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.
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.
version
command. From 2023 until mid-2024, it returned version 2.0.
version
command now returns 3.0.
Several popular versions of YOLO were pre-trained for convenience on the MSCOCO dataset. This dataset has 80 classes, which can be seen in the text file cfg/coco.names
.
There are several other simpler datasets and pre-trained weights available for testing Darknet/YOLO, such as LEGO Gears and Rolodex. See the Darknet/YOLO FAQ for details.
The MSCOCO pre-trained weights can be downloaded from several different locations, and are also available for download from this repo:
The MSCOCO pre-trained weights are provided for demo-purpose only. The corresponding @p .cfg and @p .names files for MSCOCO are in the cfg directory. Example commands:
wget --no-clobber https://github.com/hank-ai/darknet/releases/download/v2.0/yolov4-tiny.weights
darknet_02_display_annotated_images coco.names yolov4-tiny.cfg yolov4-tiny.weights image1.jpg
darknet_03_display_videos coco.names yolov4-tiny.cfg yolov4-tiny.weights video1.avi
DarkHelp coco.names yolov4-tiny.cfg yolov4-tiny.weights image1.jpg
DarkHelp coco.names yolov4-tiny.cfg yolov4-tiny.weights video1.avi
Note that people are expected to train their own networks. MSCOCO is normally used just to confirm that everything is working correctly.
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.
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.
Software developers are encouraged to visit https://darknetcv.ai/ to get information on the internals of the Darknet/YOLO object detection framework.
The Google Colab instructions are the same as the Linux instructions. Several Jupyter notebooks are available showing how to do certain tasks, such as training a new network.
See the notebooks in the colab
subdirectory.
nvcc
and nvidia-smi
. You may have to modify your PATH
variable.CMakeCache.txt
file from your Darknet build
directory to force CMake to re-find all of the necessary files.TODO: is libomp-dev also necessary for OpenMP?
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 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
If using bash
as your command shell, you'll want to re-start your shell at this point. If using fish
, it should immediately pick up the new path.
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 runningmake -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
.
If you don't have /usr/bin/darknet
then this means you _did not_ install it, you only built it! Make sure you install the
.debor
.rpm` file as described above.
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:
Modify
Desktop Development With C++
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
. Do not use PowerShell for these steps, you will run into problems!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"
.
Once you have the Developer Command Prompt running as described above, 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
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.
nvcc.exe
and nvidia-smi.exe
. You may have to modify your PATH
variable.C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/[version]/
. You may need to overwrite some files.CMakeCache.txt
file from your Darknet build
directory to force CMake to re-find all of the necessary files.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
msbuild.exe /property:Platform=x64;Configuration=Release PACKAGE.vcxproj
If you get an error about some missing CUDA or cuDNN 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-cli\Release\
(That is an example! Check to make sure what version you are running, and run the command that is appropriate for what you have installed.)
Once the files have been copied, re-run the last msbuild.exe
command to generate the NSIS installation package:
msbuild.exe /property:Platform=x64;Configuration=Release PACKAGE.vcxproj
Advanced users:
Note that the output of the
cmake
command is a normal Visual Studio solution file,Darknet.sln
. If you are a software developer who regularly uses the Visual Studio GUI instead ofmsbuild.exe
to build projects, you can ignore the command-line and load the Darknet project in Visual Studio.
You should now have this file you can run: C:\src\Darknet\build\src-cli\Release\darknet.exe
. Run this to test: C:\src\Darknet\build\src-cli\Release\darknet.exe version
.
To correctly install Darknet, the libraries, the include files, and the necessary DLLs, run the NSIS installation wizard that was built in the last step. See the file darknet-VERSION.exe
in the build
directory. For example:
darknet-2.0.31-win64.exe
Installing the NSIS installation package will:
Darknet
, such as C:\Program Files\Darknet\
.darknet.exe
..dll
files, such as those from OpenCV..dll
, .lib
and .h
files to use darknet.dll
from another application..cfg
files.You are now done! Once the installation wizard has finished, Darknet will have been installed into C:\Program Files\Darknet\
. Run this to test: C:\Program Files\Darknet\bin\darknet.exe version
.
If you don't have
C:/Program Files/darknet/bin/darknet.exe
then this means you did not install it, you only built it! Make sure you go through each panel of the NSIS installation wizard in the previous step.
The following is not the full list of all commands supported by Darknet.
In addition to the Darknet CLI, also note the DarkHelp project CLI which provides an alternative CLI to Darknet/YOLO. The DarkHelp CLI also has several advanced features that are not available directly in Darknet. You can use both the Darknet CLI and the DarkHelp CLI together, they are not mutually exclusive.
