The focus of this article is to teach you how to create the required environment in Anaconda for your labelImg — an Image Annotation tool. You will also be introduced to Image annotation and labelImg as image annotation tool and the need for an environment just in case you are not familiar with them. Whereas you will get resources that show you how to annotate, this Article will not be dwelling on that. The knowledge from this short article will help you in setting up a conda environment, building your custom Image dataset from scratch, most importantly launching labelImg software on your device.
Image Annotation in Machine Learning or Computer Vision is the process of labelling image data with predetermined labels in order to provide the Computer vision or Machine learning model the information on objects represented in an image.
The algorithm would then use the annotated data to learn and recognize similar patterns when presented with new data. Image annotation is a critical part of computer vision.
The computer vision industry advances almost every minute, thus, the demand for quality and reliable data for training or improving their models kept rising. It might as well interest you to know that, many companies across the World that value Quality, Speed, Scale, Security, and the need to prevent internal biases are already outsourcing their Data Annotation tasks.
“Properly annotated data is very important for the development of autonomous vehicles, computer vision for aerial drones, and many other AI and robotics applications.” — SAS
More also, quality data will continue to be in demand as Computer Vision and Machine Learning keeps evolving. Therefore, the quality of data and the need to minimize the bias in current and future data cannot be underemphasized.
I will basically describe LabelImg as a graphical image annotation tool. It is written in Python programming language and uses Qt for its graphical interface. Annotations are saved as XML files in PASCAL VOC format, the format used by ImageNet. Besides, it also supports YOLO format. Shall we dive into the practical?
Virtual environments are useful in Python because they help in creating an isolated space for you to experiment with new versions of third-party modules, new directions with code you’re writing yourself, different versions of Python itself, etc., all without polluting the system’s preinstalled Python version, or whatever you call the “normal” version of Python.
I often prefer to ensure that all my packages are up-to-date by updating them monthly. It is more like a routine check for me, lol. I will explain below the process for creating a python environment in Anaconda Prompt.
#object-detection #deep-learning #computer-vision #image-annotation #machine-learning #deep learning
Kubectl is a command-line tool for Kubernetes. It allows us to execute Kubernetes operations via the API. We can use Kubectl to deploy apps, check logs as well as manage all the other resources of the cluster.
Kubernetes uses an HTTP-based REST API which is the actual Kubernetes user interface employed to manage it. This means that every Kubernetes operation is represented as an API endpoint and can be carried out based on an HTTP-request sent to the endpoint.
In this article, we will review Kubectl, and outline its installation, configuration, and use.
The name Kubernetes has its origins from the original Greek term for helmsman or pilot. Kubernetes, or ‘k8s’ (pronounced “Kate’s”) is an open-source software tool that was created by Google and is used for scaling, deploying and coordinating containerized applications into easy to manage groups. It supports multiple containerization technologies as well as orchestrates hardware virtualization.
To manage a Kubernetes server cluster effectively, we utilize kubectl as the command-line tool of choice. Basically, kubectl communicates with the master Kubernetes node(s) which in turn submits commands to the worker nodes to manage the cluster. A Kubernetes cluster basically consists of two types of resources.
Each node contains a Kubelet, which is the agent for managing the node and communicating with the master. We can use kubectl to deploy, explore, review and remove Kubernetes objects (like nodes, images or containers).
Initially, Kubernetes was designed and developed by Google engineers to employ and utilize containers for its workload management. Google generates more than 2 billion containers deployments a week which was provided for by their internal platform code-named Borg (the predecessor to Kubernetes). During Borg’s development, the experience gained was one of the main factors that influenced a significant part of Kubernetes current technologies. Currently, Kubernetes is maintained by the Cloud Native Computing Foundation.
The easiest way to install kubectl is to use one of the default package managers for a Linux OS.
sudo apt-get update && sudo apt-get install -y apt-transport-https gnupg2 curl -s https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add - echo "deb https://apt.kubernetes.io/ kubernetes-xenial main" | sudo tee -a /etc/apt/sources.list.d/kubernetes.list sudo apt-get update sudo apt-get install -y kubectl
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For months now I have been working with data out of Jupyter notebooks in an Anaconda (conda) virtual environment pip installing Python package after Python package, naively unaware of how anything could possibly go wrong with my computer’s ever growing library of PyPI — all until a few conflicts arose and the code I was working with indicated it was dependent on an earlier versions of a packages I had already installed.
If you are beginning to work with data in Python and furiously learning the endlessly growing library of packages that come along with knowledge of Python, here is my word of advice to you: you will save yourself hours of bug fixing and backtracking by knowing how to work with Python virtual environments from the start. If you haven’t already, start by downloading Anaconda. This can take a little while, so go brew yourself a little coffee (or tea!) while it downloads and then you can finish the rest of this blog in about 5 minutes.
#anaconda #virtual-environment #data-science #python-programming #python
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One of the tasks that could be confusing for a beginner starting out with Python on the Windows Operating System (OS) is creating a virtual environment for projects.
In this article, we will go through how to create a virtual environment for a Django project in Windows 10
virtualenv : is a Python tool used for creating isolated environments. This separates each project from another so that the tools and actions active in one project environment do not have an effect in another environment except they are installed or activated in the other environment.
#beginners #django #virtual environment