Darknet Object Detection Framework and YOLO

Read how Hank.ai is helping the Darknet/YOLO community
Read the Darknet/YOLO FAQ: https://www.ccoderun.ca/programming/darknet_faq/
Join the Darknet/YOLO discord server: https://discord.gg/zSq8rtW

Papers

Paper YOLOv7: https://arxiv.org/abs/2207.02696
Paper Scaled-YOLOv4: https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html
Paper YOLOv4: https://arxiv.org/abs/2004.10934

General Information

YOLOv7 is more accurate and faster than YOLOv5 by 120% FPS, than YOLOX by 180% FPS, than Dual-Swin-T by 1200% FPS, than ConvNext by 550% FPS, than SWIN-L by 500% FPS, and PPYOLOE-X by 150% FPS.

YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100, batch=1.

Building

Not all build solutions make sense for all platforms. Choose the one that works best for you.

Makefile

Typical solution for Linux. This is described here in the FAQ: https://www.ccoderun.ca/programming/darknet_faq/#how_to_build_on_linux

sudo apt-get install build-essential git libopencv-dev
mkdir ~/src
cd ~/src
git clone https://github.com/hank-ai/darknet
cd darknet
# edit Makefile to set LIBSO=1, and possibly other flags
make
sudo cp libdarknet.so /usr/local/lib/
sudo cp include/darknet.h /usr/local/include/
sudo ldconfig

When using this solution, see the flags in the first few lines of Makefile. It is important to set these flags correctly. If you want to use your CUDA-capable GPU, then you must also set the ARCH= flag, and have CUDA and CUDNN correctly installed.

CMake

This solution works for all platforms. You need to have the usual build tools installed, including cmake, git, and both C and C++ compilers.

mkdir ~/src
cd ~/src
git clone https://github.com/hank-ai/darknet
cd darknet
mkdir build_release
cd build_release
cmake ..
cmake --build . --target install --parallel 8

Powershell

Typical solution for Windows. You will need to have the usual build tools installed. This is described here in the FAQ: https://www.ccoderun.ca/programming/darknet_faq/#how_to_build_on_windows

Set-ExecutionPolicy -ExecutionPolicy Unrestricted -Scope Process
md c:\src
cd c:\src
git clone https://github.com/hank-ai/darknet
cd darknet
./build.ps1

There are many options available when running build.ps1. For example:

./build.ps1 -UseVCPKG -EnableOPENCV -EnableCUDA -EnableCUDNN

Vcpkg

This solution can be used from both Linux and Windows, but is much more common on Windows. You can use vcpkg to install old versions of Darknet. The newer versions of Darknet are not available via vcpkg.

Using Darknet

CLI

This is not the full list of all commands supported by Darknet. See the previous readme for additional details and examples.

Run a single image: darknet detector test animals.data animals.cfg animals_best.weights dog.jpg
Output coordinates: darknet detector test animals.data animals.cfg animals_best.weights -ext_output dog.jpg
Working with videos: darknet detector demo animals.data animals.cfg animals_best.weights -ext_output test.mp4
Reading from a webcam: darknet detector demo animals.data animals.cfg animals_best.weights -c 0
Smart webcam: darknet detector demo animals.data animals.cfg animals_best.weights http://192.168.0.80:8080/video?dummy=param.mjpg
Save results to a video: darknet detector demo animals.data animals.cfg animals_best.weights test.mp4 -out_filename res.avi
JSON and MJPEG server: darknet detector demo animals.data animals.cfg animals_best.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output
Running on a specific GPU: darknet detector demo animals.data animals.cfg animals_best.weights -i 1 test.mp4
To check accuracy mAP@IoU=50: darknet detector map animals.data animals.cfg animals_best.weights
To check accuracy mAP@IoU=75: darknet detector map animals.data animals.cfg animals_best.weights -iou_thresh 0.75
To train: darknet detector -map -dont_show train animals.data animals.cfg (also see the training section below

Training

The simplest way to annotate and train is with the use of DarkMark to create all of the necessary files.

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.cfg.
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:

    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.
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.
- 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. For this example, 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.
- Note width=... and height=.... These are the network dimensions. The 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/
    ~/src/darknet/darknet detector -map -dont_show train animals.data animals.cfg

Be patient. The best weights will be stored in animals_best.weights. And the progress of training can be observed by viewing the chart.png file.

Roadmap

Last updated 2023-07-23:

Short-term goals

convert Darknet to use the C++ compiler (g++ on Linux, VisualStudio on Windows)
remove old solutions and Makefile
make OpenCV non-optional
remove STB
re-write CMakeLists.txt to use the new CUDA detection and build out-of-source
have better version number output

Mid-term goals

better use of cv::Mat instead of the custom image structure in C
on-going code cleanup

Long-term goals

fix CUDA/CUDNN issues with all GPUs
look into adding support for non-NVIDIA GPUs

Kevin-Delnoije/darknet