https://github.com/riaz-mahmud/blind-people-smart-aid

Bangladeshi Currency Detect, Object Detect Android App using TensorFlow Lite image classification
https://github.com/riaz-mahmud/blind-people-smart-aid

android bangladeshi-taka-detact btd-detect color-palette color-picker currency-detection object-detection sensor tensorflow tensorflow-lite

Last synced: 3 days ago
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Bangladeshi Currency Detect, Object Detect Android App using TensorFlow Lite image classification

• Host: GitHub
• URL: https://github.com/riaz-mahmud/blind-people-smart-aid
• Owner: Riaz-Mahmud
• Created: 2021-08-11T05:38:13.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2023-12-05T20:37:19.000Z (12 months ago)
• Last Synced: 2023-12-05T21:34:55.327Z (12 months ago)
• Topics: android, bangladeshi-taka-detact, btd-detect, color-palette, color-picker, currency-detection, object-detection, sensor, tensorflow, tensorflow-lite
• Language: Java
• Homepage:
• Size: 33.2 MB
• Stars: 4
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Android Bangladeshi Currency Detect, Object Detect App using TensorFlow Lite image classification

## Overview

This is an example application for [TensorFlow Lite](https://tensorflow.org/lite)

on Android. It uses

[Image classification](https://www.tensorflow.org/lite/models/image_classification/overview)

to continuously classify whatever it sees from the device's back camera.

Inference is performed using the TensorFlow Lite Java API. The demo app

classifies frames in real-time, displaying the top most probable

classifications. It allows the user to choose between a floating point or

[quantized](https://www.tensorflow.org/lite/performance/post_training_quantization)

model, select the thread count, and decide whether to run on CPU, GPU, or via

[NNAPI](https://developer.android.com/ndk/guides/neuralnetworks).

These instructions walk you through building and

running the demo on an Android device. For an explanation of the source, see

[TensorFlow Lite Android image classification example](https://www.tensorflow.org/lite/models/image_classification/android).

### Model

Inside Assests folder zip file is there.

Resnet50 

16 batch size

100 epochs

Teachable ML

## Requirements

*   Android Studio 3.2 (installed on a Linux, Mac or Windows machine)

*   Android device in

    [developer mode](https://developer.android.com/studio/debug/dev-options)

    with USB debugging enabled

*   USB cable (to connect Android device to your computer)

![233090843_626919464944441_2065100748483262971_n](https://user-images.githubusercontent.com/58476836/130997939-d66fad6e-97ea-4d8d-94fa-ecfccc12328e.jpg)

![240407189_600856440900181_597519115783163963_n](https://user-images.githubusercontent.com/58476836/130997953-95632914-92f9-4b82-9477-8167ef755798.jpg)

## Assets folder

_Do not delete the assets folder content_. If you explicitly deleted the

files, choose `Build -> Rebuild` to re-download the deleted model files into the

assets folder.