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Darknet Object Detection Framework and YOLO

[Darknet and Hank.ai logos]

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 that runs within the Darknet framework.

Read how Hank.ai is helping the Darknet/YOLO community

Announcing Darknet V3 "Jazz"

See the Darknet/YOLO website

Please read through 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 continues to be both faster and more accurate than other frameworks and versions of YOLO.

This framework is completely free and open source. You can incorporate Darknet/YOLO into existing projects and products, including commercial ones, without a license or paying a fee.

Darknet V3 ("Jazz"), released in October 2024, can accurately run the LEGO dataset videos at up to 1000 FPS when using an NVIDIA RTX 3090 GPU, meaning each video frame is read, resized, and processed by Darknet/YOLO in 1 millisecond or less.

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

The CPU version of Darknet/YOLO can run on simple devices like Raspberry Pi, cloud & colab servers, desktops, laptops, and high-end training rigs. The GPU version of Darknet/YOLO requires a CUDA-capable GPU from NVIDIA.

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

Darknet Version

The original Darknet tool written by Joseph Redmon in 2013-2017 did not have a version number. We consider this version 0.x.

The next popular Darknet repo maintained by Alexey Bochkovskiy between 2017-2021 also did not have a version number. We consider this version 1.x.

The Darknet repo sponsored by Hank.ai and maintained by Stéphane Charette starting in 2023 was the first one with a version command. From 2023 until late 2024, it returned version 2.x "OAK".

The goal was to try and break as little of the existing functionality while getting familiar with the codebase. Here are some key changes:

1. Re-wrote the build steps so we have 1 unified way to build using CMake on both Windows and Linux.

2. Converted the codebase to use the C++ compiler.

3. Enhanced chart.png while training.

4. Bug fixes and performance-related optimizations, mostly related to cutting down the time it takes to train a network.

The last branch of this codebase is version 2.1 in the v2 branch.

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

You can always do a checkout of the previous v2 branch if you need to run one of these commands. Let us know so we can investigate adding back any missing commands.

Here are some of the key changes in Darknet V3 "JAZZ":

1. Removed many old and unmaintained commands.

2. Many performance optimizations, both when training and during inference.

3. Legacy C API was modified. Applications that use the original Darknet API will need minor modifications: https://darknetcv.ai/api/api.html

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

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

MSCOCO Pre-trained Weights

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:

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

The MSCOCO pre-trained weights are provided for demo-purpose only. The corresponding .cfg and .names files for MSCOCO are in the cfg directory. Example commands:

`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 people are expected to train their own networks. MSCOCO is normally used to confirm that everything is working correctly.

Building

The various build methods available in the past (pre-2023) have been merged together into a single unified solution. Darknet requires C++17 or newer, OpenCV, and uses CMake to generate the necessary project files.

You do not need to know C++ to build, install, nor run Darknet/YOLO, the same way you don't need to be a mechanic to drive a car.

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.

Google Colab

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, and/or follow the Linux instructions below.

Linux CMake Method

Darknet build tutorial for Linux

1. 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.

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

3. Remember to re-build Darknet.

4. Darknet can run without it, but if you want to train a custom network then either CUDA or CUDA+cuDNN is required.

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.

Once you install CUDA make sure you can run nvcc and nvidia-smi. You may have to modify your PATH variable.

If you install CUDA or CUDA+cuDNN at a later time, or you upgrade to a newer version of the NVIDIA software:

These instructions assume (but do not require!) a system running Ubuntu 22.04. Adapt as necessary if you're using a different distribution.

`bash

sudo apt-get install build-essential git libopencv-dev cmake

mkdir ~/srccd ~/src

git clone https://github.com/hank-ai/darknet

cd darknet

mkdir build

cd build

cmake -DCMAKEBUILDTYPE=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:

`bash

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 an 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:

`bash

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

`

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

`bash

SET (CPACK_GENERATOR "DEB")

SET (CPACK_GENERATOR "RPM")

`

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

`bash

sudo dpkg -i darknet-2.0.1-Linux.deb

`

Installing the .deb 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 .deb or .rpm file as described above.

Windows CMake Method

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

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

`bash

winget install Git.Git

winget install Kitware.CMake

winget install nsis.nsis

winget install Microsoft.VisualStudio.2022.Community

`

2. 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.

3. 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 FilesMicrosoft Visual Studio2022CommunityCommon7ToolsVsDevCmd.bat".

4. Once you have the Developer Command Prompt running as described above (not PowerShell!), run the following commands to install Microsoft VCPKG, which will then be used to build OpenCV:

`bash

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.

5. 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.

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

7. Remember to re-build Darknet.

8. Darknet can run without it, but if you want to train a custom network then either CUDA or CUDA+cuDNN is required.

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.

Once you install CUDA make sure you can run nvcc.exe and nvidia-smi.exe. You may have to modify your PATH variable.

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

If you install CUDA or CUDA+cuDNN at a later time, or you upgrade to a newer version of the NVIDIA software:

CUDA must be installed after Visual Studio. If you upgrade Visual Studio, remember to re-install CUDA.

9. 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:

`bash

cd c:src

git clone https://github.com/hank-ai/darknet.git

cd darknet

mkdir build

cd 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

`

10. 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:

`bash

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

`

(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.)

11. Once the files have been copied, re-run 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 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.

You should now have this file you can run: C:srcDarknetbuildsrc-cliReleasedarknet.exe. Run this to test: C:srcDarknetbuildsrc-cliReleasedarknet.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:

`bash

darknet-2.0.31-win64.exe

`

Installing the NSIS installation package will:

Create a directory called Darknet, such as C:Program FilesDarknet.

Install the CLI application, darknet.exe and other sample apps.

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

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

Install the template .cfg files.

You are now done! Once the installation wizard has finished, Darknet will have been installed into C:Program FilesDarknet. Run this to test: C:Program FilesDarknetbindarknet.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.

Using Darknet

CLI

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:

1. LEGO Gears (finding objects in an image)

2. Rolodex (finding text in an image)

3. MSCOCO (standard 80-class object detection)

Commands to run include:

1. List some possible commands and options to run:

`bash

darknet help

`

2. Check the version:

`bash

darknet version

`

3. Predict using an image:

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

`

4. 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

`

5. Working with videos:

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

`

6. Reading from a webcam:

V2:

`bash

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

`

V3:

`bash

darknet08display_webcam animals

`

7. Save results to a 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

`

8. 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

`

9. Running on a specific GPU:

V2:

`bash

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

`

10. 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

`

11. To check accuracy mAP@IoU=75:

`bash

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

`

12. 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:

`bash

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

`

13. Train a new network:

`bash

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

`

Training

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

How should I setup my files and directories?

Which configuration file should I use?

What command should I use when training my own network?

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:

1. 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/.

2. 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.

3. 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.

4. 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

`

5. 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

`

6. Create a folder where you'll store your images and annotations. For example, this could be ~/nn/animals/dataset. Each image will need a corresponding .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.

7. 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.

8. 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. See the Darknet/YOLO FAQ if 1 doesn't work for you.

Note maxbatches=.... 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 maxbatches=8000.

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

Note width=... and height=.... These are the network dimensions. The Darknet/YOLO 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 (numberofclasses + 5) 3. Meaning for this example, (4 + 5) * 3 = 27. So we'd use filters=27 on the appropriate lines.

9. Start training! Run the following commands:

`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. 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:

`bash

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

`