How to Build Landmark Classifier in Flutter Step by Step 2021

Learn How to Build Landmark Classifier in Flutter Step by Step 2021

00:00 - Intro
00:44 - Project Setting
01:01 - AndroidManifest Setting
01:08 - Podfile Setting (TFLite Setting)
01:21 - Info.plist Setting
01:30 - TFLite Model & Label Setting
02:12 - Home Page
10:28 - ImageService
11:30 - ClassificationService
18:45 - Classification Page

* Github: https://github.com/PuzzleLeaf/flutter_tflite_landmark_classifier

#flutter  #tflite  #classification 

How to Build Populer Wine Classifier App Using Flutter

Learn How to Build Populer Wine Classifier App Using Flutter

▶SourceCode
* Github
- https://github.com/PuzzleLeaf/flutter_popular_wine_classifier

▶Timestamp
00:00 - Intro
00:39 - TFHub Model
00:53 - Main Page
03:29 - Classifier Page (Camera Setting)
07:06 - TFLite Model
13:09 - Test

#flutter  #tflite 

How to Build to Populer US Products Classifier Using Flutter

Learn How to Build to Populer US Products Classifier Using Flutter

This model is trained to recognize more than 100,000 popular supermarket products in the United States from images. The model is mobile-friendly and can run on-device.

▶SourceCode
* Github
- https://github.com/PuzzleLeaf/flutter_tensorflow_lite_us_products_classifier

▶Timestamp
00:00 - Intro
00:21 - MainPage
01:05 - Camera Setting
03:22 - Modal Bottom Sheet
05:08 - TFLite Model Setting
15:08 - Test

#flutter  #tflite  #classification #lite 

TensorFlow Lite Text Classification models with Model Maker

Generate TF Lite models from custom data using Model Maker

In this article, let’s look at how you can use TensorFlow Model Maker to create a custom text classification model. Currently, the TF Lite model maker supports image classification, question answering, and text classification models. It uses transfer learning for shortening the amount of time required to build TF Lite models.

#text-classification #tflite #model-makers #heartbeat

Weight Pruning with Keras

In this blog, we will be understanding the concept of weight pruning with Keras. Basically, weight pruning is a model optimization technique. In weight pruning, it gradually zeroes out model weight during the training process to achieve model sparsity.

This technique brings improvements via model compression. This technique is widely used to decrease the latency of the model.

I will be implementing weight pruning in the Fashion MNIST dataset where I have made a comparison between the normal way and the pruning method.

#machine-learning #deep-learning #keras #tensorflow #tflite

Machine Learning in Android using TensorFlow Lite

Once your TensorFlow model is ready, you can easily deploy it to a mobile application. This is done by converting it to the TF Lite format. If you are working on a common task such as image classification and object detection, you can easily grab a pre-trained model from TensorFlow Hub . In this piece, we’ll use a pre-trained model to illustrate how one can deploy their model on an Android device.

#tflite #tensorflow #mobile-app-development #android #heartbeat

TensorFlow Lite Image Classification models with Model Maker

TensorFlow is one of the greatest gifts to the machine learning community by Google. An end-to-end open-source framework for machine learning with a comprehensive ecosystem of tools, libraries and community resources, TensorFlow  lets researchers push the state-of-the-art in ML and developers can easily build and deploy ML-powered applications. Ever since its release to the public back in November 2015, TensorFlow has grown to become one of the most popular deep learning frameworks. This month, TensorFlow  turned five, and in this article, we take a look at its popular libraries.

#model-makers #tensorflow #heartbeat #image-classification #tflite

TFLite Object Detection Android App Tutorial | TensorFlow Object Detection

In this video, we will show you how to Detect Tensorflow Objects in Android Apps using TFLite.

Clone this repository: https://github.com/tensorflow/examples

Subscribe : https://www.youtube.com/channel/UCgyQ4pSntDf9hw9Rv4hmNBA

#tensorflow #tflite #deep-learning

My Journey in Converting PyTorch to TensorFlow Lite

Intro

I recently had to convert a deep learning model (a MobileNetV2 variant) from PyTorch to TensorFlow Lite. It was a long, complicated journey, involved jumping through a lot of hoops to make it work. I found myself collecting pieces of information from Stackoverflow posts and GitHub issues. My goal is to share my experience in an attempt to help someone else who is lost like I was.

