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Darknet Object Detection Framework and YOLO

darknet logo hank.ai logo

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 target detection system that runs in the Darknet framework.

Read how Hank.ai helps the Darknet/YOLO community

Announcing Darknet V3 "Jazz"

Check out the Darknet/YOLO website

Please read the Darknet/YOLO FAQ

Join the Darknet/YOLO Discord server

Papers

1. Paper YOLOv7

2. Paper Scaled-YOLOv4

3. Paper YOLOv4

4. Paper YOLOv3

General Information

The Darknet/YOLO framework is still faster and more accurate than other frameworks and YOLO versions.

This framework is completely free and open source. You can integrate Darknet/YOLO into existing projects and products - including commercial products - without licensing or fees.

Darknet V3 ("Jazz"), released in October 2024, can accurately run LEGO dataset videos at up to 1000 FPS using NVIDIA RTX 3090 GPUs, meaning each video frame is captured by Darknet in 1 millisecond or less /YOLO Read, resize and process.

If you need help or want to discuss Darknet/YOLO, please join the Darknet/YOLO Discord server: https://discord.gg/zSq8rtW

The CPU version of Darknet/YOLO can run on simple devices such as Raspberry Pi, cloud and collaboration servers, desktops, laptops and high-end training equipment. The GPU version of Darknet/YOLO requires a CUDA-compatible GPU from NVIDIA.

Darknet/YOLO is known to work well on Linux, Windows, and Mac. See build instructions below.

Darknet version

The original Darknet tools written by Joseph Redmon in 2013-2017 did not have version numbers. We consider this version to be 0.x.

The next popular Darknet repository maintained by Alexey Bochkovskiy from 2017-2021 also has no version number. We believe this version is 1.x.

The Darknet repository sponsored by Hank.ai and maintained by Stéphane Charette from 2023 is the first to have a version command. From 2023 to the end of 2024, it returns to version 2.x "OAK".

The goal is to try to break existing functionality as little as possible while getting familiar with the code base.

1. Rewrite the build steps so that we have a unified way to build on Windows and Linux using CMake.

2. Convert the code base to use a C++ compiler.

3. Enhanced chart.png during training.

4. Bug fixes and performance-related optimizations, mainly related to reducing the time required to train the network.

The last branch of the code base is version 2.1 in the v2 branch.

The next phase of development begins in mid-2024 and will be released in October 2024. The version command now returns 3.x "JAZZ".

You can always checkout the previous v2 branch if you need to run the following command. Please let us know so we can investigate adding any missing commands.

1. Removed many old and unmaintained commands.

2. Many performance optimizations, including training and inference processes.

3. The traditional C API has been modified; applications using the original Darknet API will need to make minor modifications: https://darknetcv.ai/api/api.html

4. New Darknet V3 C and C++ API: https://darknetcv.ai/api/api.html

5. New applications and sample code in src-examples: https://darknetcv.ai/api/files.html

MSCOCO pre-trained weights

For convenience, several popular versions of YOLO are pre-trained on the MSCOCO dataset. This data set has 80 categories and 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. For more information, see the Darknet/YOLO FAQ.

MSCOCO pre-trained weights can be downloaded from a number of different locations or from this repository:

1. YOLOv2, November 2016

-YOLOv2-tiny

-YOLOv2-full

2. YOLOv3, May 2018

- YOLOv3-tiny

-YOLOv3-full

3. YOLOv4, May 2020

- YOLOv4-tiny

-YOLOv4-full

4. YOLOv7, August 2022

-YOLOv7-tiny

-YOLOv7-full

MSCOCO pretrained weights are for demonstration purposes only. The corresponding .cfg and .names files for MSCOCO are located in the cfg directory. Example command:

`bash

wget --no-clobber https://github.com/hank-ai/darknet/releases/download/v2.0/yolov4-tiny.weights darknet02displayannotatedimages coco.names yolov4-tiny.cfg yolov4-tiny.weights image1.jpg darknet03display_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 one should train their own network. MSCOCO is often used to confirm that everything is OK.

build

Various building methods from the past (pre-2023) have been merged into a unified solution. Darknet requires C++17 or higher, OpenCV, and using CMake to generate the necessary project files.

