https://github.com/redwansharafatkabir/seeinme

Product Detecting Machine Learning App with TensorflowLite
https://github.com/redwansharafatkabir/seeinme

android image-classification image-processing java machine-learning tensorflow-lite

Last synced: about 1 month ago
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Product Detecting Machine Learning App with TensorflowLite

• Host: GitHub
• URL: https://github.com/redwansharafatkabir/seeinme
• Owner: RedwanSharafatKabir
• Created: 2021-09-05T13:53:29.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-03-29T18:49:57.000Z (almost 2 years ago)
• Last Synced: 2024-11-08T11:21:08.107Z (3 months ago)
• Topics: android, image-classification, image-processing, java, machine-learning, tensorflow-lite
• Language: Java
• Homepage:
• Size: 26 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
## Android Product Detector 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





### Screen Shots

#### Detect Product -

            ![product1](https://user-images.githubusercontent.com/37416018/142223670-d47a6c5c-c51b-4727-873a-f138e14ac2b3.jpg)

            ![product2](https://user-images.githubusercontent.com/37416018/142223680-5f978be8-78e4-4bc5-8bf1-edd19574f8c9.jpg)

- - - -

#### Voice Command -

                 ![voice command](https://user-images.githubusercontent.com/37416018/142223655-71774b00-1cf2-48ea-96a9-99247e14e9e6.jpg)

- - - -

#### Cart List - 

                 ![cart](https://user-images.githubusercontent.com/37416018/142223662-9bf8ce27-e4d6-43da-950d-a592b02ea89e.jpg)

- - - -




### Requirements

*   Minimum Android Studio 3.2 and Recomended Artic Fox | 2020.3.1

*   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)