(Urdu/Hindi ) Learn how to make an ML classifier without programming or installing anything.

By Taha Anwar

On January 25, 2020

Teachable Machine Version 1 (Google AI Experiments) 

In this video lesson I’ll teach you how to create an Image Classifier without actually coding. For this I’m using Teachable Machine version 1 which is part of Google AI Experiments. This application allows you to create image classifiers and introduces computer vision to new comers in a really fun and exciting way. I also go in the technical working of this application so people who already have some fundamental knowledge about about building classifiers can benefit from this.

Here’s the link to access this amazing tool. This is version 1, version 2 of this has also been released which deals with pose and voice recognition and even lets you export the models.

I hope you found this tutorial useful. For future Tutorials by us, make sure to Subscribe to Bleed AI below

I’m offering a premium 3-month Comprehensive State of the Art course in Computer Vision & Image Processing with Python (Urdu/Hindi). This course is a must take if you’re planning to start a career in Computer vision & Artificial Intelligence, the only prerequisite to this course is some programming experience in any language.

This course goes into the foundations of Image processing and Computer Vision, you learn from the ground up what the image is and how to manipulate it at the lowest level and then you gradually built up from there in the course, you learn other foundational techniques with their theories and how to use them effectively.

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Training a Object Detector with Tensorflow Object Detection API

Training a Object Detector with Tensorflow Object Detection API

This is a really descriptive and interesting tutorial, let me highlight what you will learn in this tutorial.

A Crystal Clear step by step tutorial on training a custom object detector.
A method to download videos and create a custom dataset out of that.
How to use the custom trained network inside the OpenCV DNN module so you can get rid of the TensorFlow framework.
Plus here are two things you will receive from the provided source code:

A Jupyter Notebook that automatically downloads and installs all the required things for you so you don’t have to step outside of that notebook.
A Colab version of the notebook that runs out of the box, just run the cells and train your own network.
I will stress this again that all of the steps are explained in a neat and digestible way. I’ve you ever plan to do Object Detection then this is one tutorial you don’t want to miss.

As mentioned, by downloading the Source Code you will get 2 versions of the notebook: a local version and a colab version.

So first we’re going to see a complete end to end pipeline for training a custom object detector on our data and then we will use it in the OpenCV DNN module so we can get rid of the heavy Tensorflow framework for deployment. We have already discussed the advantages of using the final trained model in OpenCV instead of Tensorflow in my previous post.

Today’s post is the 3rd tutorial in our 3 part Deep Learning with OpenCV series. All three posts are titled as:

Deep Learning with OpenCV DNN Module, A Comprehensive Guide
Training a Custom Image Classifier with OpenCV, Converting to ONNX, and using it in OpenCV DNN module.
Training a Custom Object Detector with Tensorflow and using it with OpenCV DNN (This Post)


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