You don't need to know C++ to build, install, or run Darknet/YOLO, just like you don't need to be a mechanic to drive a car.

Google Colab

Google Colab instructions are the same as Linux instructions. There are several Jupyter notebooks showing how to perform certain tasks, such as training a new network.

See the notebook in the colab subdirectory, or follow the Linux instructions below.

Linux CMake method

Darknet build tutorial for Linux

Optional: If you have a modern NVIDIA GPU, you can install CUDA or CUDA+cuDNN at this time. If installed, Darknet will use your GPU to accelerate image (and video) processing.

You must delete the CMakeCache.txt file in the Darknet build directory to force CMake to re-find all necessary files.

Remember to rebuild Darknet.

Darknet can be run without it, but if you want to train a custom network, you need CUDA or CUDA+cuDNN.

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.

After installing CUDA, make sure you can run nvcc and nvidia-smi. You may need to modify the PATH variable.

If you install CUDA or CUDA+cuDNN later, or if you upgrade to a newer version of NVIDIA software, do the following:

These instructions assume (but do not require!) a system running Ubuntu 22.04. If using another distribution, adjust as needed.

`bash

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

`

If you are using an older version of CMake, you will need to upgrade CMake before running the cmake command above. Upgrading CMake on Ubuntu can be done using the following command:

`bash

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

`

If using bash as your command shell, you will need to restart the shell at this time. If you use fish, it should pick up the new path immediately.

Advanced users:

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

`bash

SET (CPACKGENERATOR "DEB")# SET (CPACKGENERATOR "RPM")

`

For distributions such as Centos and OpenSUSE, you need to change these two lines in CM_package.cmake to:

`bash

SET (CPACKGENERATOR "DEB")SET (CPACKGENERATOR "RPM")

`

To install the installation package, once it has been built, use your distribution's usual package manager. For example, on a Debian-based system such as Ubuntu:

`bash

sudo dpkg -i darknet-2.0.1-Linux.deb

`

Installing the .deb package will copy the following files:

1. /usr/bin/darknet is the usual Darknet executable file. Run darknet version from the CLI to confirm that it is installed correctly.

2. /usr/include/darknet.h is the Darknet API for C, C++ and Python developers.

3. /usr/include/darknet_version.h contains version information for developers.

4. /usr/lib/libdarknet.so is a library for C, C++ and Python developers.

5. /opt/darknet/cfg/... is the location where all .cfg templates are stored.

You are done now! Darknet is built and installed into /usr/bin/. Run the following command to test: darknet version.

If you don't have /usr/bin/darknet, you didn't install it, you just built it! Make sure to install the .deb or .rpm file as described above.

Windows CMake methods

These instructions assume a fresh installation of Windows 11 22H2.

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

`bash

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:

1. Click the Windows Start menu and run Visual Studio Setup.

2. Click Edit.

3. Choose desktop development using C++.

4. Click "Edit" in the lower right corner, then click "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 to perform these steps, you will run into problems!

Advanced users:

Instead of running the developer command prompt, you can use a normal command prompt or log in to the device using ssh and run "Program FilesMicrosoft Visual Studio2022CommunityCommon7ToolsVsDevCmd.bat" manually.

Once you have the developer command prompt running as described above (not PowerShell!), run the following command to install Microsoft VCPKG and then use it to build OpenCV:

`bash

cd c:mkdir c:srccd c:src git clone https://github.com/microsoft/vcpkgcd 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 with this last step as it may take a long time to run. It requires downloading and building a lot of stuff.

Advanced users:

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

Optional: If you have a modern NVIDIA GPU, you can install CUDA or CUDA+cuDNN at this time. If installed, Darknet will use your GPU to accelerate image (and video) processing.

You must delete the CMakeCache.txt file in the Darknet build directory to force CMake to re-find all necessary files.

Remember to rebuild Darknet.

Darknet can be run without it, but if you want to train a custom network, you need CUDA or CUDA+cuDNN.

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.

After installing CUDA, make sure you can run nvcc.exe and nvidia-smi.exe. You may need to modify the PATH variable.

After downloading cuDNN, unzip and copy the bin, include, and lib directories to C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/[version]/. You may need to overwrite some files.

