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Beginner-friendly image classifier built with PyTorch and CIFAR-10.
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Table of Contents
An image classifier is an ML model that recognizes objects in images. We can build image classifiers by feeding tens of thousands of labelled images to a neural network. Tools like PyTorch train these networks by evaluating their performance against the dataset.
You can either run the project locally on your device or in the cloud using Google colab follow the steps below for more details.
This is an example of how to list things you need to use the software and how to install them.
- npm
npm install npm@latest -g
- Get a free API Key at https://example.com
- Clone the repo
git clone https://github.com/Abood-devo/simple-image-classifier.git
- Install NPM packages
npm install
- Enter your API in
config.js
const API_KEY = 'ENTER YOUR API';
Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.
For more examples, please refer to the Documentation
- Feature 1
- Feature 2
- Feature 3
- Nested Feature
See the open issues for a full list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature
) - Commit your Changes (
git commit -m 'Add some AmazingFeature'
) - Push to the Branch (
git push origin feature/AmazingFeature
) - Open a Pull Request
Distributed under the MIT License. See LICENSE.txt
for more information.
Abdulqader Dada - @adfd_2 - [email protected]
Project Link: https://github.com/Abood-devo/simple-image-classifier