Table of Contents

• Darknet Object Detection Framework and YOLO
• Papers
• General Information
  • Darknet Version
• Pre-trained Weights
  • People-R-People Pre-trained Weights
  • MSCOCO Pre-trained Weights
• Building
  • Google Colab
  • WSL
  • Linux CMake Method
  • Windows CMake Method
  • Docker
• Using Darknet
  • CLI
  • Training
• Other Tools and Links
• Roadmap
  • Short-term goals
  • Mid-term goals
  • Long-term goals

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.

• 2025-08: Darknet/YOLO repo has moved to Codeberg.org/CCodeRun/darknet/
  • All commits are automatically mirrored from Codeberg to the older Hank.ai GitHub repo.
• See the Darknet/YOLO web site
• Please read through the Darknet/YOLO FAQ
• Join the Darknet/YOLO discord server

Papers

• Paper YOLOv7
• Paper Scaled-YOLOv4
• Paper YOLOv4
• 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 both 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 a NVIDIA RTX 3090 GPU, meaning each video frame is read, resized, and processed by Darknet/YOLO in 1 millisecond or less. Additional performance improvements have been implemented in Darknet V4 and the upcoming Darknet V5, without compromising the quality of detections.

Please join the Darknet/YOLO Discord server if you need help or you want to discuss Darknet/YOLO and related tools: https://discord.gg/MQw32W9Cqr

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

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.
  • Re-wrote the build steps so we have 1 unified way to build using CMake on Windows, Linux, and Mac.
  • Converted the codebase to use the C++ compiler.
  • Enhanced chart.png while training.
  • 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".
  • Removed many old and unmaintained commands.
    • 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.
  • Many performance optimizations, both when training and during inference.
  • Legacy C API was modified; applications that use the original Darknet API will need minor modifications: https://www.ccoderun.ca/darknet/api.html
  • New Darknet V3 C and C++ API to make it easier for developers to write applications that use Darknet/YOLO: https://www.ccoderun.ca/darknet/api.html
  • New apps and sample code in src-examples: https://www.ccoderun.ca/darknet/files.html
• The next release was in early 2025. The version command now returns 4.x "SLATE".
  • Added support for AMD GPUs using ROCm.
    • Still need to add support for MIOpen.
  • All printf() and std::cout calls have all been replaced so Darknet/YOLO logging messages can easily be redirected.
• The next release was in August 2025. The version command now returns 5.x "Moonlit".
  • Move source code repo from github to codeberg.
  • More performance optimizations.
  • Use of OpenBLAS when building CPU-only versions of Darknet.
  • Support for Profile-Guided Optimization.
  • ONNX export functionality. [experimental]
  • JAVA language bindings. [incomplete, in-progress]
• The most recent release is v5.1, which continues the "Moonlit" name introduced in v5.0.
  • Released in December 2025.
  • The ONNX export tool now includes the necessary nodes to export both "confs" and "boxes".
  • The mAP function was re-written.
  • Small number of new performance optimizations.

Pre-trained Weights

People are generally expected to train their own network. But pre-trained weights are also popular. These are weights which someone else has trained and made available for free on the internet. This is sometimes convenient when first installing Darknet/YOLO, since the software can be quickly tested without having to train a new neural network.

• People-R-People Pre-trained Weights (2 classes, person and head)
• MSCOCO Pre-trained Weights (80 classes covering a large range of objects, such as person, backpack, chair, clock, ...)
• There are several other simple datasets and pre-trained weights available for testing Darknet/YOLO, such as LEGO Gears and Rolodex. See the Darknet/YOLO FAQ for details.

People-R-People Pre-trained Weights

The People-R-People pre-trained weights are used to find people. This dataset only has 2 classes: person and head.

You can download these pre-trained weights directly from C Code Run's web site.

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.

The MSCOCO pre-trained weights can be downloaded from several different locations, and are also available for download from this repo:

• YOLOv2, November 2016
  • YOLOv2-tiny
  • YOLOv2-full
• YOLOv3, May 2018
  • YOLOv3-tiny
  • YOLOv3-full
• YOLOv4, May 2020
  • YOLOv4-tiny
  • YOLOv4-full
• 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:

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

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.

[!NOTE] You do not need to know CMake, C++, C, Python, or any other programmaing language to build, install, nor run Darknet/YOLO. Some advanced topics -- such as using the Darknet/YOLO API -- does require programming experience.

Select one of the following build types:

• Google Colab
• WSL
• Linux
• Windows
• Docker

[!CAUTION] Beware if you are following old tutorials with more complicated build steps, or build steps that don't seem to match what is in this readme. The new build steps started in August 2023.

