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https://github.com/kanugurajesh/image-classification

Mobile application to identity objects even in offline mode
https://github.com/kanugurajesh/image-classification

kotlin machine-learning tensorflow tflite xml

Last synced: 3 months ago
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Mobile application to identity objects even in offline mode

Host: GitHub
URL: https://github.com/kanugurajesh/image-classification
Owner: kanugurajesh
License: mit
Created: 2023-10-23T17:22:20.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2023-12-03T06:10:02.000Z (about 1 year ago)
Last Synced: 2024-01-12T04:54:48.662Z (about 1 year ago)
Topics: kotlin, machine-learning, tensorflow, tflite, xml
Language: Kotlin
Homepage:
Size: 3.17 MB
Stars: 8
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- Contributing: contributing.md
- License: LICENSE.txt
- Code of conduct: code_of_conduct.md

Awesome Lists containing this project

README

#### ✨ Star my repo if you like it

# Smart Image Classifier: An Offline Mobile Solution

## Problem Statement 🧐

In a world awash with images, the ability to recognize and understand the content within them is a challenge for many. While online solutions like Google Lens exist, they are often limited by the requirement for an internet connection 🌐. This leaves a gap for an intelligent offline image classification tool.

## Solution 💡

Introducing the Smart Image Classifier 📱, a mobile application designed to empower users with the ability to effortlessly classify images without the need for an internet connection. This innovative solution harnesses the combined strength of TensorFlow Lite 🧠 and Android (Kotlin) 🤖, providing a user-friendly interface for image classification.

With the Smart Image Classifier, you can identify and categorize objects, scenes, and much more 🌟. Whether you're a curious explorer 🔍, a student conducting research 📚, or simply someone looking to understand their surroundings better, this tool allows you to do so with ease, anytime and anywhere 🌍.

Say goodbye to the limitations of online-only solutions and embrace the power of offline image classification at your fingertips 📲. The Smart Image Classifier - your window to a smarter, more connected world 🌠.

## ✨ Features

- Offline Image Classification 📴: No internet connection required, ensuring reliability even in remote areas.

- Object Recognition 🕵️‍♂️: Identifies and classifies various objects within images, aiding in tasks like inventory management 📦.

- Scene Analysis 🌆: Recognizes and categorizes different scenes, from landscapes 🏞️ to bustling city streets 🏙️.

- User-Friendly Interface 📲: Intuitive and user-friendly, accessible to a wide range of users.

- Cross-Platform Compatibility 📱: Designed for Android (Kotlin) for use on a variety of Android devices.

- TensorFlow Lite Integration 🧠: Efficient image classification with TensorFlow Lite.

- Real-time Classification 📷: Instant results by pointing the camera at an object or scene.

- Customizable Categories 🎨: Define and customize categories for classification.

- Educational Tool 📚: Valuable for students to learn about image recognition and AI.

- No Subscription Required 💰: One-time purchase or free to use, no recurring fees.

- Privacy-Focused 🔒: User data is private, and no online connectivity is required.

- Continuous Updates 🔄: Regular updates to improve image recognition capabilities.

- Offline Database 📂: Extensive offline image database for swift classification.

- Share and Save Results 💾: Easily save and share classification results with others.

- Accessible Anytime, Anywhere 🌍: Classify images in any location or situation.

## Prerequisites

```bash
1. Android Studio
```

## Installation

```bash
git clone [email protected]:kanugurajesh/Image-Classification.git
cd Image-Classification
Open the project in android studio
Build the project
Run the app
```

## Screenshots

### App Icon

### App Inside

## Demo

![Image-classifier](https://github.com/kanugurajesh/Image-Classification/assets/77529419/f1d9e201-9401-4321-9285-f88dad413ae8)

## Tech Stack

- Kotlin
- XML
- Android Studio
- Tensorflow
- Tensorflow-lite

## 🔗 Links
[![portfolio](https://img.shields.io/badge/my_portfolio-000?style=for-the-badge&logo=ko-fi&logoColor=white)](https://rajeshportfolio.me/)
[![linkedin](https://img.shields.io/badge/linkedin-0A66C2?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/rajesh-kanugu-aba8a3254/)
[![twitter](https://img.shields.io/badge/twitter-1DA1F2?style=for-the-badge&logo=twitter&logoColor=white)](https://twitter.com/exploringengin1)

## Contributing

I want to add more features and Contributions are always welcome!

See [contributing.md](https://github.com/kanugurajesh/Image-Classification/blob/main/contributing.md) for ways to get started.

Please adhere to this project's [code of conduct](https://github.com/kanugurajesh/Image-Classification/blob/main/code_of_conduct.md).

## Authors

- [@kanugurajesh](https://github.com/kanugurajesh)

## Support

For support, you can buy me a coffee

[![MIT License](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/kanugurajesh/Image-Classification/blob/main/LICENSE.txt)