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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 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 faster and more accurate than other frameworks and YOLO versions.

The 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 when using an NVIDIA RTX 3090 GPU, meaning each video frame can be processed in 1 millisecond or Read, resized and processed by Darknet/YOLO in less time.

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 & colab servers, desktops, laptops and high-end training platforms. The GPU version of Darknet/YOLO requires NVIDIA's CUDA-compatible GPU.

Darknet/YOLO is known to run 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 0.x.

The next popular Darknet repository maintained by Alexey Bochkovskiy from 2017-2021 also does not have a version number. We consider this version 1.x.

The Darknet repository, sponsored by Hank.ai and maintained by Stéphane Charette since 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 break existing functionality as little as possible while getting familiar with the code base.

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

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

Enhanced chart.png during training.

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

The last branch of this 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".

Removed many old and unmaintained commands.

Many performance optimizations, both at training time and at inference time.

The traditional C API has been modified; applications using the original Darknet API require minor modifications: https://darknetcv.ai/api/api.html

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

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

If you need to run one of these commands, you can always check out the previous v2 branch. Please let us know so we can investigate adding back any missing commands.

MSCOCO pre-trained weights

For convenience, several popular versions of YOLO are pre-trained on the MSCOCO dataset. This data set contains 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

The various building methods available in the past (before 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 any more than you need to be a mechanic to drive a car.

Be aware if you are following an older tutorial that has more complex build steps, or if the build steps do not match those in this readme. Starting in August 2023, the new build steps are described below.

Software developers are encouraged to visit https://darknetcv.ai/ for more information on the internals of the Darknet/YOLO object detection framework.

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 new networks.

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 from the Darknet build directory to force CMake to re-find all necessary files.

Remember to rebuild Darknet.

Darknet can 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 at a later time, or if you upgrade to a newer version of NVIDIA software:

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/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, 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 you are using bash as your command shell, you will need to restart your shell at this point. 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:

`cmake

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

`

For distributions like Centos and OpenSUSE, you need to switch these two lines in CM_package.cmake to:

`cmake

SET (CPACK_GENERATOR "DEB")

SET (CPACK_GENERATOR "RPM")

`

To install a package, 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:

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

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

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

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

/opt/darknet/cfg/... is 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! Please 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:

Click on the Windows Start menu and run Visual Studio Installer

Click "Edit"

Select "Desktop development using C++"

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 Visual Studio 2022. Do not use PowerShell for these steps, you will run into problems!

Advanced users:

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

Once you have the developer command prompt running as above (not PowerShell!), run the following command to install Microsoft VCPKG, which will be used to build OpenCV:

`bash

cd c:

mkdir c:srccd 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

`

Please be patient with this last step as it may take a long time to run. It requires a lot of downloading and building.

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 from the Darknet build directory to force CMake to re-find all necessary files.

Remember to rebuild Darknet.

Darknet can 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 at a later time, or if you upgrade to a newer version of NVIDIA software:

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.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

`

If you receive an error about some missing CUDA or cuDNN DLL (for example, 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 which version you are running and run the appropriate command for the version you have installed.)

After copying the files, 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 the 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 ready to 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. 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 named Darknet, for example C:Program FilesDarknet.

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

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

Install the necessary Darknet .dll, .lib, and .h files to use darknet.dll from other applications.

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! Please make sure to complete each panel of the NSIS Installation Wizard 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, also note the DarkHelp project CLI, which provides an alternative CLI for Darknet/YOLO. 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 will need the .weights file and its corresponding .names and .cfg files. You can train your own network (highly recommended!) or download networks that others have trained and made available to the Internet 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)

Runnable commands include:

List some commands and options that may be run:

`bash

darknet help

`

Check version:

`bash

darknet version

`

Use image prediction:

V2: darknet detector test cars.data cars.cfg cars_best.weights image1.jpg

V3: darknet02displayannotatedimages 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 animalsbest.weights -extoutput dog.jpg

V3: darknet01inference_images animals dog.jpg

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

Processing video:

V2: darknet detector demo animals.data animals.cfg animalsbest.weights -extoutput test.mp4

V3: darknet03display_videos animals.cfg test.mp4

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

Reading from webcam:

V2: darknet detector demo animals.data animals.cfg animals_best.weights -c 0

V3: darknet08display_webcam animals

Save results to video:

V2: darknet detector demo animals.data animals.cfg animalsbest.weights test.mp4 -outfilename res.avi

V3: darknet05processvideosmultithreaded 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 animalsbest.weights test50.mp4 -jsonport 8070 -mjpegport 8090 -extoutput

V3: darknet06imagestojson animals image1.jpg

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

Run on a specific GPU:

V2: darknet detector demo animals.data animals.cfg animals_best.weights -i 1 test.mp4

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

`

Check accuracy 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. However, if you want to run an older version in Darknet:

`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

`

(See also training section below)

train

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

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

2. Which profile should I use?

3. Which command should I use when training my 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 wish to manually set up the various files to train a custom network:

1. Create a new folder to store the files. In this example, you will create a neural network that detects animals, so create the following directory: ~/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. In 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. In this example, we now have ~/nn/animals/animals.names.

4. Use a text editor to edit the animals.names file. List the categories you want to use. You need to have exactly 1 entry per line, no blank lines, no comments. In 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. In 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 precise 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, respectively, for validation when calculating mAP%. 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 best value to use is 1, so start with that. If you are unable to use 1, please see the Darknet/YOLO FAQ.

Note that maxbatches=.... When starting out, the optimal value to use is a number of classes of 2000. In 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. In this example, we will use steps=6400,7200 because 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.

* Search for all instances containing the line classes=... and modify them with the number of classes in the .names file. In this example we will use classes=4.

In the [convolutional] section before each [yolo] section, search for all instances containing the line filters=... . The value to use is (number of categories + 5) 3. This means that in this example, (4 + 5) * 3 = 27. Therefore, we will use filters=27 for the appropriate rows.

9. Start training! Run the following command:

`bash

cd ~/nn/animals/

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

`

Please wait. The best weights will be saved as animals_best.weights. You can observe the progress of training 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

`