Darknet Object Detection Framework and YOLO
Papers
General Information
Building
- Linux CMake Method
- Windows CMake Method
Using Darknet
- CLI
- Training
Other Tools and Links
Roadmap

Darknet Object Detection Framework and YOLO

Darknet is an open source neural network framework written in C, C++, and CUDA.

YOLO (You Only Look Once) is a state-of-the-art, real-time, object detection system, which runs in the Darknet framework.

Read how Hank.ai is helping the Darknet/YOLO community
See the Darknet/YOLO web site: https://darknetcv.ai/
Please read through the Darknet/YOLO FAQ: https://www.ccoderun.ca/programming/darknet_faq/
Join the Darknet/YOLO discord server: https://discord.gg/zSq8rtW

Papers

Paper YOLOv7: https://arxiv.org/abs/2207.02696
Paper Scaled-YOLOv4: https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html
Paper YOLOv4: https://arxiv.org/abs/2004.10934
Paper YOLOv3: https://arxiv.org/abs/1804.02767

General Information

YOLOv7 is more accurate and faster than YOLOv5 by 120% FPS, than YOLOX by 180% FPS, than Dual-Swin-T by 1200% FPS, than ConvNext by 550% FPS, than SWIN-L by 500% FPS, and PPYOLOE-X by 150% FPS.

YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100, batch=1.

Building

The various build methods available in the past have been merged together into a single unified solution. Darknet requires OpenCV, and uses CMake to generate the necessary project files.

Linux
Windows

Beware if you are following old tutorials with more complicated build steps, or build steps that don't match what is in this readme. The new build steps as described below started in August 2023.

Linux CMake Method

Optional: If you have a modern NVIDIA GPU, you can install either CUDA or CUDA+cuDNN at this point. If installed, Darknet will use your GPU to speed up image (and video) processing.
- Visit https://developer.nvidia.com/cuda-downloads to download and install CUDA.
- Visit https://developer.nvidia.com/rdp/cudnn-download or https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#cudnn-package-manager-installation-overview to download and install cuDNN.
- If you install CUDA or CUDA+cuDNN at a later time, or you upgrade to a newer version of the NVIDIA software:
  - You must delete the CMakeCache.txt file from your Darknet build directory to force CMake to re-find all of the necessary files.
  - Remember to re-build Darknet.

These instructions assume a system running Ubuntu 22.04.

sudo apt-get install build-essential git libopencv-dev cmake
mkdir ~/src
cd ~/src
git clone https://github.com/hank-ai/darknet
cd darknet
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j4 package
sudo dpkg -i darknet-VERSION.deb

If you are using an older version of CMake, such as the one with Ubuntu 18.04 or 20.04, then you'll need to upgrade CMake before you can run the cmake command above. Upgrading CMake on Ubuntu can be done with the following commands:

sudo apt-get purge cmake
sudo snap install cmake --classic

Advanced users:

If you want to build a RPM installation file instead of a DEB file, see the relevant lines in CM_package.cmake. Prior to running make -j4 package you'll need to edit these two lines:

SET (CPACK_GENERATOR "DEB")
# SET (CPACK_GENERATOR "RPM")

For distros such as Centos and OpenSUSE, you'll need to switch those two lines in CM_package.cmake to be:

# SET (CPACK_GENERATOR "DEB")
SET (CPACK_GENERATOR "RPM")

To install the installation package, use the usual package manager for your distribution. For example, on Debian-based systems such as Ubuntu:

sudo dpkg -i darknet-2.0.1-Linux.deb

Installing the package will copy the following files:

/usr/bin/darknet is the usual Darknet executable. Run darknet version from the CLI to confirm it is installed correctly.
/usr/include/darknet.h is the Darknet API for C, C++, and Python developers.
/usr/include/darknet_version.h contains version information for developers.
/usr/lib/libdarknet.so is the library to link against for C, C++, and Python developers.
/opt/darknet/cfg/... is where all the .cfg templates are stored.

You are now done! Darknet has been built and installed into /usr/bin/. Run this to test: darknet version.

Windows CMake Method

These instructions assume a brand new installation of Windows 11 22H2.

