(Urdu/Hindi) From Training to Deploying Classification, Pose Estimation & Sound Recognition models without coding.

By Taha Anwar

On April 4, 2020

In less than 10 minutes I teach you how to train an effective hand finger recognition classifier, a pose detection model & a sound recognition model and also show you multiple deployment options.

If you’re not impressed yet than let me tell you this: “You won’t need any programming knowledge or need to install anything to work with this, an internet connection with a browser is sufficient.”

Unless you’re using Internet Explorer 😐

So the tool we are using is Teachable Machine version 2.  A few months ago I made a video on Teachable machine version 1 but version 1 was more of a teaching tool. This version actually is a lot more powerful and allows you to export models in various ways.

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)

10 Comments

  1. Hayat Ullah

    Much appreciated your work..
    thanks you sir.. God bless you..
    Plz keep on sharing these valuable information regarding CV and AI..

    Reply
    • Taha Anwar

      Thank you Hayat for the comments.

      Reply
  2. Muhammad Aqib

    Thank you sir.Allah ap ko khosh rake.

    Reply
    • Taha Anwar

      Thank you Aqib

      Reply
  3. Asif

    I really appreciate your work. Indeed you are doing a tremendous job. Quality Content

    Reply
    • Taha Anwar

      Much Appreciated, thank you.

      Reply
  4. Usama Habib

    Keep up the good work sir much appreciated

    Reply
    • Taha Anwar

      Thank you Usama.

      Reply
  5. Rao Tariq

    Commendable performance. Thanks a lot

    Reply
    • Taha Anwar

      You’re Welcome 🙂

      Reply

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