1218muskan / ml-image-classification

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Image Classification 💻

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Abstract:
Image classification refers to the task of extracting information classes from a multiband raster image. The resulting raster from image classification can be used to create thematic maps. Image Classification is a fundamental task that attempts to comprehend an entire image as a whole. The goal is to classify the image by assigning it to a specific label. Typically, Image Classification refers to images in which only one object appears and is analyzed. In contrast, object detection involves both classification and localization tasks, and is used to analyze more realistic cases in which multiple objects may exist in an image. Depending on the interaction between the analyst and the computer during classification, there are two types of classification: supervised and unsupervised.

Types of Classification

There are 2 types of classifications-

• Supervised classification Supervised classification uses the spectral signatures obtained from training samples to classify an image. With the assistance of the Image Classification toolbar, you can easily create training samples to represent the classes you want to extract. You can also easily create a signature file from the training samples, which is then used by the multivariate classification tools to classify the image.

• Unsupervised classification Unsupervised classification finds spectral classes (or clusters) in a multiband image without the analyst’s intervention. The Image Classification toolbar aids in unsupervised classification by providing access to the tools to create the clusters, capability to analyze the quality of the clusters, and access to classification tools.

Structure of the Projects 📝

This repository consists of various machine learning projects, and all of the projects must follow a certain template. I wish the contributors will take care of this while contributing in this repository.

Dataset - This folder stores the dataset used in this project. If the Dataset is not being able to uploaded in this folder due to the large size, then put a README.md file inside the Dataset folder and put the link of the collected dataset in it. That'll work!

Images - This folder is used to store the images generated during the data analysis, data visualization, data segmentation of the project.

Model - This folder would have your project file (that is .ipynb file) be it analysis or prediction.

Resources 📝

https://youtube.com/playlist?list=PLZoTAELRMXVPGU70ZGsckrMdr0FteeRUi&utm_source=EKLEiJECCKjOmKnC5IiRIQ https://youtube.com/playlist?list=PLZoTAELRMXVPBTrWtJkn3wWQxZkmTXGwe&utm_source=EKLEiJECCKjOmKnC5IiRIQ

🧮 Workflow

• Fork the repository
• Clone your forked repository using terminal or gitbash.
• Make changes to the cloned repository
• Add, Commit and Push
• Then in Github, in your cloned repository find the option to make a pull request

CONTRIBUTING TO THIS PROJECT

• Take a look at the Existing Issues of your project and find one that interests you or create your own Issues!
• Tag the repository maintainers or issue creators to assign that issue to you.
• Wait for the Issue to be assigned to you after which you can start working on it.
• Fork the Repo and create a Branch for any Issue that you are working upon.
• Create a Pull Request which will be promptly reviewed and suggestions would be added to improve it.
• Once your PR is approved, you changes will be merged into the project.
• Add Screenshots to help us know what this Script is all about.
• Repository specific contribution information is in the respective READMEs of each repo.
• Do not abuse and/or use bad language. Ensure you don't insult anyone. Be respectful and inclusive.
• Please mention your full name on your GitHub handle to be eligible for prizes.

You can take up any of the existing issues or create a new to to contribute any of your own projects!
Contribution period ends: 22 December 2022

How to get started?

You can refer to the following resources on Git and Github to get started and contact our Project Mentors via Discord if you have any doubts.

• Learn how to contribute to GDSC IGDTUW Autumn of Code Projects
• Go through this repository to how to contribute learn step-by-step
• Watch this video to get started
• Forking a Repo
• Cloning a Repo
• How to create a Pull Request
• Getting started with Git and GitHub

Prizes

• Top 3 contributors 🍁
  Special prize | Swag Kits | Shoutouts on Social Media handles | Certificate of appreciation

• Top 5 female contributors 🍁
  Special prize | Swag Kits | Shoutouts on Social Media handles | Certificate of appreciation

• Top 10 contributors 🍁
  Shoutouts on Social Media handles | Swag kits and lots of exciting goodies | Certificate of appreciation

• Top 25 contributors 🍁
  Swag kits and lots of exciting goodies | Certificate of appreciation

• All the contributors will get a certificate of appreciation for their first successful pull request

Join our Discord to stay in touch with project mentors and for any furthur questions.

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Thanks goes to these Wonderful People. Contributions of any kind are welcome!🚀

© 2022 Arushi Garg- GDSC

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