Open a normal cmd.exe command prompt window and run the following commands:

winget install Git.Git
winget install Kitware.CMake
winget install nsis.nsis
winget install Microsoft.VisualStudio.2022.Community

At this point we need to modify the Visual Studio installation to include support for C++ applications:

click on the "Windows Start" menu and run "Visual Studio Installer"
click on Modify
select Desktop Development With C++
click on Modify in the bottom-right corner, and then click on Yes

Once everything is downloaded and installed, click on the "Windows Start" menu again and select Developer Command Prompt for VS 2022.

Advanced users:

Instead of running the Developer Command Prompt, you can use a normal command prompt or ssh into the device and manually run "\Program Files\Microsoft Visual Studio\2022\Community\Common7\Tools\VsDevCmd.bat".

Run the following commands to install Microsoft VCPKG, which will then be used to build OpenCV:

cd c:\
mkdir c:\src
cd c:\src
git clone https://github.com/microsoft/vcpkg
cd vcpkg
bootstrap-vcpkg.bat
.\vcpkg.exe integrate install
.\vcpkg.exe integrate powershell
.\vcpkg.exe install opencv[contrib,dnn,freetype,jpeg,openmp,png,webp,world]:x64-windows pthreads:x64-windows

Be patient at this last step as it can take a long time to run. It needs to download and build many things.

Advanced users:

Note there are many other optional modules you may want to add when building OpenCV. Run .\vcpkg.exe search opencv to see the full list.

Optional: If you have a modern NVIDIA GPU, you can install either CUDA or CUDA+cuDNN at this point. If installed, Darknet will use your GPU to speed up image (and video) processing.
- Visit https://developer.nvidia.com/cuda-downloads to download and install CUDA.
- Visit https://developer.nvidia.com/rdp/cudnn-download or https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#download-windows to download and install cuDNN.
- If you install CUDA or CUDA+cuDNN at a later time, or you upgrade to a newer version of the NVIDIA software:
  - You must delete the CMakeCache.txt file from your Darknet build directory to force CMake to re-find all of the necessary files.
  - Remember to re-build Darknet.
- CUDA must be installed after Visual Studio. If you upgrade Visual Studio, remember to re-install CUDA.

Once all of the previous steps have finished successfully, you need to clone Darknet and build it. During this step we also need to tell CMake where vcpkg is located so it can find OpenCV and other dependencies:

cd c:\src
git clone https://github.com/hank-ai/darknet.git
cd darknet
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=C:/src/vcpkg/scripts/buildsystems/vcpkg.cmake ..
msbuild.exe /property:Platform=x64;Configuration=Release /target:Build -maxCpuCount -verbosity:normal -detailedSummary darknet.sln

Advanced users:

Note that the output of the cmake command in the previous step is a normal Visual Studio solution file, Darknet.sln. If you are a software developer who regularly uses the Visual Studio GUI instead of msbuild.exe to build projects, you can ignore the command-line and load the Darknet project in Visual Studio or VS Code.

If you want, you can stop here. You should now have this file you can run: C:\src\Darknet\build\src\Release\Darknet.exe. Run this to test: C:\src\Darknet\build\src\Release\Darknet.exe version.

To properly package up Darknet, the libraries, the include files, and the necessary DLLs, create the NSIS installation package like this:

msbuild.exe /property:Platform=x64;Configuration=Release PACKAGE.vcxproj

This will create a darknet-VERSION.exe file in the build directory.

If you get an error about some missing CUDA DLLs such as cublas64_12.dll, then manually copy the CUDA .dll files into the same output directory as Darknet.exe. For example:
copy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.2\bin\*.dll" src\Release\
Similarly, if you get an error about CUDNN, then manually copy the CUDNN .dll file into the same output directory. For example:
copy "C:\Program Files\NVIDIA\CUDNN\v8.x\bin\cudnn64_8.dll" src\Release\
Once the files have been copied, re-run the last command to generate the NSIS installation package.

Installing the NSIS installation package will:

Create a directory called Darknet, such as C:\Program Files\Darknet\.
Install the darknet.exe CLI application, as well as required .dll files
Install the neccesary Darknet .dll, .lib and .h files to use darknet.dll from another application.
Install the template .cfg files.

You are now done! Darknet has been built and installed into C:\Program Files\Darknet\. Run this to test: C:\Program Files\Darknet\bin\darknet.exe version.

Using Darknet

CLI

This is not the full list of all commands supported by Darknet. See the previous readme for additional details and examples.

