How to install and compile YOLO v4 with GPU enable settings in Windows 10?

Another post starts with you beautiful people!
Last year I had shared a post about installing and compiling Darknet YOLOv3 in your Windows machine and also how to detect an object using YOLOv3 with Keras. This year on April' 2020 the fourth generation of YOLO has arrived and since then I was curious to use this as soon as possible. Due to my project (built on YOLOv3 :)) work I could not find a chance to check this latest release. Today I got some relief and successfully able to install and compile YOLOv4 in my machine. In this post I am going to share a single shot way to do the same in your Windows 10 machine. If your machine does not have GPU then you can follow my previous post by just replacing YOLOv3 related files with YOLOv4 files. For GPU having Windows machine, follow my steps to avoid any issue while building the Darknet repository.

My machine has following configurations:
Windows 10 64 bit Intel Core i7
16 GB RAM
NVIDIA GeForce GTX 1660 Ti Version 445.87
CUDA 10.1, cuDNN 7.6
Visual Studio 2017

Before proceeding please make sure that you have installed CUDA, cuDNN as per your GPU drivers and Visual Studio 2017 is installed with C++ distributions. An invalid CUDA/cuDNN version will show unnecessary errors while installing. Once you check and assure all setup is up to date; disable your antivirus settings till the end of this process. For building the Darknet code I am here using Vcpkg instead of Darknet repo's build.ps1 file since with this build.ps1 file I was not able to build the code with GPU settings. Let's start the process-

1. Download Vcpkg from this link. It is a C++ library manager and will be used to install and compile the Darknet code.

2. Open Windows PowerShell with admin rights and go to the root directory of the Vcpkg folder you have downloaded from step 1 and run following two commands one by one:

1. .\bootstrap-vcpkg.bat
2. .\vcpkg integrate install

Please note if you are using Windows PowerShell first time, run Set-ExecutionPolicy RemoteSigned and press A when asked and then press Enter.

3. Next, run following command: .\vcpkg install darknet[full]:x64-windows

Above command will download the opencv with cuda, other required libraries and Darknet code. After downloading it will install the all libraries and then it will compile the Darknet code with GPU enable settings automatically. This process will take some time so have patience and wait to complete. Once this process is completed, you will see logs in your PowerShell and some green lines with done status. Make sure all green lines have done status. Once you see done status in all it means Darknet code is successfully compiled with GPU enable settings in your Windows machine. You can check it by going into the darknet directory: <Your_System_dir>\vcpkg-master\installed\x64-windows\tools\darknet

4. Download the yolov4.weights file from this link and put it into the darknet directory
5. Download a sample mp4 video file and put into the same darknet directory
6. In the same PowerShell window go to the path of your darknet directory and run the following command-
.\darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights Car_Racing.mp4 -dont_show -out_filename carRacing_result.mp4

Once you run the above command, in the starting of the running command you will see following like line-

You can see my CUDA, cuDNN and GPU count are showing in the console which confirms that my GPU is used while using the pre-trained YOLOv4 weights on a sample video file. Once this run is completed you can go to your darknet directory and found a output file carRacing_result.mp4 or your sample video output file. Open that file in a media player and you will see bounding boxes around the detected object names like person, car, truck on them. Inspite of fast moving objects in my video file, YOLOv4 is able to detect objects accurate. You can see my output video from this link . In my machine the average FPS with GPU was 6.7 for this video and with CPU it was 0.4. True power of using a real time object detector like YOLO is with GPU only :)

You can also test the detector with an image by running the same command but replacing video file with image file like below-
.\darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights <IMAGE_FiLE> -dont_show -out_filename

Once you open the saved image (predictions.jpg), the image will show the detection result as below-

You can see pre-trained weights of YOLOv4 is able to detect person, cup, spoon, wine glass, fork, bowl, chair, backpack and knife in the above picture.

That's it for today. With this installation we have a real time object detector. Using it's pre-trained weights only we are able to detect many objects. I am using 2 custom YOLOv3 based models in my project and both are successfully running in production. Soon I will replace both models with the 4th generation YOLO. In my next post I will share you how I prepared my custom objects and trained them with YOLOv4. Till then Go chase your dreams, have an awesome day, make every second count and see you later in my next post.