For most of the commands shown below, you'll need the .weights
file with the corresponding .names
and .cfg
files. You can either train your own network (highly recommended!) or download a neural network that someone has already trained and made available for free on the internet. Examples of pre-trained datasets include:
Commands to run include:
List some possible commands and options to run:
darknet help
Check the version:
darknet version
Predict using an image:
darknet detector test cars.data cars.cfg cars_best.weights image1.jpg
darknet_02_display_annotated_images cars.cfg image1.jpg
DarkHelp cars.cfg cars.cfg cars_best.weights image1.jpg
Output coordinates:
darknet detector test animals.data animals.cfg animals_best.weights -ext_output dog.jpg
darknet_01_inference_images animals dog.jpg
DarkHelp --json animals.cfg animals.names animals_best.weights dog.jpg
Working with videos:
darknet detector demo animals.data animals.cfg animals_best.weights -ext_output test.mp4
darknet_03_display_videos animals.cfg test.mp4
DarkHelp animals.cfg animals.names animals_best.weights test.mp4
Reading from a webcam:
darknet detector demo animals.data animals.cfg animals_best.weights -c 0
darknet_08_display_webcam animals
Save results to a video:
darknet detector demo animals.data animals.cfg animals_best.weights test.mp4 -out_filename res.avi
darknet_05_process_videos_multithreaded animals.cfg animals.names animals_best.weights test.mp4
DarkHelp animals.cfg animals.names animals_best.weights test.mp4
JSON:
darknet detector demo animals.data animals.cfg animals_best.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output
darknet_06_images_to_json animals image1.jpg
DarkHelp --json animals.names animals.cfg animals_best.weights image1.jpg
Running on a specific GPU:
darknet detector demo animals.data animals.cfg animals_best.weights -i 1 test.mp4
To check the accuracy of the neural network:
darknet detector map driving.data driving.cfg driving_best.weights
...
Id Name AvgPrecision TP FN FP TN Accuracy ErrorRate Precision Recall Specificity FalsePosRate
-- ---- ------------ ------ ------ ------ ------ -------- --------- --------- ------ ----------- ------------
0 vehicle 91.2495 32648 3903 5826 65129 0.9095 0.0905 0.8486 0.8932 0.9179 0.0821
1 motorcycle 80.4499 2936 513 569 5393 0.8850 0.1150 0.8377 0.8513 0.9046 0.0954
2 bicycle 89.0912 570 124 104 3548 0.9475 0.0525 0.8457 0.8213 0.9715 0.0285
3 person 76.7937 7072 1727 2574 27523 0.8894 0.1106 0.7332 0.8037 0.9145 0.0855
4 many vehicles 64.3089 1068 509 733 11288 0.9087 0.0913 0.5930 0.6772 0.9390 0.0610
5 green light 86.8118 1969 239 510 4116 0.8904 0.1096 0.7943 0.8918 0.8898 0.1102
6 yellow light 82.0390 126 38 30 1239 0.9525 0.0475 0.8077 0.7683 0.9764 0.0236
7 red light 94.1033 3449 217 451 4643 0.9237 0.0763 0.8844 0.9408 0.9115 0.0885
To check accuracy mAP@IoU=75:
darknet detector map animals.data animals.cfg animals_best.weights -iou_thresh 0.75
Recalculating anchors is best done in DarkMark, since it will run 100 consecutive times and select the best anchors from all the ones that were calculated. But if you want to run the old version in Darknet:
darknet detector calc_anchors animals.data -num_of_clusters 6 -width 320 -height 256
Train a new network:
darknet detector -map -dont_show train animals.data animals.cfg
(also see the training section below)Quick links to relevant sections of the Darknet/YOLO FAQ:
The simplest way to annotate and train is with the use of DarkMark to create all of the necessary Darknet files. This is definitely the recommended way to train a new neural network.
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.names
.
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 exactly 4 lines:
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. The YOLO annotation format is described in the Darknet/YOLO FAQ.
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.
batch=64
.1
so start with that. See the Darknet/YOLO FAQ if 1
doesn't work for you.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. Meaning we'll use max_batches=8000
.steps=...
. This should be set to 80% and 90% of max_batches
. For this example we'd use steps=6400,7200
since max_batches
was set to 8000.width=...
and height=...
. These are the network dimensions. The Darknet/YOLO FAQ explains how to calculate the best size to use.classes=...
and modify it with the number of classes in your .names
file. For this example, we'd use classes=4
.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 saved as animals_best.weights
. And the progress of training can be observed by viewing the chart.png
file. See the Darknet/YOLO FAQ for additional parameters you may want to use when training a new network.
If you want to see more details during training, add the --verbose
parameter. For example:
darknet detector -map -dont_show --verbose train animals.data animals.cfg
Last updated 2024-09-21:
cv::Mat
to void*
but use it as a proper C++ objectimage
structure gets usedchar*
code and replace with std::string
cv::Mat
instead of the custom image
structure in C (in progress)list
functionality with std::vector
or std::list