Check the version: darknet version
Predict using an image: darknet detector test animals.data animals.cfg animals_best.weights dog.jpg
Output coordinates: darknet detector test animals.data animals.cfg animals_best.weights -ext_output dog.jpg
Working with videos: darknet detector demo animals.data animals.cfg animals_best.weights -ext_output test.mp4
Reading from a webcam: darknet detector demo animals.data animals.cfg animals_best.weights -c 0
Smart webcam: darknet detector demo animals.data animals.cfg animals_best.weights http://192.168.0.80:8080/video?dummy=param.mjpg
Save results to a video: darknet detector demo animals.data animals.cfg animals_best.weights test.mp4 -out_filename res.avi
JSON and MJPEG server: darknet detector demo animals.data animals.cfg animals_best.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output
Running on a specific GPU: darknet detector demo animals.data animals.cfg animals_best.weights -i 1 test.mp4
To check accuracy mAP@IoU=50: darknet detector map animals.data animals.cfg animals_best.weights
To check accuracy mAP@IoU=75: darknet detector map animals.data animals.cfg animals_best.weights -iou_thresh 0.75
Train a new network: darknet detector -map -dont_show train animals.data animals.cfg (also see the training section below

Training

The simplest way to annotate and train is with the use of DarkMark to create all of the necessary files.

If you'd rather manually setup the various files to train a custom network:

Create a new folder where the files will be stored. For this example, a neural network will be created to detect animals, so the following directory is created: ~/nn/animals/.
Copy one of the Darknet configuration files you'd like to use as a template. For example, see cfg/yolov4-tiny.cfg. Place this in the folder you created. For this example, we now have ~/nn/animals/animals.cfg.
Create a animals.names text file in the same folder where you placed the configuration file. For this example, we now have ~/nn/animals/animals.cfg.
Edit the animals.names file with your text editor. List the classes you want to use. You need to have exactly 1 entry per line, with no blank lines and no comments. For this example, the .names file will contain:

    dog
    cat
    bird
    horse

Create a animals.data text file in the same folder. For this example, the .data file will contain:

    classes = 4
    train = /home/username/nn/animals/animals_train.txt
    valid = /home/username/nn/animals/animals_valid.txt
    names = /home/username/nn/animals/animals.names
    backup = /home/username/nn/animals

Create a folder where you'll store your images and annotations. For example, this could be ~/nn/animals/dataset. Each image will need a coresponding .txt file which describes the annotations for that image. The format of the .txt annotation files is very specific. You cannot create these files by hand since each annotation needs to contain the exact coordinates for the annotation. See DarkMark or other similar software to annotate your images.
Create the "train" and "valid" text files named in the .data file. These two text files need to individually list all of the images which Darknet must use to train and for validation when calculating the mAP%. Exactly one image per line. The path and filenames may be relative or absolute.
Modify your .cfg file with a text editor.
- Make sure that batch=64.
- Note the subdivisions. Depending on the network dimensions and the amount of memory available on your GPU, you may need to increase the subdivisions. The best value to use is 1 so start with that.
- Note max_batches=.... A good value to use when starting out is 2000 x the number of classes. For this example, we have 4 animals, so 4 * 2000 = 8000. For this example, we'll use max_batches=8000.
- Note steps=.... This should be set to 80% and 90% of max_batches. For this example we'd use steps=6400,7200.
- Note width=... and height=.... These are the network dimensions. The FAQ explains how to calculate the best size to use.
- Search for all instances of the line classes=... and modify it with the number of classes in your .names file. For this example, we'd use classes=4.
- Search for all instances of the line filters=... in the [convolutional] section prior to each [yolo] section. The value to use is (number_of_classes + 5) * 3. Meaning for this example, (4 + 5) * 3 = 27. So we'd use filters=27 on the appropriate lines.
Start training! Run the following commands:

    cd ~/nn/animals/
    darknet detector -map -dont_show train animals.data animals.cfg

Be patient. The best weights will be stored in animals_best.weights. And the progress of training can be observed by viewing the chart.png file.

Roadmap

Last updated 2023-08-30:

Short-term goals

Mid-term goals

better use of cv::Mat instead of the custom image structure in C
on-going code cleanup

Long-term goals

fix CUDA/CUDNN issues with all GPUs
look into adding support for non-NVIDIA GPUs

drvirens / darknet

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