 x 64 0.604 BFLOPs
2 conv 32 1 x 1 / 1 128 x 128 x 64 -> 128 x 128 x 32 0.067 BFLOPs
3 conv 64 3 x 3 / 1 128 x 128 x 32 -> 128 x 128 x 64 0.604 BFLOPs
4 res 1 128 x 128 x 64 -> 128 x 128 x 64
5 conv 128 3 x 3 / 2 128 x 128 x 64 -> 64 x 64 x 128 0.604 BFLOPs
6 conv 64 1 x 1 / 1 64 x 64 x 128 -> 64 x 64 x 64 0.067 BFLOPs
7 conv 128 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 128 0.604 BFLOPs
8 res 5 64 x 64 x 128 -> 64 x 64 x 128
9 conv 64 1 x 1 / 1 64 x 64 x 128 -> 64 x 64 x 64 0.067 BFLOPs
10 conv 128 3 x 3 / 1 64 x 64 x 64 -> 64 x 64 x 128 0.604 BFLOPs
11 res 8 64 x 64 x 128 -> 64 x 64 x 128
12 conv 256 3 x 3 / 2 64 x 64 x 128 -> 32 x 32 x 256 0.604 BFLOPs
13 conv 128 1 x 1 / 1 32 x 32 x 256 -> 32 x 32 x 128 0.067 BFLOPs
14 conv 256 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.604 BFLOPs
15 res 12 32 x 32 x 256 -> 32 x 32 x 256
16 conv 128 1 x 1 / 1 32 x 32 x 256 -> 32 x 32 x 128 0.067 BFLOPs
17 conv 256 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.604 BFLOPs
18 res 15 32 x 32 x 256 -> 32 x 32 x 256
19 conv 128 1 x 1 / 1 32 x 32 x 256 -> 32 x 32 x 128 0.067 BFLOPs
20 conv 256 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.604 BFLOPs
21 res 18 32 x 32 x 256 -> 32 x 32 x 256
22 conv 128 1 x 1 / 1 32 x 32 x 256 -> 32 x 32 x 128 0.067 BFLOPs
23 conv 256 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.604 BFLOPs
24 res 21 32 x 32 x 256 -> 32 x 32 x 256
25 conv 128 1 x 1 / 1 32 x 32 x 256 -> 32 x 32 x 128 0.067 BFLOPs
26 conv 256 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.604 BFLOPs
27 res 24 32 x 32 x 256 -> 32 x 32 x 256
28 conv 128 1 x 1 / 1 32 x 32 x 256 -> 32 x 32 x 128 0.067 BFLOPs
29 conv 256 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.604 BFLOPs
30 res 27 32 x 32 x 256 -> 32 x 32 x 256
31 conv 128 1 x 1 / 1 32 x 32 x 256 -> 32 x 32 x 128 0.067 BFLOPs
32 conv 256 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.604 BFLOPs
33 res 30 32 x 32 x 256 -> 32 x 32 x 256
34 conv 128 1 x 1 / 1 32 x 32 x 256 -> 32 x 32 x 128 0.067 BFLOPs
35 conv 256 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.604 BFLOPs
36 res 33 32 x 32 x 256 -> 32 x 32 x 256
37 conv 512 3 x 3 / 2 32 x 32 x 256 -> 16 x 16 x 512 0.604 BFLOPs
38 conv 256 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 256 0.067 BFLOPs
39 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
40 res 37 16 x 16 x 512 -> 16 x 16 x 512
41 conv 256 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 256 0.067 BFLOPs
42 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
43 res 40 16 x 16 x 512 -> 16 x 16 x 512
44 conv 256 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 256 0.067 BFLOPs
45 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
46 res 43 16 x 16 x 512 -> 16 x 16 x 512
47 conv 256 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 256 0.067 BFLOPs
48 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
49 res 46 16 x 16 x 512 -> 16 x 16 x 512
50 conv 256 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 256 0.067 BFLOPs
51 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
52 res 49 16 x 16 x 512 -> 16 x 16 x 512
53 conv 256 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 256 0.067 BFLOPs
54 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
55 res 52 16 x 16 x 512 -> 16 x 16 x 512
56 conv 256 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 256 0.067 BFLOPs
57 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
58 res 55 16 x 16 x 512 -> 16 x 16 x 512
59 conv 256 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 256 0.067 BFLOPs
60 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
61 res 58 16 x 16 x 512 -> 16 x 16 x 512
62 conv 1024 3 x 3 / 2 16 x 16 x 512 -> 8 x 8 x1024 0.604 BFLOPs
63 conv 512 1 x 1 / 1 8 x 8 x1024 -> 8 x 8 x 512 0.067 BFLOPs
64 conv 1024 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x1024 0.604 BFLOPs
65 res 62 8 x 8 x1024 -> 8 x 8 x1024
66 conv 512 1 x 1 / 1 8 x 8 x1024 -> 8 x 8 x 512 0.067 BFLOPs
67 conv 1024 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x1024 0.604 BFLOPs
68 res 65 8 x 8 x1024 -> 8 x 8 x1024
69 conv 512 1 x 1 / 1 8 x 8 x1024 -> 8 x 8 x 512 0.067 BFLOPs
70 conv 1024 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x1024 0.604 BFLOPs
71 res 68 8 x 8 x1024 -> 8 x 8 x1024
72 conv 512 1 x 1 / 1 8 x 8 x1024 -> 8 x 8 x 512 0.067 BFLOPs
73 conv 1024 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x1024 0.604 BFLOPs
74 res 71 8 x 8 x1024 -> 8 x 8 x1024
75 conv 512 1 x 1 / 1 8 x 8 x1024 -> 8 x 8 x 512 0.067 BFLOPs
76 conv 1024 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x1024 0.604 BFLOPs
77 conv 512 1 x 1 / 1 8 x 8 x1024 -> 8 x 8 x 512 0.067 BFLOPs
78 conv 1024 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x1024 0.604 BFLOPs