If you install CUDA or CUDA+cuDNN later, or if you upgrade to a newer version of NVIDIA software, do the following:

CUDA must be installed after Visual Studio. If you upgrade Visual Studio, remember to reinstall CUDA.

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

`bash

cd c:src git clone https://github.com/hank-ai/darknet.gitcd darknetmkdir buildcd build cmake -DCMAKEBUILDTYPE=Release -DCMAKETOOLCHAINFILE=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 receive an error about some missing CUDA or cuDNN DLL (such as cublas64_12.dll), manually copy the CUDA .dll file to the same output directory as Darknet.exe. For example:

`bash

copy "C:Program FilesNVIDIA GPU Computing ToolkitCUDAv12.2bin*.dll" src-cliRelease

`

(This is an example! Please check to make sure which version you are running and run the appropriate command for what you have installed.)

After copying the files, rerun the last msbuild.exe command to generate the NSIS installation package:

`bash

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 frequently 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.

You should now have the following file that you can run: C:srcDarknetbuildsrc-cliReleasedarknet.exe. Run the following command to test: C:srcDarknetbuildsrc-cliReleasedarknet.exe version.

To properly install Darknet, libraries, include files, and necessary DLLs, run the NSIS installation wizard built in the last step. Check the file darknet-VERSION.exe in the build directory. For example:

`bash

darknet-2.0.31-win64.exe

`

Installing the NSIS installation package will:

1. Create a directory named Darknet, for example C:Program FilesDarknet.

2. Install the CLI application, darknet.exe, and other sample applications.

3. Install the required third-party .dll files, such as those from OpenCV.

4. Install the necessary Darknet .dll, .lib, and .h files to use darknet.dll from another application.

5. Install the template .cfg file.

You are done now! After the installation wizard is completed, Darknet will be installed in C:Program FilesDarknet. Run the following command to test: C:Program FilesDarknetbindarknet.exe version.

If you don't have C:/Program Files/darknet/bin/darknet.exe, you didn't install it, you just built it! Be sure to complete each panel of the NSIS Installation Wizard as described in the previous steps.

Using Darknet

CLI

The following is not a complete list of all commands supported by Darknet.

In addition to the Darknet CLI, please note the DarkHelp project CLI, which provides an alternative to the Darknet/YOLO CLI. DarkHelp CLI also has several advanced features not directly available in Darknet. You can use the Darknet CLI and DarkHelp CLI together, they are not mutually exclusive.

For most of the commands shown below, you need a .weights file with corresponding .names and .cfg files. You can either train your own network (highly recommended!) or download a neural network from the Internet that has been trained by others and is available for free. Examples of pre-training datasets include:

1. LEGO Gears (find objects in images)

2. Rolodex (find text in image)

3. MSCOCO (standard 80 category target detection)

Commands to run include:

List some commands and options that may be run:

`bash

darknet help

`

Check version:

`bash

darknet version

`

Use images to make predictions:

V2:

`bash

darknet detector test cars.data cars.cfg cars_best.weights image1.jpg

`

V3:

`bash

darknet02displayannotatedimages cars.cfg image1.jpg

`

DarkHelp:

`bash

DarkHelp cars.cfg cars.cfg cars_best.weights image1.jpg

`

Output coordinates:

V2:

`bash

darknet detector test animals.data animals.cfg animalsbest.weights -extoutput dog.jpg

`

V3:

`bash

darknet01inference_images animals dog.jpg

`

DarkHelp:

`bash

DarkHelp --json animals.cfg animals.names animals_best.weights dog.jpg

`

Use video:

V2:

`bash

darknet detector demo animals.data animals.cfg animalsbest.weights -extoutput test.mp4

`

V3:

`bash

darknet03display_videos animals.cfg test.mp4

`

DarkHelp:

`bash

DarkHelp animals.cfg animals.names animals_best.weights test.mp4

`

Reading from webcam:

V2:

`bash

darknet detector demo animals.data animals.cfg animals_best.weights -c 0

`

V3:

`bash

darknet08display_webcam animals

`

Save results to video:

V2:

`bash

darknet detector demo animals.data animals.cfg animalsbest.weights test.mp4 -outfilename res.avi

`

V3:

