Picturehouses Download - Picturehouses Source code download

Picturehouses

Other source code

Download

Darknet Object Detection Framework and YOLO

Compiled by Downcodes editor

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"

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.

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 use NVIDIA RTX 3090 GPUs to run LEGO dataset videos at up to 1000 FPS, meaning each video frame is generated by Darknet/ YOLO reads, resizes and processes.

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

Darknet/YOLO is known to run on Linux, Windows and Mac. Please see the build instructions below.

Darknet Version

The original Darknet tools, written by Joseph Redmon in 2013-2017, did not have version numbers. We think this is version 0.x.

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

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

The goal is to get familiar with the code base while breaking as little existing functionality as possible.

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.

Enhance 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 codebase is version 2.1 in the v2 branch.

The next phase of development begins in mid-2024, with release in October 2024. The version command now returns 3.x "JAZZ".

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

Removed many old and unmaintained commands.

Many performance optimizations, both during training and inference.

The old C API has been modified; applications using the original Darknet API will need to make some 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

MSCOCO Pre-trained Weights

For convenience, several popular YOLO versions are pre-trained on the MSCOCO dataset. This dataset 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. See the Darknet/YOLO FAQ for details.

MSCOCO pre-trained weights can be downloaded from a number of different locations and can also be downloaded 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

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 usually used to confirm that everything is OK.

Building

The various build methods available in the past (before 2023) have been merged into a unified solution. Darknet requires C++17 or higher, OpenCV, and uses 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 that show how to perform certain tasks, such as training new networks.

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

Linux CMake Method

Darknet build tutorial on Linux

Optional: If you have a modern NVIDIA GPU, you can install CUDA or CUDA+cuDNN now. 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 upgrade to a newer version of NVIDIA software:

These instructions assume (but are not required!) a system running Ubuntu 22.04. If you are using another distribution, please 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. To upgrade CMake on Ubuntu you can use the following command:

`bash

sudo apt-get purge cmake

sudo snap install cmake --classic

`

If you use bash as your command shell, you may need to restart your shell. 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 such as 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 the package, once it's 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:

/usr/bin/darknet is the usual Darknet executable. Run darknet version from the CLI to confirm it has been 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 C, C++ and Python developers to link against.

/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, that means you didn't install it, you just built it! Make sure you have the .deb or .rpm files installed as mentioned above.

Windows CMake Method

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

Open a regular 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 Installer".

2. Click "Edit".

3. Select "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. Don't use PowerShell for these steps, you'll run into problems!

Advanced users:

In addition to running the developer command prompt, you can also use an ordinary command prompt or ssh to enter the device and manually run "Program FilesMicrosoft Visual Studio2022CommunityCommon7ToolsVsDevCmd.bat".

Once you have a 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/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 during this last step as this 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 now. 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 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.gitcd darknet

mkdir 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 get an error about some missing CUDA or cuDNN DLL (e.g. cublas64_12.dll), then manually copy the CUDA .dll file into the same output directory as Darknet.exe. For example:

`bash

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

`

(Here's an example! Check to make sure which version you're running, and run the appropriate command for what you've 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 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 this 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:

Create a directory called 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 another application.

Install the template .cfg file.

You are done now! Once the installation wizard is complete, Darknet will be installed into 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, that means you didn't install it, you just built it! Make sure you complete each panel of the NSIS Installation Wizard in the previous step.

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 to Darknet/YOLO. DarkHelp CLI also has several advanced features not found in Darknet. You can use Darknet CLI and DarkHelp CLI at the same time, they are not mutually exclusive.

For most of the commands shown below, you need the .weights file and the corresponding .names and .cfg files. You can train your own network (highly recommended!) or download a neural network that others have trained and are freely available on the Internet. Examples of pre-training datasets include:

LEGO Gears (find objects in images)

Rolodex (find text in image)

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 images to make predictions:

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

Use 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 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 runs it 100 times in a row and selects the best anchor point from all calculated anchor points. But if you want to run older versions 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)

Training

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

How do I set up my files and directories?

Which profile should I use?

Which command should you use when training your own network?

Create all necessary Darknet files using DarkMark, 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:

1. Create a new folder to store the files. In 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 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 place 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. There must be only one entry per line, no blank lines and 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 exact coordinates of the annotation. Please refer to 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 (when calculating mAP%) respectively. Exactly one image per row. Paths and filenames can be relative or absolute.

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

9. Make sure batch=64.

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

11. Note maxbatches=…. A good value to use when starting out is the number of categories multiplied by 2000. In this example we have 4 animals, so 4 * 2000 = 8000. This means we will use maxbatches=8000.

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

13. Pay attention to width=... and height=.... These are network dimensions. The Darknet/YOLO FAQ explains how to calculate the optimal size to use.

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

15. Search for instances of all filters=... lines in the [convolutional] section before each [yolo] section. 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 on the appropriate lines.

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. By viewing the chart.png file, you can observe the progress of training. 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

`