DISCLAIMER: This is not a guide_ on how to properly do this conversion. I only wish to share my experience. I might have done it wrong (especially because I have no experience with Tensorflow). If you notice something that I could have done better/differently — please comment and I’ll update the post accordingly._

The Mission

Convert a deep learning model (a MobileNetV2 variant) from Pytorch to TensorFlow Lite. The conversion process should be:

Pytorch →ONNX → Tensorflow → TFLite

Tests

In order to test the converted models, a set of roughly 1,000 input tensors was generated, and the PyTorch model’s output was calculated for each. That set was later used to test each of the converted models, by comparing their yielded outputs against the original outputs, via a mean error metric, over the entire set. The mean error reflects how different are the converted model outputs compared to the original PyTorch model outputs, over the same input.

I decided to treat a model with a mean error smaller than 1e-6 as a successfully converted model.

It might also be important to note that I added the batch dimension in the tensor, even though it was 1. I had no reason doing so other than a hunch that comes from my previous experience converting PyTorch to DLC models.

#mlops #tensorflow #onnx #pytorch #tflite

Custom Calculators in MediaPipe

This is the Part 2 of the MediaPipe Series I am writing.

Previously, we saw how to get started with MediaPipe and use it with your own tflite model. If you haven’t read it yet, check it out here.

We had tried using the portrait segmentation tflite model in the existing segmentation pipeline with the calculators already present in MediaPipe

Portrait Segmentation MediaPipe — PART 1

After getting bored with this Blue Background, I decided to have some fun with it by having some Zoom like Virtual Backgrounds like beautiful stars or some crazy clouds instead :)

Virtual Clouds Background — PART 2

For this, I wrote a custom calculator and used it with the existing pipeline.

So today I’ll show how did I go about making this App

Before we get started, I would suggest you to go through this part of the documentation which explains the Flow of a basic calculator.

https://google.github.io/mediapipe/framework_concepts/calculators.html

Now, let’s get started with the code

1. Clone the Portrait Segmentation repository from Part1

$ git clone https://github.com/SwatiModi/portrait-segmentation-mediapipe.git

2. New Flow of the Pipeline (making changes in the graph .pbtxt file)

So earlier, the rendering/coloring was done by the _RecolorCalculator , _it used to take image and mask gpu buffer as input and returned gpu buffer rendered output (rendered using opengl)

Here, for replacing the Background with an Image(jpg/png), I have used OpenCV operations.

NOTE_ : OpenCV operations are performed on CPU — ImageFrame datatype where as opengl operations are performed on GPU — Image-buffer datatype_

portrait_segmentation.pbtxt

We will replace the RecolorCalculator with the BackgroundMaskingCalculator

#deep-learning #machine-learning #image-segmentation #augmented-reality #tflite #deep learning

Tensorflow Lite (TFLite) with Golang (Go)

Tensorflow Lite commonly known as TFLite is used to generate and infer machine learning models on mobile and IoT(Edge) devices. TFLite made the on-device(offline) inference easier for multiple device architectures, such as Android, iOS, Raspberry pi and even backend servers. With TFLite you can build a lightweight server based inference application using any programming language with lightweight models, rather than using heavy Tensorflow models.

As developers, we can simply use existing optimized research models or convert existing Tensorflow models to TFLite. There are multiple ways of using TFLite in your mobile, IoT or server applications.

• Implement the inference for different architecture(Android, iOS etc…) using the standard libraries, SDKs provided by TFLite.
• Use the TFLite C API for inference along with platform independent programming language like Golang. And cross-compile for platforms like Android, iOS etc…

In this post I’m going to show case the implementation of TFLite inference application using platform independent language Golang and **cross-compiling **to a shared library. Which then can be consumed by Android, iOS etc…

First thanks to mattn who created the TFLite Go bindings and you can find the repo here. We will start the implementation of a simple Golang application for TFLite inference(You can find the example here). Here I’m using a simple text classifier which will classify to ‘Positive’ or ‘Negative’.

Here is the classifier.go, which has Go functions and are exported for use by C code.

#tensorflow #tflite #ios #golang #go