Software developers are encouraged to visit https://www.ccoderun.ca/darknet/ to get information on the internals of the Darknet/YOLO object detection framework.

[!IMPORTANT] An important change was made in Darknet v5.1 to one of the prediction structures. If you have software that includes darknet.h or darknet.hpp you'll want to recompile your application to prevent segfaults.

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 for details, and/or follow the Linux instructions below.

WSL

If you have a modern version of Windows and a decent computer, then the use of WSL (Windows Subsystem for Linux) and Ubuntu 24.04 LTS is highly recommended.

WSL is a feature in Windows which allows people to run Linux-based applications from within their Windows desktop. This is similar to a virtual machine with host/guest extensions. Linux apps running in WSL have access to the GPU if you install the Linux NVIDIA driver for WSL, and you can train a new network with Darknet/YOLO running within WSL.

[!TIP] Once WSL is installed, please remember to run sudo apt-get update at least once from your Ubuntu command prompt to get the updated list of packages, and then follow the usual Linux instructions.

If you don't want to use Darknet/YOLO from within WSL, then skip ahead to the Windows instructions.

Linux CMake Method

[!IMPORTANT] Darknet can run without access to a GPU, but if you want to train a custom network or you need higher performance, then a modern NVIDIA GPU or AMD GPU is strongly recommended.

• Depending on which GPU you have, please read the NVIDIA GPU Readme or the AMD GPU Readme.

[!TIP] If you are building a CPU-only version of Darknet, you may want to install OpenBLAS to increase performance. These libraries are only used on CPU-only builds:

sudo apt-get install libopenblas64-0 libopenblas64-0-openmp libopenblas64-openmp-dev

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

sudo apt-get install build-essential git libopencv-dev cmake libprotobuf-dev protobuf-compiler
mkdir ~/src
cd ~/src
git clone https://codeberg.org/CCodeRun/darknet.git
cd darknet
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j4 package
sudo dpkg -i darknet-<INSERT-VERSION-YOU-BUILT-HERE>.deb

[!WARNING] 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

[!WARNING] 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.

[!TIP] Advanced users:
See the readme for PGO (Profile-Guided Optimization) if you want to run an optimized Darknet/YOLO.

[!TIP] 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 once it has finished building, 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 .deb (or .rpm) package will install the following files:

• /usr/bin/darknet*... are the usual Darknet executables. Run darknet version from the CLI to confirm it is installed correctly.
• /usr/include/darknet*... are the Darknet API header files for C, C++, and Python 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.

[!CAUTION] 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

[!IMPORTANT] Before building Darknet/YOLO for Windows please see the note about using WSL. (Spoiler ... Darknet/YOLO works great in WSL!)

• These instructions assume (but does not require) a brand new installation of Windows 11 22H2.
• These instructions are for a native Windows installation, not using WSL. If you'd rather use WSL, see above.

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

[!IMPORTANT] 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!

[!TIP] 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".

[!WARNING] Please don't skip this step! Re-read the "important" description above to see what kind of window you must be using. Anytime you want to use Visual Studio from the command prompt to compile C++ code you must use the Visual Studio developer command prompt as described above.

Once you have the Developer Command Prompt, run the following commands to install Microsoft VCPKG, which will then be used to build OpenBlas and 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 protobuf: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.

[!TIP] 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.

[!IMPORTANT] Darknet can run without access to a GPU, but if you want to train a custom network or you need higher performance, then a modern NVIDIA GPU or AMD GPU is strongly recommended.

• Depending on which GPU you have, please read the NVIDIA GPU Readme or the AMD GPU Readme.

[!TIP] Adding the vcpkg bin directory to the environment variable PATH will prevent later problems. The directory you'll want to add is C:/src/vcpkg/installed/x64-windows/bin/.

[!TIP] If you are building a CPU-only version of Darknet, you may want to install OpenBLAS to increase performance. These libraries are only used on CPU-only builds:

.\vcpkg.exe install openblas[core,threads]:x64-windows

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. Make sure you continue to use the Developer Command Prompt as described above when you run these commands:

cd c:\src
git clone https://codeberg.org/CCodeRun/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

[!TIP] 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:\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-<INSERT-VERSION-YOU-BUILT-HERE>-win64.exe in the build directory. For example:

darknet-<INSERT-VERSION-YOU-BUILT-HERE>-win64.exe

Installing the NSIS installation package will:

• Create a directory called Darknet, such as C:\Program Files\Darknet\.
• Install the CLI applications, darknet.exe and other sample apps.
• Install the required 3rd-party .dll files, such as those from OpenCV and Darknet.
• Install the neccesary Darknet .dll, .lib and header files to use darknet.dll from another application.
• Install the template .cfg files.