79 conv 512 1 x 1 / 1 8 x 8 x1024 -> 8 x 8 x 512 0.067 BFLOPs
80 conv 1024 3 x 3 / 1 8 x 8 x 512 -> 8 x 8 x1024 0.604 BFLOPs
81 conv 255 1 x 1 / 1 8 x 8 x1024 -> 8 x 8 x 255 0.033 BFLOPs
82 detection
83 route 79
84 conv 256 1 x 1 / 1 8 x 8 x 512 -> 8 x 8 x 256 0.017 BFLOPs
85 upsample 2x 8 x 8 x 256 -> 16 x 16 x 256
86 route 85 61
87 conv 256 1 x 1 / 1 16 x 16 x 768 -> 16 x 16 x 256 0.101 BFLOPs
88 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
89 conv 256 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 256 0.067 BFLOPs
90 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
91 conv 256 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 256 0.067 BFLOPs
92 conv 512 3 x 3 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.604 BFLOPs
93 conv 255 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 255 0.067 BFLOPs
94 detection
95 route 91
96 conv 128 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x 128 0.017 BFLOPs
97 upsample 2x 16 x 16 x 128 -> 32 x 32 x 128
98 route 97 36
99 conv 128 1 x 1 / 1 32 x 32 x 384 -> 32 x 32 x 128 0.101 BFLOPs
100 conv 256 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.604 BFLOPs
101 conv 128 1 x 1 / 1 32 x 32 x 256 -> 32 x 32 x 128 0.067 BFLOPs
102 conv 256 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.604 BFLOPs
103 conv 128 1 x 1 / 1 32 x 32 x 256 -> 32 x 32 x 128 0.067 BFLOPs
104 conv 256 3 x 3 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.604 BFLOPs
105 conv 255 1 x 1 / 1 32 x 32 x 256 -> 32 x 32 x 255 0.134 BFLOPs
106 detection
Loading weights from yolov3.weights...Done!
data/dog.jpg: Predicted in 0.066842 seconds.
dog: 100%
truck: 51%
car: 77%
bicycle: 74%
This section applies to git tag v5.3 and earlier. ie, Yolov2.
- Download latest commit of darknet-cpp, ex
git clone https://github.com/prabindh/darknet
- Create Yolo compatible training data-set. I use this to create Yolo compatible bounding box format file, and training list file.
https://github.com/prabindh/euclid
This creates a training list file (train.txt) that will be needed in next step of training.
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Change the files per below:
- yolo-voc.cfg - change line classes=20 to suit desired number of classes
- yolo-voc.cfg - change the number of filters in the CONV layer above the region layer - (#classes + 4 + 1)*(5), where 4 is '#of coords', and 5 is 'num' in the cfg file.
- voc.data - change line classes=20, and paths to training image list file
- voc.names - number of lines must be equal the number of classes
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Place label-images corresponding to name of classes in data/labels, ex - data/labels/myclassname1.png
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Download http://pjreddie.com/media/files/darknet19_448.conv.23
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Train as below
./darknet-cpp detector train ./cfg/voc-myclasses.data ./cfg/yolo-myconfig.cfg darknet19_448.conv.23- Atleast for the few initial iterations, observe the log output, and ensure all images are found and being used. After convergence, detection can be performed using standard steps.
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Testing with Arapaho C++ API for detection
Arapaho needs the darknet-cpp shared library (.so file on Linux, .dll on Windows). This can be built as below on Linux.
make darknet-cpp-sharedOn Windows, the .dll is built by default. The windows build requires the .sln files from (https://github.com/prabindh/darknet-cpp-windows)
Refer the file https://github.com/prabindh/darknet/blob/master/arapaho/arapaho_readme.txt for more details on running Arapaho.
If there is a need to report an issue with the darknet-cpp port, use the link - https://github.com/prabindh/darknet/issues.
Information required for filing an issue:
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Output of
git log --format="%H" -n 1 -
Options enabled in Makefile (GPU,CUDNN)
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If using Arapaho C++ wrapper, what options were used to build
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Platform being used (OS version, GPU type, CUDA version, and OpenCV version)
Currently tested with VS2015, CUDA8.0 on Win10 upto RS5.
The solution file requires the below repository.
https://github.com/prabindh/darknet-cpp-windows
The Windows port does not require any additional downloads (like pthreads), and builds the same darknet code-base for Windows, to generate the darknet.dll. Building the Arapaho C++ API and test wrapper, creates arapaho.exe, that works exactly the same way as arapaho on Linux.
Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.
For more information see the Darknet project website.
For questions or issues please use the Google Group.