Skip to main content

How can I install and use Darknet framework in Windows?


Another post starts with you beautiful people! I hope you have enjoyed my last post about using real time object detection system- Yolo with keras api. In that post I mentioned that Yolo is built on Darknet framework and this framework is written on C and cuda. That's why we used Python wrapper of Darknet  framework instead of installing original framework. Many readers asked me about how can we install and use the original framework in our window machine. In this post I will try to show you the steps about this installation. Before following the steps I strongly recommend you to activate virtual env and install all libraries I have mentioned in my last post.

 For this setup I have followed this original github repository- AlexeyAB/darknet . this repo is as same as original Darknet repo with additional Windows support. So don't forget to give a star to this repo as a token of our respect to the author. If you are reading my blog first time, then I recommend to read the Requirements section mentioned in this repo first.

Once your machine is ready with all requirements, next step is to install a compilation software- vcpkg which you can download from this link. Once you download the zip file, extract it, open command prompt with admin rights, navigate to the extracted vcpkg-master folder and run the following command-

Once the above command finishes, run the below command in same prompt-

Next, we need to add vcpkg root path in our environment variable. So open the system variable and add the location of the vcpkg-master folder under the name of VCPKG_ROOT like below-

Along with this new variable, add another variable VCPKG_DEFAULT_TRIPLET with following value-


Now we are ready for installing Darknet with this compilation software. Open Anaconda Poweshell Prompt with admin rights, navigate to the vcpkg-master folder by running following command:
cd $env:VCPKG_ROOT
After this run the following command:
.\vcpkg install pthreads opencv[ffmpeg]
Once you run above two commands, screen will look like below-

It will take some time to complete. Once this process is completed next step is to download the code from AlexeyAB/darknet. Once you downlaod the zip file from this repo, extract it and in the Anaconda Powershell Prompt navigate to the location of the darknet folder and run .\build.ps1 like below-

This command will install darknet repository once finishes. After this run following command:
Set-ExecutionPolicy -ExecutionPolicy Restricted and then when asked press A to exclude any restriction. Once this is done, you can navigate to the installed repository in your respective folder and see all installed files there. Now you need to copy the pretrained Yolo weights file in this location. You can download the weights from this link. After this we are ready to test the original YOLO system built on Darknet framework on our image.

To test any image you need to run following command in same Anaconda Powershell Prompt-
.\darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25

Once this command finishes , it will ask you to enter the path of your test image. enter the full path and press enter-

Once you enter the image path and press enter, your input image will be opened in default photo viewer having bounding boxes and label. Also in server console accuracy of the prediction will be shown with each detected object-

That's amazing right. In this post we have successfully installed Darknet framework in our Windows machine and in my last post we have successfully setup the Python wrapper of Darknet. Now you are familiar with both ways. If you are working with a client who has Windows infrastructure like mine then this post will help you to deliver state of the art object detection model built on Yolo system. I recommend you to give some time to learn this framework and get your hands dirty by practicing it on different image dataset many times. In my next post I will share another my learnings. Till then Go chase your dreams, have an awesome day, make every second count and see you later in my next post.
Labels:

Comments

Post a Comment

Popular posts from this blog

Generative AI with LangChain: Basics

  Wishing everyone a Happy New Year '24😇 I trust that you've found valuable insights in my previous blog posts. Embarking on a new learning adventure with this latest post, we'll delve into the realm of Generative AI applications using LangChain💪. This article will initially cover the basics of Language Models and LangChain. Subsequent posts will guide you through hands-on experiences with various Generative AI use cases using LangChain. Let's kick off by exploring the essential fundamentals💁 What is a Large Language Model (LLM)? A large language model denotes a category of artificial intelligence (AI) models that undergo extensive training with extensive textual data to comprehend and produce language resembling human expression🙇. Such a large language model constitutes a scaled-up transformer model, often too extensive for execution on a single computer. Consequently, it is commonly deployed as a service accessible through an API or web interface. These models are...
6 comments
Read more

How can I become a TPU expert?

Another post starts with you beautiful people! I have two good news for all of you! First good news is that Tensorflow has released it's new version (TF 2.1) which is focused on TPUs and the most interesting thing about this release is that it now also supports Keras high level API. And second wonderful news is to help us get started Kaggle has launched a TPU Playground Challenge . This means there is no any way to stop you learning & using TPUs. In this post I am going to share you how to configure and use TPUs while solving a image classification problem. What are TPUs? You must have heard about TPU while using  Google Colab . Now Kaggle also supports this hardware accelerator. TPUs or Tensor Processing Units are hardware accelerators specialized in deep learning tasks. They were created by Google and have been behind many cutting edge results in machine learning research. Kaggle Notebooks are configured with TPU v3-8s, which is a specialized hardware with...
4 comments
Read more

How to convert your YOLOv4 weights to TensorFlow 2.2.0?

Another post starts with you beautiful people! Thank you all for your overwhelming response in my last two posts about the YOLOv4. It is quite clear that my beloved aspiring data scientists are very much curious to learn state of the art computer vision technique but they were not able to achieve that due to the lack of proper guidance. Now they have learnt exact steps to use a state of the art object detection and recognition technique from my last two posts. If you are new to my blog and want to use YOLOv4 in your project then please follow below two links- How to install and compile Darknet code with GPU? How to train your custom data with YOLOv4? In my  last post we have trained our custom dataset to identify eight types of Indian classical dance forms. After the model training we have got the YOLOv4 specific weights file as 'yolo-obj_final.weights'. This YOLOv4 specific weight file cannot be used directly to either with OpenCV or with TensorFlow currently becau...
88 comments
Read more