`bash

darknet05processvideosmultithreaded animals.cfg animals.names animals_best.weights test.mp4

`

DarkHelp:

`bash

DarkHelp animals.cfg animals.names animals_best.weights test.mp4

`

JSON:

V2:

`bash

darknet detector demo animals.data animals.cfg animalsbest.weights test50.mp4 -jsonport 8070 -mjpegport 8090 -extoutput

`

V3:

`bash

darknet06imagestojson animals image1.jpg

`

DarkHelp:

`bash

DarkHelp --json animals.names animals.cfg animals_best.weights image1.jpg

`

Run on a specific GPU:

V2:

`bash

darknet detector demo animals.data animals.cfg animals_best.weights -i 1 test.mp4

`

To check the accuracy of the neural network:

`bash

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 the accuracy of mAP@IoU=75:

`bash

darknet detector map animals.data animals.cfg animalsbest.weights -iouthresh 0.75

`

Recalculating anchor points is best done in DarkMark as it will run 100 times in a row and select the best anchor point from all calculated anchor points. But if you want to run an older version in Darknet, do the following:

`bash

darknet detector calcanchors animals.data -numof_clusters 6 -width 320 -height 256

`

Train a new network:

`bash

darknet detector -map -dont_show train animals.data animals.cfg (also see the training section below)

`

train

Quick links to relevant sections of the Darknet/YOLO FAQ:

1. How should I set up my files and directories?

2. Which profile should I use?

3. Which command should you use when training your own network?

Using DarkMark to create all necessary Darknet files is the easiest way to annotate and train. This is definitely the recommended way to train new neural networks.

If you want to manually set up the various files to train a custom network, do the following:

1. Create a new folder to store these files. For this example, a neural network will be created to detect animals, so the following directory will be created: ~/nn/animals/.

2. Copy one of the Darknet configuration files you want to use as a template. For example, see cfg/yolov4-tiny.cfg. Place it in the folder you created. For this example, we now have ~/nn/animals/animals.cfg.

3. Create an animals.names text file in the same folder where you placed the configuration file. For this example, we now have ~/nn/animals/animals.names.

4. Use your text editor to edit the animals.names file. List the categories you want to use. You need exactly one entry per line, no blank lines, and no comments. For this example, the .names file will contain exactly 4 lines:

`

dog

cat

bird

horse

`

5. Create an 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

`

6. Create a folder to store your images and annotations. For example, this could be ~/nn/animals/dataset. Each image requires a corresponding .txt file describing the annotations for that image. The format of .txt comment files is very specific. You cannot create these files manually because each annotation needs to contain the exact coordinates of the annotation. See DarkMark or other similar software to annotate your images. The YOLO annotation format is described in the Darknet/YOLO FAQ.

7. Create "train" and "valid" text files named in the .data file. These two text files need to list all the images that Darknet must use for training and validation mAP% respectively. There is exactly one image per row. Paths and filenames can be relative or absolute.

8. Use a text editor to modify your .cfg file.

- Make sure batch=64.

- Pay attention to subdivisions. Depending on the network size and the amount of memory available on the GPU, you may need to increase subdivisions. The optimal value is 1, so start with that. If 1 doesn't work for you, see the Darknet/YOLO FAQ.

- Note maxbatches=.... When starting out, a good value is 2000 for the number of categories. For this example we have 4 animals, so 4 2000 = 8000. This means we will use maxbatches=8000.

- Note steps=.... This should be set to 80% and 90% of maxbatches. For this example, we will use steps=6400,7200 since maxbatches is set to 8000.

- Note that width=... and height=.... these are network dimensions. The Darknet/YOLO FAQ explains how to calculate the optimal size to use.

- In each [yolo] section preceding the [convolutional] section, search for all instances of filters=... lines. The value to use is (number of categories + 5) 3. This means that for this example, (4 + 5) 3 = 27. Therefore, we use filters=27 on the appropriate lines.

9. Start training! Run the following command:

`bash

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. You can observe the training progress by viewing the chart.png file. See the Darknet/YOLO FAQ for additional parameters you may wish to use when training a new network.

- If you want to see more details during training, add the --verbose parameter. For example:

`bash

darknet detector -map -dont_show --verbose train animals.data animals.cfg

`