Fruit Rekog
"??? An intelligent application with the power to accurately classify fruits! ?? Utilizing state-of-the-art TensorFlow Lite models for predictions, this advanced system ensures superior accuracy, making fruit identification a seamless and delightful experience.
Fruit Classifier ???
An intelligent application for accurately classifying fruits using TensorFlow Lite models, with the added convenience of offline functionality.
Features:
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Offline Capability ??:
- The application works seamlessly without requiring an internet connection, ensuring reliable performance anytime, anywhere.
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High Accuracy Predictions ??:
- Powered by TensorFlow Lite models, the classifier delivers precise and reliable fruit classifications, enhancing the user experience.
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User-Friendly Interface ?️?:
- The application boasts an intuitive and easy-to-use interface, making fruit identification a simple and enjoyable process.
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Wide Range of Fruits ???:
- Capable of identifying a diverse range of fruits, providing comprehensive support for various fruit types.
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Fast and Efficient ?⚡:
- Swift prediction times ensure a seamless user experience, making the fruit classification process quick and efficient.
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Open Source ?️?:
- The source code is available for exploration and modification, promoting transparency and community collaboration.
Getting Started:
- Fork the Repository:
Fork the repository which will create a copy of this project in your github - Clone the Repository:
git clone https://github.com/user-name/Fruit-Rekog.git
- Open the Fruit-Rekog Folder with Android-Studio:
Go to android studio and open the Fruit-Rekog folder
- Make your own changes:
Brainstorm and make your own changes in the app
Application Demo
Application Screenshot
? Links
Tech Stack
- Kotlin
- XML
- Android Studio
- Tensorflow
- Tensorflow Lite
Authors
Contributing
Contributions are always welcome!
See contributing.md for ways to get started.
Please adhere to this project's code of conduct.
Support
For support, you can buy me a coffee
