Image Classification Tutorial: Train and Detect Objects with TensorFlow and Pixellib

Welcome to our comprehensive tutorial on image annotation, training, and object detection using TensorFlow, Pixellib, and ResNet101! In this video, we'll guide you through the process of annotating objects within images, creating JSON files to label each image accurately, and training a custom model with the powerful ResNet101 architecture.

First, we'll demonstrate how to annotate objects by placing dots along the image, effectively defining their boundaries. We'll then generate a JSON file for each image, which serves as a label containing the coordinates of the annotated dots. This step is crucial for training our custom model accurately.

Next, we'll introduce ResNet101, a pre-trained deep learning model renowned for its exceptional performance in image classification tasks. Leveraging transfer learning, we'll train our custom images and corresponding labels using ResNet101. This process allows us to harness the capabilities of a well-established model while adapting it to our specific object detection requirements.

Code for this video: https://github.com/feitgemel/Object-Detection/tree/main/Pixellib

More relevant content in this playlist : https://www.youtube.com/playlist?list=PLdkryDe59y4YSWxIesJtiNqpayN0QE4_h

A perfect course for learning modern Computer Vision with deep dive in TensorFlow , Keras and Pytorch . You can find it here : http://bit.ly/3HeDy1V 

I also recommend this book, https://amzn.to/44GnlLW : "Make Your Own Neural Network - An In-depth Visual Introduction For Beginners ".

I am using one of these desktop cameras in my projects :
Logitech C270 , Simple and basic camera : https://amzn.to/37CAPjO
Logitech HD Pro Webcam C920 , A very good camera : https://amzn.to/3rM1Lof

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