Simple Object Detection in Python using TensorFlow | Bounding Box Regression | OpenCV | #1
In this video, we will be building a basic object detector from scratch using TensorFlow. Designed for beginners, we will be focusing on detecting only one object per image. Not only that, we will also be learning about the Intersection over Union (IoU) metric and using it as an evaluation metric for our object detector. We will start by explaining the basic concepts of object detection and then begin writing the code.
By the end of this video, you will have a good understanding of the basics of object detection and how to use IoU as an evaluation metric. So, whether you're a beginner or just new to object detection, this tutorial is for you. Let's get started!
Code: https://github.com/nikhilroxtomar/Obj...
Dataset: https://www.kaggle.com/datasets/nikhi...
Timeline:
00:00 - Introduction
00:58 - What is Object Detection?
02:04 - Objective of this video
02:37 - Dataset
04:24 - Theory on Image and Bounding Box Normalization
07:16 - Architecture (MobileNetv2)
08:49 - Implementing the object detection mode using the pre-trained MobileNetv2
18:39 - Begining the Training code
18:53 - Importing all the required libraries and functions
20:07 - Global parameters (height and width)
20:19 - Function: To create an empty directory
21:14 - Seeding the environment
21:45 - Creating a directory (files)
21:56 - Defining the Hyperparameters
23:20 - Function: To load and split the dataset into training, validation and testing
31:48 - Building dataset pipeline using tf.data API
44:10 - Buiding the model
46:17 - Training the model
46:54 - Begining the Testing code
47:04 - Importing all the required libraries and functions
47:16 - Global parameters (height and width)
47:30 - Seeding the environment
47:37 - Creating a directory (results)
47:47 - Load the trained model
48:43 - Load the dataset
49:15 - Prediction and Evaluation
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