[!IMPORTANT] Make sure to update your PATH to include C:/Program Files/Darknet/bin (or wherever you chose to install it). Once you update PATH you'll need to restart your command prompt.

[!TIP] If you encounter an error about some missing Protocol Buffer DLL files such as libprotobuf.dll and abseil_dll.dll when running the ONNX export tool, it probably means you forgot to update your PATH or restart the command prompt. The missing DLLs can be found in C:/src/vcpkg/installed/x64-windows/bin/.

You are now done! Once the installation wizard has finished and PATH has been updated, run this command to test: darknet.exe version.

[!CAUTION] 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.

Docker

To build Darknet via Docker with GPU support, nvidia-container-toolkit must be installed on your system, and the build process must be executed in docker run.

Below is an example Dockerfile built with the following command:

docker build -t darknet-hankai . && docker run -it --gpus all darknet-hankai

FROM nvidia/cuda:12.8.0-cudnn-devel-ubuntu24.04

# Set environment variables
ENV DEBIAN_FRONTEND=noninteractive
ENV PATH="/usr/local/cuda/bin:${PATH}"
ENV LD_LIBRARY_PATH="/usr/local/cuda/lib64:${LD_LIBRARY_PATH}"

# Install dependencies
RUN apt-get update
RUN apt-get install -y build-essential git libopenblas64-openmp-dev libopencv-dev wget file cmake

# Set working directory for Darknet
WORKDIR /workspace

# Clone Darknet
RUN git clone https://codeberg.org/CCodeRun/darknet.git

# Defer building the Darknet package to runtime (GPU visible)
CMD ["/bin/bash", "-c", "\
    cd /workspace/darknet && \
    # initialize cmake
    mkdir build && cd build && cmake ..; \
    # build darknet
    make -j$(nproc) package && \
    # package darknet
    dpkg -i /workspace/darknet/build/darknet-*.deb && \
    # run darknet version to verify build & enter terminal
    darknet version && \
    exec /bin/bash"]

Using Darknet

CLI

The following is not the full list of all commands supported by Darknet.

[!IMPORTANT] In addition to the Darknet CLI, also note the DarkHelp project CLI which provides an additional 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.

[!TIP] 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:

• LEGO Gears (finding ojects in an image)
• Rolodex (finding text in an image)
• People-R-People (finding people in an image)
• MSCOCO (standard 80-class object detection)

Commands to run include:

• List some possible commands and options to run:

  • darknet help

• Check the version:

  • darknet version

• Predict using an image:

  • V2: darknet detector test cars.data cars.cfg cars_best.weights image1.jpg
  • V3+: darknet_02_display_annotated_images cars.cfg image1.jpg
  • DarkHelp: DarkHelp cars.cfg cars.cfg cars_best.weights image1.jpg

• Output coordinates:

  • V2: darknet detector test animals.data animals.cfg animals_best.weights -ext_output dog.jpg
  • V3+: darknet_01_inference_images animals dog.jpg
  • DarkHelp: DarkHelp --json animals.cfg animals.names animals_best.weights dog.jpg

• Working with videos:

  • V2: darknet detector demo animals.data animals.cfg animals_best.weights -ext_output test.mp4
  • V3+: darknet_03_display_videos animals.cfg test.mp4
  • DarkHelp: DarkHelp animals.cfg animals.names animals_best.weights test.mp4

• Reading from a webcam:

  • V2: darknet detector demo animals.data animals.cfg animals_best.weights -c 0
  • V3+: darknet_08_display_webcam animals

• Save results to a video:

  • V2: darknet detector demo animals.data animals.cfg animals_best.weights test.mp4 -out_filename res.avi
  • V3+: darknet_05_process_videos_multithreaded animals.cfg animals.names animals_best.weights test.mp4
  • DarkHelp: DarkHelp animals.cfg animals.names animals_best.weights test.mp4

• JSON:

  • V2: darknet detector demo animals.data animals.cfg animals_best.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output
  • V3+: darknet_06_images_to_json animals image1.jpg
  • DarkHelp: DarkHelp --json animals.names animals.cfg animals_best.weights image1.jpg

• Running on a specific GPU:

  • V2: 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)

• Display YOLO heatmaps:

  • V3+: darknet_02_display_annotated_images --heatmaps cars images/*.jpg
  • V3+: darknet_03_display_videos --heatmaps cars videos/*.m4v

• ONNX export:

  • darknet_onnx_export cars.cfg

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?

