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)