[!TIP] 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.
  • 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 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.
  • Note 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.
  • 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 (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

• If you have multiple GPUs, then use the -gpus ... flag like this example with 4 GPUs:

cd ~/nn/animals/
darknet detector -gpus 0,1,2,3 -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:

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

The -log ... flag can be used to send all of the console output to a file. For example:

cd ~/nn/animals/
darknet detector -gpus 0 -verbose -log output.log -map -dont_show train animals.data animals.cfg

Other Tools and Links

• To manage your Darknet/YOLO projects, annotate images, verify your annotations, and generate the necessary files to train with Darknet, see DarkMark.
• For a robust alternative CLI to Darknet, to use image tiling, for object tracking in your videos, or for a robust C++ API that can easily be used in commercial applications, see DarkHelp.
• See if the Darknet/YOLO FAQ can help answer your questions.
• See the many tutorial and example videos on Stéphane's YouTube channel
• C++ bindings for Darknet/YOLO (in the Darknet/YOLO repo)
• C bindings for Darknet/YOLO (in the Darknet/YOLO repo)
• Python bindings for Darknet/YOLO (in the Darknet/YOLO repo)
• Java bindings for Darknet/YOLO [incomplete, in-progress]
• Delphi bindings for Darknet/YOLO
• C# bindings for Darknet/YOLO
• Darknet2Any
• If you have a support question or want to chat with other Darknet/YOLO users, join the Darknet/YOLO discord server.

Sponsors

Thank you to both individual and corporate sponsors. A special thanks to past corporate sponsors who helped fund the continued development of Darknet/YOLO.

In alphabetical order, this includes:

• Detect-It
• Hank AI
• Koshee
• Medius
• Upplevelseinstitutet

Roadmap

Last updated 2026-06-27:

Completed

• swap out qsort() for std::sort() where used during training (some other obscure ones remain)
• get rid of check_mistakes, getchar(), and system()
• convert Darknet to use the C++ compiler (g++ on Linux, VisualStudio on Windows)
• fix Windows build
• fix Python support
• build darknet library
• re-enable labels on predictions ("alphabet" code)
• re-enable CUDA/GPU code
• re-enable CUDNN
• re-enable CUDNN half
• do not hard-code the CUDA architecture
• better CUDA version information
• re-enable AVX
• remove old solutions and Makefile
• make OpenCV non-optional
• remove dependency on the old pthread library
• remove STB
• re-write CMakeLists.txt to use the new CUDA detection
• remove old "alphabet" code, and delete the 700+ images in data/labels
• build out-of-source
• have better version number output
• performance optimizations related to training (on-going task)
• performance optimizations related to inference (on-going task)
• pass-by-reference where possible
• clean up .hpp files
• re-write darknet.h
• do not cast cv::Mat to void* but use it as a proper C++ object
• fix or be consistent in how internal image structure gets used
• fix build for ARM-based Jetson devices
  • original Jetson devices (unlikely to fix since they are no longer supported by NVIDIA and do not have a C++17 compiler)
  • new Jetson Orin devices are working
• fix Python API in V3
  • better support for Python is needed (any Python developers want to help with this?)
• heatmaps
• remove all calls to printf() and std::cout and replace with std::ofstream to log to a file
• add support for AMD GPUs
• add support for OpenBLAS (CPU builds only)
• add support for Profile Guided Optimization
• move from github to codeberg
• ONNX export tool
• re-write function that calculates mAP (mean average precision)
• support both FP32 and FP16 with the ONNX export tool

Short-term goals

• Java bindings (incomplete)
• look into old zed camera support
• better and more consistent command line parsing (in progress)
• add support for MIOpen

Mid-term goals

• remove all char* code and replace with std::string
• don't hide warnings and clean up compiler warnings (in progress)
• better use of cv::Mat instead of the custom image structure in C (in progress)
• replace old list functionality with std::vector or std::list (in progress)
• fix support for 1-channel greyscale images
• add support for N-channel images where N > 3 (e.g., images with an additional depth or thermal channel)
• on-going code cleanup (in progress)
• add support for 8-bit quantization in the ONNX export tool

Long-term goals

• fix CUDA/CUDNN issues with all GPUs
• re-write CUDA+cuDNN code
• look into adding support for additional hardware
• rotated bounding boxes, or some sort of "angle" support
• keypoints/skeletons
• segmentation